contextflow winner of the Healthy Hub competition from four health insurance companies: Innovative solutions for women’s health and early detection

DORTMUND, 13.03.2024. The winners of the 2024 Healthy Hub Competition want to improve women’s health with innovative care. The various offers relate to different phases of life and clinical pictures – from hybrid tests against HPV viruses, to care for typical female diseases such as endometriosis, to innovative therapy for menopausal symptoms. People with lung diseases should receive better care in terms of early detection. The winning teams will each tackle these topics with one of the four health insurance companies – BIG direkt gesund, IKK Südwest, mhplus Krankenkasse and SBK Siemens-Betriebskrankenkasse. This is the fifth time that the health insurance companies have jointly organized the competition.

“This time, the focus was on selected contractual care solutions, which take into account the gender health gap, i.e. the inequality of care between women and men,” explains Dr. Elmar Waldschmitt, Managing Director of the Healthy Hub and Board Representative at BIG.  Unfortunately, medical research and care is still too heavily focused on men.

The winners were selected from over 40 applications:

Remi Health, health insurance partner BIG direkt gesund

Remi Health has developed a cervical cancer screening program that allows insured women to test themselves for human papillomavirus (HPV) at home. If the results are positive, an online consultation and an appointment with a gynecologist are provided. Remi’s digital platform also provides information about the importance of screening and early detection. “The low-threshold HPV test combines home testing, telemedicine and prevention in one seamless process,” explains Marvin Abert, Co-CEO of Remi Health. “In particular, we want to reach young women who have not yet taken advantage of cancer screening and have no immunity through an HPV vaccination. In our opinion, the HPV self-test from Remi Health is an innovative approach to complementing cancer screening in a meaningful way,” says Christiane Heidrich, Team Leader Managed Care at BIG direkt gesund.

Femna Health, health insurance partner SBK Siemens-Betriebskrankenkasse

Around a third of women of fertile age, i.e. in their fertile years, suffer from severely restrictive physical, psychological and social stress due to cycle complaints. These include conditions such as PMS, endometriosis and dysmenorrhea. “Current care does not adequately address these problems due to a lack of treatment options, long waiting times and a lack of individualized care. FEMNA has therefore developed a hybrid care model that provides women with improved, comprehensive and immediate care,” explains Maxie Matthiessen, founder of Femna Health. “Femna Care improves behavioral patterns and helps to prevent secondary diseases and increase the quality of life of affected women,” says Christina Bernards, Team Leader Care Management, SBK Siemens-Betriebskrankenkasse. “This is an issue close to our hearts, because women’s health in particular is massively neglected and underfunded in medicine and research. It’s time we changed that.”

YoniCare, health insurance partner mhplus Krankenkasse

With YoniCare (MICADO HEALTH CARE GmbH), the Healthy Hub has a non-digital care product in its portfolio for the first time. YoniCare enables laser therapy for the treatment of genitourinary syndrome. “The drop in estrogen levels during the menopause leads to vaginal dryness, changes in vaginal tissue and the vaginal mucosa and, as a result, symptoms such as itching, incontinence, infections and other complaints,” says Mandy Wilms from MICADO HEALTH CARE GmbH. The therapy can revitalize the vaginal epithelium that lines the vagina and the vaginal vestibule. The symptoms decrease and gynecological health is restored. “The therapy can make hormone replacement therapy superfluous and reduce interactions with medication,” says Fabienne Knaub, consultant for selective contracts and care analysis at mhplus Krankenkasse. 

contextflow, health insurance partner IKK Südwest

Early detection is essential for successful treatment of thoracic diseases. “We offer AI software that provides additional information for identifying and interpreting lung-specific image patterns in CT scans,” says Markus Holzer, CEO and co-founder of contextflow. The ultimate goal of the software is to detect lung cancer as early as possible in order to save healthcare system resources and protect patients from unnecessary interventions. “The image analysis AI enables radiologists to assess relevant image patterns of lung cancer and respiratory diseases faster and better,” says Dr. Florian Brandt, Health Innovation Manager at IKK Südwest. 

Developing use cases for healthcare practice

Over the next few months, the four health insurance companies will work with the winning teams to develop specific use cases that are suitable for use in statutory health insurance (SHI). “Our aim is to ensure that these innovative care methods are used in practice,” says Dr. Elmar Waldschmitt. In addition, the Healthyhub’s cooperation partner, GWQ ServicePlus AG, will be involved in the development from the outset. 

About the Healthy Hub

Since 2018, the four health insurance companies BIG direkt gesund, IKK Südwest, mhplus Krankenkasse and SBK Siemens-Betriebskrankenkasse have been committed to the digitalization of the healthcare system with the Healthy Hub. The aim is to enable innovative solutions for better, integrated care. To this end, the health insurance funds regularly organize competitions for start-ups. They have already brought 27 start-ups into the healthcare sector. 

About BIG direkt gesund 

BundesInnungskrankenkasse Gesundheit – BIG direkt gesund for short – was founded in Dortmund in 1996. The big idea behind BIG: We create space for being human. This means a modern online approach and empathetic service that is fast and solution-oriented. BIG offers versatile communication channels to suit the respective lifestyles of its around 510,000 policyholders across Germany. These customers receive appreciative and understandable advice by phone, chat, email or letter. Many additional benefits and an attractive bonus program are further major plus points. BIG direkt gesund has its legal domicile in Berlin, its head office in Dortmund and an important administrative location in Aachen. BIG employs around 950 staff at its operating locations and offers on-site advice in 11 BIGshops.

For inquiries

Bettina Kiwitt

Head of Corporate Communications

Phone: 0231/5557-1016


About IKK Südwest:

IKK Südwest currently serves more than 635,000 insured persons and over 90,000 companies in Hesse, Rhineland-Palatinate and Saarland. Insured and interested parties can rely on personal support in our 21 customer centers in the region. In addition, IKK Südwest can be contacted around the clock seven days a week via the IKK service hotline 0681/ 3876 1000 or at

For inquiries

Mathias Gessner

Press spokesman

Phone: 0681/3876-1163


About the mhplus health insurance company:

mhplus Krankenkasse is open to those with statutory health insurance. Around 1,000 employees look after more than half a million policyholders throughout Germany. Independent tests confirm high service standards and solid finances. The mhplus supplementary contribution has remained stable at 1.58% since 2023.

For inquiries

Isabell Rabe

Head of Press and Politics

Phone: 07141/9790-9845


About the SBK: 

SBK Siemens-Betriebskrankenkasse is the largest company health insurance fund in Germany and is one of the 20 largest statutory health insurance funds. As an open, nationwide health insurance company, it insures more than one million people and looks after over 100,000 corporate customers in Germany – with around 2000 employees in 86 branches. For more than 100 years, SBK has been personally committed to the interests of its policyholders. It positions itself as a pioneer for genuine quality competition in statutory health insurance. From the SBK’s point of view, the prerequisite for this is more transparency for the insured – about relevant key financial figures, but also about the willingness to perform, advice and service quality of health insurance companies. In the interests of the customer, SBK also combines the best of the personal and digital worlds and is actively driving forward digitalization in the healthcare sector. 

For inquiries 

Julia Mederle

Corporate Communications Department 

Phone: +49 89 62700-363 

E-mail: Internet:

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contextflow targets early lung cancer detection by augmenting ADVANCE Chest CT with malignancy scoring from RevealDx

Vienna, Austria (02.05.2024) – Chest CT experts contextflow GmbH have released a new version of their comprehensive computer-aided detection support tool, ADVANCE Chest CT. In addition to lung nodule detection, quantification, visualization and classification, the updated software now analyzes nodules for malignancy with the aim of detecting cancer early and reducing unnecessary procedures.

It’s widely known that lung cancer constitutes one of the leading causes of premature death, and thus early detection of cancer is crucial. For that very reason, contextflow has implemented a malignancy Similarity Index (mSI) feature into ADVANCE Chest CT, a clinical decision support tool that aids radiologists in the diagnosis of lung cancer, interstitial lung diseases (ILD) and chronic-obstructive pulmonary disease (COPD). 

A malignancy Similarity Index is a value from 0 to 1 that indicates the degree of similarity between a nodule in question and nodules with known outcomes in a reference set. In clinical practice, a high mSI would indicate “upgrading” followup of a nodule as compared to guideline recommendations because there is increased certainty that the nodule in question is malignant. Here, the goal is to detect cancer as early as possible in order to improve patient outcomes.

As contextflow CEO Markus Holzer puts it, “Detecting lung cancer is a challenging and time-consuming task for radiologists. Detection is not straightforward. All too often patients are scheduled for followups months after their initial exam without knowing whether they actually have cancer or not. This is extremely stressful, but thankfully we can start to change that.”

On the flip side, a low mSI score would indicate “downgrading” the nodule with the aim of reducing invasive, unnecessary procedures and patient stress. In a clinical study published in the Journal of the American College of Radiology, use of the RevealDx mSI feature was shown to detect cancer up to one year sooner in approximately 45% of cases while simultaneously reducing false positive detection rates by 18% (Adams, Scott J. et al. JACR September 2022).

As Chris Wood, CEO of RevealDx says, “Our integration with ADVANCE Chest CT simplifies the interpretation of lung nodules. Automatically detected nodules have their mSI scores calculated before the radiologist starts reading the exam, which should save time while providing clinical insight.”

The latest version of contextflow ADVANCE Chest CT also includes a nodule tracking report to visualize and quantify changes in nodules over time. contextflow ADVANCE Chest CT is a CE marked medical device under MDR. For more information, contact or visit

About contextflow 

contextflow is a spin-off of the Medical University of Vienna (MUW) and European research project KHRESMOI, supported by the Technical University of Vienna (TU). Founded by a team of AI and engineering experts in July 2016, the company has a strong interest in bringing state-of-the-art machine learning techniques to the market e.g. improved emphysema detection and lung segmentation. Its computer-aided detection software ADVANCE Chest CT is CE Marked and available for clinical use within Europe under the new MDR. Visit for more information. 

About RevealDx

RevealDx developed RevealAI-Lung, the world’s first CADx software for the characterization of lung nodules to receive the CE Mark. RevealAI-Lung has been validated in clinical studies that show improvement in diagnostic precision using our patented methods.  Results demonstrate the software can significantly accelerate lung cancer diagnosis and reduce unnecessary procedures.

For more information, contact: 

Julie Sufana, Chief Marketing Officer, contextflow


Phone: +43 676 920 1032 

Chris Wood, CEO, RevealDx


Phone: +01 425 895 2845

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contextflow gewinnt den Wettbewerb Healthy Hub von vier Krankenkassen: Innovative Lösungen für Frauengesundheit und Früherkennung

DORTMUND, 13.03.2024. Die Gewinnerinnen und Gewinner des Healthy-Hub-Wettbewerbs 2024 wollen die Gesundheit von Frauen mit innovativer Versorgung verbessern. Die verschiedenen Angebote beziehen sich auf unterschiedliche Lebensphasen und Krankheitsbilder – von hybriden Tests gegen HPV-Viren, über die Versorgung bei frauentypischen Krankheiten wie Endometriose, bis hin zu einer innovativen Therapie bei Beschwerden in der Menopause. Menschen mit Lungenerkrankungen sollen in Sachen Früherkennung besser betreut werden. Diese Themen gehen die Siegerteams jeweils mit einer der vier Krankenkassen – BIG direkt gesund, IKK Südwest, mhplus Krankenkasse und SBK Siemens-Betriebskrankenkasse – an. Die Kassen haben den Wettbewerb bereits zum fünften Mal gemeinsam ausgerichtet.

„Im Fokus standen dieses Mal selektivvertragliche Versorgungslösungen, die insbesondere das Gender Health Gap, also die Ungleichheit der Versorgung von Frauen gegenüber Männern, berücksichtigen sollten“, erläutert Dr. Elmar Waldschmitt, Geschäftsführer des Healthy Hub und Vorstandsbeauftragter bei der BIG.  Die medizinische Forschung und Versorgung sei leider immer noch zu stark auf Männer fokussiert.

Aus gut 40 Bewerbungen wurden die Gewinnerinnen und Gewinner ausgewählt. Diese sind:

Remi Health, Kassenpartnerin BIG direkt gesund

Remi Health hat eine Früherkennung gegen Gebärmutterhalskrebs entwickelt, bei der sich weibliche Versicherte zuhause auf Humane Papillomviren (HPV) testen können. Bei auffälligem Befund sind eine Online-Beratung und die Terminvereinbarung bei einer Gynäkologin oder einem Gynäkologen vorgesehen. Die digitale Plattform von Remi klärt zudem über die Bedeutung der Vorsorge und Früherkennung auf. „Der niedrigschwellige HPV-Test vereint Heimtests, Telemedizin und Prävention in einem nahtlosen Prozess“, erläutert Marvin Abert, Co-CEO von Remi Health. „Damit möchten wir insbesondere junge Frauen erreichen, die bislang die Krebsvorsorgeuntersuchungen nicht in Anspruch nehmen und keine Immunität durch eine HPV-Impfung besitzen. Der HPV-Selbsttest von Remi Health ist unserer Ansicht nach ein innovativer Lösungsansatz, um die Krebsvorsorge sinnvoll zu ergänzen“, sagt Christiane Heidrich, Teamleiterin Managed Care der BIG direkt gesund.

Femna Health, Kassenpartnerin SBK Siemens-Betriebskrankenkasse

Rund ein Drittel der Frauen im fertilen Alter, also in den fruchtbaren Jahren, leidet unter stark einschränkenden physischen, psychischen und sozialen Belastungen aufgrund von Zyklusbeschwerden. Darunter fallen Erkrankungen wie PMS, Endometriose oder auch Dysmenorrhö. „Die aktuelle Versorgung geht unter anderem aufgrund von mangelnden Therapiemöglichkeiten, langen Wartezeiten und fehlender individueller Betreuung diese Probleme nicht adäquat an. FEMNA hat deshalb ein hybrides Versorgungsmodell entwickelt, das Frauen eine verbesserte, vollumfängliche und sofortige Versorgung ermöglicht“, erklärt Maxie Matthiessen, Gründerin von Femna Health. „Femna Care verbessert Verhaltensmuster und hilft, Folgeerkrankungen zu vermeiden sowie die Lebensqualität betroffener Frauen zu steigern“, sagt Christina Bernards, Teamleiterin Versorgungsmanagement, SBK Siemens-Betriebskrankenkasse. „Für uns ein echtes Herzensthema, denn gerade die Frauengesundheit wird in Medizin und Forschung massiv vernachlässigt und unterfinanziert. Es wird Zeit, dass wir das ändern.“

YoniCare, Kassenpartnerin mhplus Krankenkasse

Mit YoniCare (MICADO HEALTH CARE GmbH) hat der Healthy Hub erstmals ein nicht-digitales Versorgungsprodukt im Portfolio. YoniCare ermöglicht eine Lasertherapie zur Behandlung des genitourinären Syndroms. „Der sinkende Östrogenspiegel in der Menopause führt bei Frauen unter anderem zu Scheidentrockenheit, Veränderungen im Vaginalgewebe sowie der Vaginalschleimhaut und in der Folge zu Beschwerden wie Jucken, Inkontinenz, Infekten und anderen Beschwerden“, so Mandy Wilms von der MICADO HEALTH CARE GmbH. Die Therapie kann das vaginale Epithel revitalisieren, das die Vagina und den Scheidenvorhof auskleidet. Die Beschwerden nehmen ab und die gynäkologische Gesundheit wird wiederhergestellt. „Die Therapie kann eine Hormonersatztherapie überflüssig machen und Wechselwirkungen mit Medikamenten reduzieren“, sagt Fabienne Knaub, Referentin Selektivverträge und Versorgungsanalyse der mhplus Krankenkasse. 

contextflow, Kassenpartnerin IKK Südwest

Bei Erkrankungen im Thoraxbereich ist eine Früherkennung wesentlich für den Therapieerfolg. „Wir bieten eine KI-Software, die ergänzende Informationen zur Identifizierung und Interpretation von lungenspezifischen Bildmustern in Scans vom Computertomographen bietet“, so Markus Holzer, CEO und Co-Founder von contextflow. Das ultimative Ziel der Software ist es, Lungenkrebs so früh wie möglich zu erkennen, um Ressourcen des Gesundheitssystems zu sparen und Patientinnen und Patienten vor unnötigen Eingriffen zu schützen. „Die Bildanalyse-KI ermöglicht den befundenden Radiologen eine schnellere und bessere Beurteilung relevanter Bildmuster von Lungenkrebs und respiratorischen Erkrankungen“, sagt Dr. Florian Brandt, Health Innovation Manager der IKK Südwest. 

Anwendungsfälle für die Versorgungspraxis entwickeln

Die vier Krankenkassen entwickeln in den nächsten Monaten gemeinsam den Gewinnerteams konkrete Anwendungsfälle, die für den Einsatz in der Gesetzlichen Krankenversicherung (GKV) geeignet sind. „Unser Ziel ist es, dass diese innovativen Versorgungsmethoden in der Versorgungspraxis ankommen“, so Dr. Elmar Waldschmitt. Zudem wird der Kooperationspartner des Healthyhub, die GWQ ServicePlus AG, in die Entwicklung von Anfang an eingebunden. 

Über den Healthy Hub

Seit 2018 engagieren sich die vier Krankenkassen BIG direkt gesund, IKK Südwest, mhplus Krankenkasse sowie SBK Siemens-Betriebskrankenkasse mit dem Healthy Hub für die Digitalisierung des Gesundheitswesens. Ziel ist es, innovative Lösungen für eine bessere, integrierte Versorgung zu ermöglichen. Dazu organisieren die Kassen regelmäßig Wettbewerbe für Start-ups. So haben sie bereits 27 Start-ups in die Versorgung gebracht. 

Über BIG direkt gesund 

Die BundesInnungskrankenkasse Gesundheit – kurz BIG direkt gesund – wurde 1996 in Dortmund gegründet. Die große Idee hinter der BIG: Wir schaffen Raum fürs Menschsein. Gemeint ist damit eine moderne Online-Ausrichtung und empathischer Service, der schnell und lösungsorientiert ist. Die BIG bietet vielseitige Kommunikationswege passend zum jeweiligen Lebensstil ihrer bundesweit rund 510.000 Versicherten. Diese Kundinnen und Kunden erfahren eine wertschätzende und verständliche Beratung per Telefon, Chat, Mail oder Brief. Viele Zusatzleistungen sowie ein attraktives Bonusprogramm sind weitere große Pluspunkte. BIG direkt gesund hat ihren Rechtssitz in Berlin, ihre Hauptverwaltung in Dortmund und einen wichtigen Verwaltungsstandort in Aachen. Die BIG beschäftigt an den operativen Standorten rund 950 Mitarbeiterinnen und Mitarbeiter, in 11 BIGshops wird Beratung vor Ort angeboten.

Für Rückfragen

Bettina Kiwitt

Leiterin Unternehmenskommunikation

Tel.: 0231/5557-1016


Über die IKK Südwest:

Aktuell betreut die IKK Südwest mehr als 635.000 Versicherte und über 90.000 Betriebe in Hessen, Rheinland-Pfalz und im Saarland. Versicherte und Interessenten können auf eine persönliche Betreuung in unseren 21 Kundencentern in der Region vertrauen. Darüber hinaus ist die IKK Südwest an sieben Tagen in der Woche rund um die Uhr über die IKK Service-Hotline 0681/ 3876 1000 oder zu erreichen.

Für Rückfragen

Mathias Gessner


Tel.: 0681/3876-1163


Über die mhplus Krankenkasse:

Die mhplus Krankenkasse ist offen für gesetzlich Krankenversicherte. Rund 1.000 Mitarbeitende betreuen deutschlandweit mehr als eine halbe Million Versicherte. Unabhängige Tests belegen hohe Servicestandards und solide Finanzen. Der Zusatzbeitrag der mhplus beträgt seit 2023 stabil 1,58 Prozent.

Für Rückfragen

Isabell Rabe

Leiterin Presse und Politik
Tel.: 07141/9790–9845


Über die SBK: 

Die SBK Siemens-Betriebskrankenkasse ist die größte Betriebskrankenkasse Deutschlands und gehört zu den 20 größten gesetzlichen Krankenkassen. Als geöffnete, bundesweit tätige Krankenkasse versichert sie mehr als eine Million Menschen und betreut über 100.000 Firmenkunden in Deutschland – mit rund 2000 Mitarbeiterinnen und Mitarbeitern in 86 Geschäftsstellen. Seit über 100 Jahren setzt sich die SBK persönlich und engagiert für die Interessen der Versicherten ein. Sie positioniert sich als Vorreiter für einen echten Qualitätswettbewerb in der gesetzlichen Krankenversicherung. Voraussetzung dafür ist aus Sicht der SBK mehr Transparenz für die Versicherten – über relevante Finanzkennzahlen, aber auch über Leistungsbereitschaft, Beratung und Dienstleistungsqualität von Krankenkassen. Im Sinne des Kunden vereint die SBK darüber hinaus das Beste aus persönlicher und digitaler Welt und treibt die Digitalisierung im Gesundheitswesen aktiv voran. 

Für Rückfragen 

Julia Mederle

Stab Unternehmenskommunikation 

Tel.: +49 89 62700-363 

E-Mail: Internet:

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contextflow included in Spanish study on rheumatoid arthritis and ILD diagnosis

The Spanish Society of Rheumatology is currently conducting a research study to explore the prevalence and early diagnosis of interstitial lung disease (ILD) in individuals diagnosed with rheumatoid arthritis (RA). Fifteen rheumatology departments will work on the study together with the radiology departments in Spain in 2024 and 2025, and hereby make use of radiology AI in rheumatology.

This collaboration will address an important healthcare issue: the early detection of ILDs, which tend to go undetected until later stages. This, in turn, negatively impacts patient outcomes, particularly when a patient suffers from cardiovascular diseases. 

Rheumatoid arthritis is a systemic autoimmune disease that predominately involves joints, but it can develop into inflammation in lungs, heart, eyes. The expectation is that approximately 30% of rheumatoid arthritis patients may develop diffuse ILDs, underscoring the need for effective screening criteria. Early diagnosis is hard due to the lack of standard. To achieve this goal, the research team has outlined specific criteria for participant selection, and the study aims to recruit over 450 patients across 15 hospital centers. The study results will hopefully help define strategies for early detection of ILD in patients with rheumatoid arthritis.

contextflow ADVANCE Chest CT as the AI solution will deliver automatic quantification regarding the extent of ILD in the study population, enabling detailed examination of the lung parenchyma. Furthermore, the study will compare the interstitial involvement of the lung parenchyma in chest CTs assessed by radiologists and compare them to the results from the automatic detection of ILD-associated patterns by contextflow’s AI software. 

While specific lung patterns under assessment remain unclear, the integration of AI-driven analysis promises to enhance accuracy and efficiency of diagnosis.

Radiology AI solution for better diagnosis

This collaborative effort aims to improve early detection and management of ILDs in rheumatoid arthritis patients, potentially leading to better outcomes and quality of life for affected individuals. It is a great example of how radiology AI solution can be used in rheumatology.

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Improving diagnosis for incidental pulmonary embolism (IPE) in the clinical workflow: the role of AI

In July 2023, Erasmus Medical Center’s radiology department kicked off a four-year project focusing on the use of CT to improve diagnosis and treatment outcomes of incidental pulmonary embolism (IPE). The project was made possible through a grant from the NHI (Netherlands Health Institute) and is a collaboration between contextflow and esteemed partners in the Netherlands:

  • Erasmus Medical Center, Department of Radiology in Rotterdam
  • Erasmus School of Health Policy and Management (ESHPM), Department of Health Technology Assessment in Rotterdam
  • Technical University Delft, Department of Imaging Physics in Delft

The project is evolving under the guidance of the dedicated PhD student Erik Kemper at Erasmus University and will explore ways in which machine learning-based technology can be seamlessly blended into workflows to help radiologists and clinicians catch IPEs earlier and more accurately. 

Besides an obvious benefit to patients and radiologists (and a seeming benefit to hospitals and the healthcare system at large) the use of AI in clinical practice has never really been explored for IPE. Therefore, the project will look into the impact of the technological advancement of PE detection in various settings with the aim of redefining the landscape of radiology and potentially set new benchmarks in efficiency and patient-centered care as they relate to IPE. 

This collaboration shows the power of interdisciplinary cooperation in medical technology innovation. Over the course of the next four years, the involved parties will work on producing results that could be used for setting new standards for radiology and healthcare.

What is incidental pulmonary embolism (IPE) and why use AI to detect it?

Incidental pulmonary embolism is a life-threatening condition that requires prompt diagnosis and treatment. Detecting PE in computed tomography (CT) scans can be challenging, and the project group believes AI can help in improving the accuracy and consistency of IPE detection by analyzing and identifying subtle patterns that may go unnoticed by human observers and by reducing false negatives and false positives. 

The interpretation of medical imaging can be time-consuming, especially in busy healthcare settings. AI algorithms can rapidly analyze images and assist radiologists in the detection of IPE, potentially reducing the time required for diagnosis. 

By providing consistent and reproducible results, AI algorithms can also help reduce inter-observer variability and ensure that the same criteria are applied to every case. This standardization can lead to more reliable diagnoses, enabling better patient management and facilitating research and quality improvement initiatives.

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Blackford and contextflow Announce Commercial Partnership to Bring Comprehensive Chest CT Detection Software to Healthcare Providers

Edinburgh, Scotland (November 16th, 2023) – Blackford, the pioneering strategic AI platform and solutions provider, and contextflow today announced a commercial partnership to bring contextflow’s ADVANCE Chest CT solution to healthcare professionals.

Under the partnership, contextflow’s innovative technology will be integrated with Blackford’s advanced enterprise AI platform. Blackford provides healthcare professionals access to an extensive portfolio of medical AI solutions designed to drive clinical efficiency and improve patient outcomes. By integrating contextflow’s advanced detection technology into the Blackford platform, Blackford can offer healthcare providers a powerful tool for detecting ILD, COPD and lung cancer on chest CTs.

“Blackford exists to improve the lives of patients and populations – we can do this by providing tailored AI solutions to healthcare providers around the world to help enhance therapy selection and treatment optimisation”, said Ben Panter, CEO of Blackford. “We are delighted to partner with contextflow to add their advanced tools for lung disease detection, quantification and monitoring of disease progression to our AI portfolio.” 

contextflow’s CE and UKCA marked technology, ADVANCE Chest CT, offers radiologists comprehensive computer-aided detection support for lung cancer, ILD, and COPD patients. The software detects, visualizes and quantifies nodules and lung disease patterns to enhance the speed and quality of radiology reports. Its upcoming malignancy scoring feature has been shown to not only detect lung cancer sooner but also to reduce both false positives and false negatives (*Adams, Scott J et al., JACR September 2022). 

As contextflow’s Chief Commercial Officer Marcel Wassink puts it, “Successful implementation of lung cancer screening programs will require the use of assistive AI to help with earlier detection and manage the workloads. We also understand that lung cancer is only one of many findings relevant to the patient’s wellbeing, and thus we are proud to offer comprehensive support for chest CT that goes beyond cancer to include ILD, COPD, and in the near future, incidental pulmonary embolism. Our partnership with Blackford will accelerate the adoption of this much-needed AI, increasing its accessibility to radiologists and patients alike.”

About Blackford 

Blackford are pioneers in the radiology AI space, with over a decade of experience working in partnership with leading hospitals and ground-breaking technology providers. We operate as a strategic AI partner, providing access to a tried-and-tested core platform, tailored services, and a portfolio of 100+ applications to help healthcare providers unlock the value of AI and improve patient outcomes. 

Our collaboration and recent arms-length acquisition by Bayer ensures that our customers and partners have the support and long-term security needed to underpin successful AI strategies. 

To learn more about Blackford’s tailored approach to AI solutions visit  

About contextflow 

contextflow is a spin-off of the Medical University of Vienna (MUW) and European research project KHRESMOI, supported by the Technical University of Vienna (TU). Founded by a team of AI and engineering experts in July 2016, the company has a strong interest in bringing state-of-the-art machine learning techniques to the market e.g. improved emphysema detection and lung segmentation. Its computer-aided detection software ADVANCE Chest CT is CE Marked and available for clinical use within Europe under the new MDR. Visit for more information.

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Always in the picture thanks to AI

Evangelisches Klinikum Niederrhein introduces contextflow ADVANCE Chest CT in the Pulmonology Department

Lung diseases are among the most common as well as most diverse health problems worldwide. They require precise diagnoses and effective treatment strategies. In this context, artificial intelligence (AI) opens up a promising avenue for detecting a wide variety of disease conditions in affected patients as early as possible. Fanar Othman, head physician at the Clinic for Pulmonary and Bronchial Medicine at Johanniter Hospital Oberhausen, has also discovered the possibilities of deep learning-based technology for himself. He uses the CE certified software contextflow ADVANCE Chest CT to ensure that even when there is a lot to do, the pulmonologist no longer misses anything.

As part of the Lower Rhine Evangelical Hospital and Bethesda Hospital network, the department not only cares for around 3,000 patients a year in its own hospital, but also provides consultative care for three other sites in the western Ruhr region, including Dinslaken and Duisburg. As a former stronghold of the coal and steel industry, the region is also known as the “coal pot”. Particularly among the older generation, the doctors still see many patients whose health was severely affected by their work in the former mines, blast furnaces and factories. As a result, a not insignificant proportion of the medical care provided locally is for occupational lung diseases such as pneumoconiosis, silicosis, asbestosis and, unfortunately, the resulting late effects such as cancer and pleural mesothelioma. 

So there is a lot to do for the total of five pulmonologists under the direction of Fanar Othman. “We have a large catchment area and, simply because of the size of our association, we get to see a large number of rare pathologies in addition to the usual clinical findings,” says the chief physician. “In these cases, it can already become a challenge to make the correct diagnosis and initiate appropriate therapy.” In this context, a single disease may be associated with multiple radiological patterns. Precise characterization is often laborious and examiner-dependent.

Quick access to relevant knowledge

Since last year, contextflow’s AI solution has been providing a remedy for this problem. It takes over the sifting and provision of information from CT examinations that are relevant for diagnostics. The recognition software detects even small changes in the lung parenchyma and relates them to specific diseases. For nodules, it can even show progression. The resulting findings report is generated automatically and is available directly in the PACS viewer within a few minutes. In this way, the tool saves the diagnostician from having to work through thousands of image slices every day, and at the same time, prevents anything important from being overlooked. 

Using an AI system in his department is new territory for Fanar Othman. He was made aware of ADVANCE Chest CT by his physician colleague, the head of radiology, Prof. Dr. Jörg Michael Neuerburg, who has already been using the software successfully for some time. “From the beginning, I liked how affable and user-friendly the application was,” Othman recalls. “You have to take care of practically nothing. The findings generated by the AI are prepared in a simple and clearly understandable way, so you can adopt the results without time-consuming cross-checking. There is not enough time for anything else in daily practice. Everything has to be done in a jiffy.”

Progress controls at the push of a button

When the opportunity arose to take part in a product training course initiated by contextflow, the pulmonologist jumped at the chance. During the online training, he discovered that the software can do a lot more that is valuable for his work: “For example, the AI is able to compare different series of images taken at different points in time. This makes it possible to assess the size development of nodules, which is very important for determining further therapeutics.”

In addition to calculating the diameter in a plane, the system also applies 3D volumetric analysis to evaluate the total mass of a pulmonary nodule. The volumetric measurement method is becoming increasingly important because it offers a more accurate assessment of malignancy risk, or tumor grade, compared to linear measurement, and also helps to better monitor response to therapy – especially when it comes to determining tumor doubling time. This refers to the fact that in most malignant lesions, volume increases first, followed by size.

From the pattern of findings to differential diagnosis

The software solution is also used for texture analysis, e.g. for characterizing parenchymal changes such as ground-glass opacities, reticular pattern or honeycombing. In this context, the distribution of the changes in the lung allows decisive conclusions to be drawn about the underlying clinical picture. However, differential diagnosis is a difficult and complex task due to the sheer number of existing lung pathologies. In addition, it is often the small but subtle differences that matter. Even experienced experts sometimes reach their limits here. “Sometimes it’s difficult to decide with subtle changes in the lower lobe: is this an emphysema bullae or honeycombing? The AI can differentiate such structures incredibly well,” Fanar Othman is pleased to report.

What the Oberhausen head physician also likes is that the structural parenchymal changes can be displayed in percentage form with the help of ADVANCE Chest CT: “When a check-up is due, you can’t always tell with the naked eye whether it has improved after therapy or not. If the response is not that great, it’s helpful to know by what percentage it ultimately got better. A lot of patients ask us explicitly about that, too.” So such clear feedback can have a positive effect on doctor-patient communication.

Overall, the expert is very pleased with how AI has been able to reduce the workload in his department over the past year while increasing diagnostic accuracy. He says his team is also enthusiastic and has already asked him – or rather the AI – for advice in one or two tricky cases. Othman is not worried that a machine could one day outstrip him: “I see it as an opportunity – especially in view of the fact that there is a shortage of staff anyway. AI is therefore a helpful support in the diagnostic process that makes a lot of things easier. In the end, it is still us who bring it all together with the laboratory values, clinical data and samples and come to a decision. That remains the fine art and continues to be the physician’s task.”

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Stets im Bild dank KI

Evangelisches Klinikum Niederrhein führt contextflow ADVANCE Chest CT in der Pneumologie ein

Lungenerkrankungen gehören weltweit zu den am häufigsten auftretenden sowie vielfältigsten Gesundheitsproblemen. Sie erfordern präzise Diagnosen und effektive Behandlungsstrategien. In diesem Zusammenhang eröffnet Künstliche Intelligenz (KI) einen vielversprechenden Weg, um verschiedenste Krankheitsbilder bei betroffenen Patienten so früh wie möglich zu erkennen. Auch Fanar Othman, Chefarzt der Klinik für Lungen- und Bronchialheilkunde am Johanniter Krankenhaus Oberhausen, hat die Möglichkeiten von Deep-Learning-basierter Technologie für sich entdeckt. Im Bereich der computertomographischen Bildgebung nutzt er das CE-zertifizierte KI-Programm contextflow ADVANCE Chest CT. Selbst wenn viel zu tun ist, entgeht dem Pneumologen so nichts mehr.

Als Teil des Verbunds Evangelisches Klinikum Niederrhein und BETHESDA Krankenhaus Duisburg betreut die Abteilung nicht nur jährlich um die 3.000 Patienten im eigenen Haus, sondern übernimmt darüber hinaus noch die konsiliarische Mitbetreuung von drei weiteren Standorten im westlichen Ruhrgebiet, darunter Dinslaken und Duisburg. Als ehemalige Hochburg der Kohle- und Stahlindustrie ist die Region auch als „Kohlenpott“ bekannt. Besonders in der älteren Generation sehen die Ärzte bis heute viele Patienten, denen ihre Arbeit in den früheren Bergwerken, Hochöfen und Fabriken massiv auf die Gesundheit geschlagen ist. Daher entfällt ein nicht unwesentlicher Teil der medizinischen Versorgung vor Ort auf berufsbedingte Lungenerkrankungen wie Staublunge (Pneumokoniose), Silikosen, Asbestosen und leider auch die daraus resultierenden Spätfolgen wie Krebs und Pleuramesotheliome. 

Viel zu tun also für die insgesamt neun Ärzte unter der Leitung von Fanar Othman. „Wir haben ein großes Einzugsgebiet und bekommen allein aufgrund der Größe unseres Verbundes neben den gängigen Krankheitsbildern auch eine Vielzahl seltener Pathologien zu Gesicht, die nicht so alltäglich sind“, so der Chefarzt. „In diesen Fällen kann es schon zur Herausforderung werden, die richtige Diagnose zu stellen und eine entsprechende Therapie einzuleiten.“ Dabei kann eine einzelne Erkrankung mit mehreren radiologischen Mustern verbunden sein. Eine genaue Charakterisierung ist oft mühsam und untersucherabhängig.

Schneller Zugriff auf relevantes Wissen

Seit letztem Jahr schafft die KI-Lösung von contextflow Abhilfe bei diesem Problem. Sie übernimmt das Sichten und Bereitstellen von für die Diagnostik relevanten Informationen aus CT-Untersuchungen. Dabei spürt die Erkennungssoftware selbst kleine Veränderungen im Lungenparenchym auf und stellt sie in Zusammenhang mit bestimmten Erkrankungen und deren Verlauf. Der daraus resultierende Befundbericht wird automatisch generiert und steht innerhalb weniger Minuten direkt im PACS-Viewer zur Verfügung. Auf diese Weise erspart das Tool dem Befunder, sich täglich durch Tausende von Schichtbildaufnahmen zu arbeiten und verhindert gleichzeitig, dass wichtige Erkenntnisse übersehen werden. 

Der Einsatz eines KI-Systems in seiner Abteilung ist Neuland für Fanar Othman. Aufmerksam gemacht wurde er auf ADVANCE Chest CT von seinem Arztkollegen, dem Chefarzt der Radiologie, Prof. Dr. Jörg Michael Neuerburg, der die Software bereits seit längerer Zeit erfolgreich nutzt. „Mir gefiel von Anfang an, wie umgänglich und benutzerfreundlich die Anwendung ist“, erinnert sich Othman. „Man muss sich praktisch um nichts kümmern. Die von der KI erstellten Befunde sind einfach und klar verständlich aufbereitet, sodass man die Ergebnisse ohne aufwendige Gegenprüfung übernehmen kann. Für alles andere bleibt in der täglichen Praxis auch gar nicht die Zeit. Da muss alles zack, zack gehen.“

Verlaufskontrollen auf Knopfdruck

Als sich die Gelegenheit bot, an einer von contextflow initiierten Produktschulung teilzunehmen, zögerte der Pneumologe nicht lang und ergriff die Chance. Während des Onlinetrainings stellte er fest, dass die Software noch einiges mehr kann, was für seine Arbeit von Wert ist: „Zum Beispiel ist die KI in der Lage, unterschiedliche Serien von Aufnahmen, die zu verschiedenen Zeitpunkten gemacht wurden, miteinander zu vergleichen. Dies ermöglicht es, die Größenentwicklung von Rundherden zu beurteilen, was sehr wichtig für das weitere therapeutische Vorgehen ist.“

Neben der Berechnung des Durchmessers in einer Ebene wendet das System auch die 3D-Volumenanalyse an, um die Gesamtmasse einer Lungenläsion zu bewerten. Die volumetrische Messmethode gewinnt zunehmend an Bedeutung, da sie im Vergleich zur linearen Messung eine genauere Einschätzung des Malignitätsrisikos bzw. des Tumorgrads bietet und außerdem dabei hilft, die Reaktion auf eine Therapie besser zu überwachen – insbesondere, wenn es darum geht, die Tumorverdopplungszeit festzustellen. Damit ist gemeint, dass bei den meisten bösartigen Läsionen zuerst das Volumen zunimmt, dann die Größe.

Vom Befundmuster zur Differentialdiagnose

Des Weiteren kommt die Softwarelösung auch bei der Texturanalyse zum Einsatz, z. B. bei der Charakterisierung von Parenchymveränderungen wie Milchglastrübungen, netzartigen retikulären Mustern oder Honigwabenbildung. Dabei lässt die Verteilung der Veränderungen in der Lunge entscheidende Rückschlüsse auf das zugrunde liegende Krankheitsbild zu. Dennoch gestaltet sich die Differentialdiagnose allein aufgrund der schieren Masse an existierenden Lungenpathologien als schwierige und komplexe Aufgabe. Hinzukommt, dass es häufig die kleinen, aber feinen Unterschiede sind, auf die es ankommt. Selbst erfahrene Experten stoßen hier teilweise an ihre Grenzen. „Manchmal ist es schwierig, bei subtilen Veränderungen im Unterlappen zu entscheiden: Ist das ein Emphysembullae oder Honigwabenmuster? Solche Strukturen kann die KI unglaublich gut differenzieren“, freut sich Fanar Othman.

Was dem Oberhausener Chefarzt außerdem gefällt, ist, dass sich die strukturellen Parenchymveränderungen mithilfe von ADVANCE Chest CT in prozentualer Form darstellen lassen: „Wenn eine Kontrolluntersuchung ansteht, kann man nicht immer mit bloßem Auge erkennen, ob es nach der Therapie besser geworden ist oder nicht. Wenn das Ansprechen nicht so großartig ausfällt, ist es hilfreich zu wissen, um wie viel Prozent es letztendlich besser geworden ist. Viele Patienten fragen uns auch explizit danach.“ So ein eindeutiges Feedback kann sich also positiv auf die Arzt-Patienten-Kommunikation auswirken.

Insgesamt zeigt sich der Experte sehr zufrieden damit, wie die KI die Arbeitslast in seiner Abteilung im letzten Jahr reduzieren konnte und gleichzeitig die diagnostische Genauigkeit erhöht hat. Auch sein Team sei begeistert und habe ihn – oder besser gesagt die KI – in dem ein oder anderen kniffeligen Fall schon um Rat gefragt. Dass eine Maschine ihm eines Tages den Rang ablaufen könnte, darüber macht sich Othman keine Sorgen: „Ich empfinde es als Chance – gerade auch vor dem Hintergrund, dass ohnehin Personalmangel herrscht. Die KI stellt daher eine hilfreiche Unterstützung bei der Befundung dar, die vieles erleichtert. Am Ende sind es immer noch wir, die das Ganze mit den Laborwerten, klinischen Daten und Proben zusammenführen und zu einer Entscheidung kommen. Das bleibt die hohe Kunst und weiterhin Aufgabe des Arztes.“

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Improving lung segmentation for higher coverage of clinically-relevant findings: a contextflow whitepaper

This whitepaper was developed by contextflow’s Scientific and R&D teams to explain our approach behind lung segmentation. The full text is listed below. For a pdf copy, click here.


Automatic lung segmentation in computed tomography (CT) is a critical component of computational medical image analysis. While many approaches exist, it remains a challenging problem for patients whose lungs are affected by disease. Here we describe and evaluate a lung segmentation algorithm that yields high accuracy despite disease patterns thanks to a 3D architecture and diverse training data set. We compare our algorithm with the established state-of-the-art algorithm.


Automatic lung segmentation in chest CT scans is relevant for several reasons. It serves as a stepping stone for additional lung-specific analysis such as nodule detection or disease pattern segmentation.  Focusing on the lung area helps to improve execution speed of dependent components and to disambiguate any finding in adjacent areas. By itself, lung segmentation yields a measurement of the lung volumetry, which can be meaningful, and is necessary to assess the proportion of disease patterns covering lung tissue.

The existing computer-assisted solutions for lung segmentation make use of a wide range of approaches with different levels of complexity pertaining to various use cases, such as lung cancer screening, COVID-19, or COPD assessment (Hu et al., 2001; Armato et al., 2004; Sluimer et al., 2005; Mansoor et al., 2015; Shamim et al., 2022).

At contextflow, we segment the lungs (chest cavity) by their most inclusive definition: we include pleural cavity patterns such as pneumothorax and effusion in our segmentation, even though they are technically not part of the lungs, and designate them accordingly, since they are relevant for comprehensive lung reporting. Our algorithms segment many pathological patterns if present, including pleural cavity patterns such as pneumothorax and effusion, using this inclusive lung segmentation as a starting point. In practice, lung segmentation needs to be simultaneously fast (because it is only one step in a host of different processes to analyze the lungs) and robust, as our product focuses on the detection and quantification of lung abnormalities, including comparison with former studies, to automatically detect (major) pathological changes.

A 3D approach towards robust lung segmentation in CT

To achieve robust lung segmentation, we first focused on the construction of an adequate database. We selected cases based on their clinical content, trying to cover as many different lung pathologies and anomalies as possible to ensure maximum diversity and extensive pathologic pattern coverage (see annex for details). We also made sure to cover a wide variety of acquisition protocols, since there is a large technical heterogeneity regarding CT vendors and acquisition parameters from different institutions around the world. In these cases, the lungs were manually annotated by expert radiologists. The annotation process consisted of a loop of annotations and quality checks until the annotation passed the quality check.

Additionally we worked on the choice of a relevant model architecture. We opted for a 3D-UNet-based architecture. One of the advantages of a model using 3D operations is that 3D information can disambiguate findings hard to discern in a 2D slice. For example, it can be hard to differentiate dense pathological patterns like pleural effusion from structures with soft tissue density like the liver on one slice only. In contrast to 2D models, the 3D model is expected to perform better in these cases, as it uses 3D context to make a prediction. Another advantage is that the model produces a segmentation that is consistent axially by design, contrary to a 2D model that processes each slice independently. It was also designed to be faster, finding a compromise between input size, number of parameters and performance.

Comparative evaluation of lung segmentation accuracy

In order to assess the performance of the developed model, we compared it with a state-of-the-art, publicly-available* model (lungmask) for lung segmentation as a baseline (Hofmanninger et al., 2020). The public model is ranked amongst the best on the public challenge LOLA** (LObe and Lung Analysis) and is frequently used for research purposes for organ segmentation. The model adopts a 2D approach for model training.  

We compared the performance between contextflow 2.0 and lungmask on 1722 scans that were sampled from clinical routine without restriction on age, sex, indication or pathology, and that cover findings such as cancer, emphysema, effusion, atelectasis, fibrosis and other pulmonary diseases. For 1694 cases, at least one Region-Of-Interest (ROI) containing a pulmonary pathological pattern is available, while 120 cases have a pixel-wise segmentation mask. All annotations were created by expert radiologists.

Results: segmentation accuracy evaluated by Dice coefficient

We use the widely-adopted Dice coefficient to compare how well the two models’ predictions overlap with an annotation done by a radiologist. The Dice score ranges between 0 (if there is no overlap) and 1 (complete overlap). We report the Dice coefficient for the whole lungs, the right lung and the left lung, measured on the test set in Table 1. We can see that both models achieve very high Dice on average. However, our method tends to achieve higher Dice with a lower standard deviation, which hints to a more robust segmentation performance over the diverse test dataset.

Results: focusing on regions containing disease patterns

To better assess the difference in performance between the two models, we introduce a metric called Region-Of-Interest (ROI) coverage. ROIs are labeled areas that contain a finding. Here, we consider rectangular ROIs on axial slices containing pulmonary pathological patterns. We define ROI coverage as the percentage of ROI centroids that the lung segmentation is able to cover, meaning that the center of the ROI is included in the segmentation. This metric assesses how accurate the lung segmentation model is, despite the presence of pulmonary diseases. This is crucial in its assessment as a tool to support diagnosis and assessment of lung imaging data. 

Annotating ROIs instead of a pixel-wise annotation of pathological patterns is beneficial because it is more time-efficient. In addition, some diffuse lung pathologies are difficult to annotate on a pixel-level even for expert radiologists, so coarsely annotating the entire affected region is more feasible. For this analysis we use ROIs that were created by radiologists on data that cover a wide range of pulmonary pathological patterns. 

* **

We report the ROI coverage figures in Table 2. The model developed by contextflow performs better overall with a coverage of 98.5% of ROIs compared to 96.5% for the open-source model. This effect is especially pronounced for patterns like masses (87.9% vs. 58.6%) and effusion (95.3% vs. 85.6%). This demonstrates that our model covers the lung pathologies more consistently than the open-source model.

Evaluating execution time

In terms of execution time, our model runs faster than the open-source one. We report the numbers for CPU execution on 8 threads for different sizes of CT scans in Table 3. By design, the open-source algorithm’s execution time directly depends on the number of slices in the input CT whereas the new model depends on the FOV (Field Of View) of the input scan. The developed model can generate lung segmentation much faster than the open-source model.


In this article we describe how we developed and evaluated a new lung segmentation algorithm. We compared it to an open-source, state-of-the-art solution over which we showed superiority with a conventional metric (Dice coefficient) and with a new clinical-finding-based metric (ROI coverage), showing a higher robustness to a wide variety of cases. Our developed solution also runs faster, making it scalable in terms of the number of scans we can process. Thus, our solution better meets the requirements for usage in clinical practice.


List of the patterns/pathologies we aim to cover with lung segmentation:

Airway wall thickening, Atelectasis, Bronchiectasis, Bulla, Consolidation, Cyst, Effusion, Emphysema, Ground glass opacification, Honeycombing, Mass, Mosaic attenuation pattern, Nodular pattern, Nodule, Pneumothorax, Pulmonary cavity, Reticular pattern, Tree-in-bud, Fibrosis, Interlobular septal thickening.


Hofmanninger J, Prayer F, Pan J, Röhrich S, Prosch H, Langs G. Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problem. European Radiology Experimental. 2020 Dec;4(1):1-3.

Mansoor A, Bagci U, Foster B, Xu Z, Papadakis GZ, Folio LR, Udupa JK, Mollura DJ. Segmentation and image analysis of abnormal lungs at CT: current approaches, challenges, and future trends. Radiographics. 2015 Jul;35(4):1056-76.

Shamim S, Awan MJ, Mohd Zain A, Naseem U, Mohammed MA, Garcia-Zapirain B. Automatic COVID-19 lung infection segmentation through modified unet model. Journal of healthcare engineering. 2022 Apr 11;2022.

Armato III SG, Sensakovic WF. Automated lung segmentation for thoracic CT: impact on computer-aided diagnosis1. Academic Radiology. 2004 Sep 1;11(9):1011-21.

Hu S, Hoffman EA, Reinhardt JM. Automatic lung segmentation for accurate quantitation of volumetric X-ray CT images. IEEE transactions on medical imaging. 2001 Jun;20(6):490-8.

Sluimer I, Prokop M, Van Ginneken B. Toward automated segmentation of the pathological lung in CT. IEEE transactions on medical imaging. 2005 Aug 1;24(8):1025-38.

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More than just nodule detection…

Radiologists at the Imapôle Lyon-Villeurbanne group benefit from the support of contextflow AI to detect lung anomalies

Medical imaging, a central pillar of modern medicine, has become indispensable –  a cornerstone of patient diagnosis – and it will be even more so moving forward. Looking towards the future, Imapôle Lyon-Villeurbanne, the medical imaging department of the largest private healthcare establishment in the Lyon region, Médipôle Lyon-Villeurbanne, is fully committed to this belief and has thus integrated contextflow ADVANCE Chest CT into its clinical routine. To better understand the reasons for adopting contextflow, the selection criteria, the deployment experience and the benefits observed, we spoke to Samir Lounis, CEO & General Manager at ImaOne. He manages and directs the Imapôle group’s activities.

Hello Mr. Lounis, could you tell us about Imapôle Lyon-Villeurbanne?

The Imapôle group consists of 10 radiologists responsible for reporting the medical images from two sites: Médipôle Lyon Villeurbanne, Europe’s largest private hospital with over 850 beds, and the Pôle Médical d’OL Vallée in Décines. These two sites carry out more than 800 examinations a day and around 170,000 examinations a year. 

Our team manages this workload, and we are strongly committed to the use of artificial intelligence solutions. We firmly believe in AI’s potential to support our radiologists and transform them into “augmented radiologists” in order to provide more accurate diagnoses and be able to manage a much higher volume of examinations.

What were the motivations and determining factors that led your radiology department to consider adopting contextflow in your clinical practice at Imapôle Lyon-Villeurbanne?

At Imapôle, a significant proportion of our work (∼1/3) is in oncology. This means that we have to interpret a large number of images to monitor or detect pathologies in our patients. In this context, we had been looking for a solution that could help us detect nodules and monitor their evolution in terms of size, particularly in terms of growth or shrinkage. 

Measuring a nodule is a complex task, subject to many variations. It depends on the anatomical plane used for the measurement and the inclination of the nodule itself. In addition, we wanted the measurements to be volumetric and reproducible. With all these factors in mind, we decided to use software. 

We are also involved in a lung cancer screening program at Médipôle – in France, studies are currently being carried out in this field. At Imapôle Lyon Villeurbanne, we have a large pneumology department, and we wanted to offer a solution that was reproducible, efficient and independent of the operator behind the screen. 

Of the various solutions we had identified, contextflow was one of the three finalists, and appeared to be the most efficient and comprehensive, meeting our needs.

What were the selection criteria and what preliminary evaluations were carried out before choosing contextflow’s software for your radiology department?

We explored the market to find solutions suited to our mission, which is to detect and monitor lung nodules over time. We also took into account other criteria such as the time taken to return results. It was essential that the analysis could be carried out quickly, with a return to the PACS and the doctor in around five minutes, in order to maintain our patient management flow. After a comparison of contextflow to other vendors, we chose contextflow because it offers more than just nodule detection and integrates very well into our PACS. 

The other point that was a “game changer” in our choice was contextflow’s ability to look ahead and offer incidental pulmonary embolism detection in the near future. This gives us a tool capable of responding to several of our problems, particularly in oncology, for long-term monitoring, analysis and reproducibility of measurements, as well as analysis and quantification of other pulmonary pathologies like emphysema.

Can you retrace the history of the integration of contextflow’s software in your radiology department, from its implementation to the present day?

contextflow’s technical teams have been extremely responsive. We were able to put them in touch with our IT and PACS teams, and all three teams quickly managed to install the virtual machine to carry out all the tests. We had a fairly tight deadline to achieve a level of integration that would enable us to use the system seamlessly without the doctor having to leave his environment. This was a key factor.

contextflow’s solution is fully integrated into our workflow. Sending is automatic from the modality to the AI solution, and the results are sent back to the PACS. So when the doctor reads the examination, they have the contextflow results at their fingertips. The technical support provided by the teams during the start-up phase was extremely responsive, which is very positive for contextflow. The level of integration with PACS is very high.

What were the key stages in the process of implementing contextflow’s software in your radiology department in terms of training, customization and change management?

As far as contextflow is concerned, training took place in two stages. First, there was a preliminary training session which essentially consisted of a product presentation, followed by a second session where the application of the product in clinical routine was presented. We examined a concrete case and analyzed the results obtained. This training was given by videoconference on different dates to suit the availability of the various doctors involved in the project, which was much appreciated. We were able to start using the solution with remote support from both contextflow and our IT and PACS teams. Everything went very smoothly.

After about a month’s use, contextflow offered to accompany our medical teams on site to benefit from their experience. This would also enable us to make personalized adjustments to the use of the product and help discover functionalities that might not have been fully grasped during the initial training sessions.

This support is still ongoing. An application engineer will come next week to meet our teams, and he will also come back if the doctors feel the need. 

Now, as far as contextflow is concerned, the big advantage is that it is not limited to detecting and monitoring nodules over time, which is essential and very important for lung cancer screening, for example, and for monitoring smokers. Rather, it also offers analysis of other pathologies, notably emphysema, which is a very important quantification, especially with a view to the future. 

In the future, it will also enable the detection of incidental pulmonary embolism, a crucial diagnosis in radiology. At Medipôle Lyon Villeurbanne, the largest private emergency department in France, we see around 250 patients a day, about half of whom go through the imaging department, and many of whom benefit from a CT scan. We’re very pleased to be supported by AI-based detection software for these 250 patients because radiologists’ workload is ever-increasing. You end up with 400 to 500 images to analyze per patient. So it’s great to have an artificial intelligence that can accompany you in this detection phase and highlight areas at risk.

That’s why contextflow’s ability to handle new pathologies to be analyzed was a decisive factor in our choice of solution.

How was contextflow’s software integrated into Imapôle’s existing radiology information system to ensure compatibility, interoperability and synchronization of clinical data?

What’s most important is the whole preparatory integration phase. This involves a considerable amount of work over several weeks, during which all the players involved can discuss technical constraints. The end users, in particular the radiologists, can express their expectations and objectives, particularly in terms of how they wish to find the results in his workflow.

The success of this stage is reflected in the fact that, in the end, the radiologist doesn’t need to leave their usual work environment. They open their PACS and work within it – the contextflow results are there without having to open a new program or change windows. The user is not confronted with a totally different interface. In addition, contextflow’s results can be adapted by the radiologist when they disagree, for example, in the case of a false positive nodule. The more transparent we can make the use of contextflow within the PACS and the more successful the integration, and the more the radiologist will use it.

How do you measure the overall satisfaction of contextflow users in your radiology department in terms of user-friendliness, performance and contribution to clinical decision-making?

Every click costs radiologists time and money, so having a well-integrated AI solution with as few clicks as possible was a priority for us. This is a key element in the use of the solution. If we suggest to a radiologist (who is already very busy and subject to the heavy mental workload linked to medical image analysis) additional constraints such as having to navigate between different windows or files, it’s certain that the solution will not be used. They may try it once or twice but will soon realize that it’s time-consuming, and they’ll end up saying to themselves “I’ll do without it” and never come back to it.

If, on the other hand, the whole process is automated, (i.e. the images are acquired by the scanner, automatically sent to contextflow for analysis, and results are sent back to the radiologist in their native working environment) then all they have to do is validate or invalidate the AI’s results for inclusion in the report. The number of clicks is reduced to a minimum. This makes the system extremely user-friendly. What’s more, the degree of integration of the solution with our PACS is extremely advanced, making our dependence on the solution even more beneficial.

What performance indicators and evaluation criteria are used to measure the effectiveness and clinical impact of contextflow’s software in your radiology department?

In terms of our prescribing physicians, we have a large number of pulmonologists and pulmonary oncologists in our department. So we have a team of doctors who specialize in lung diseases. They have been very satisfied with the contextflow software at an advanced level of pulmonary analysis, especially here in Lyon. They have particularly appreciated the ability to detect and track lung nodules over time and to compare results.

When a patient is sent for assessment after three or six months of chemotherapy, it is extremely valuable to have a tool like contextflow to ensure reproducibility of analysis and measurements. This has really been a major asset for our prescribing physicians.

Today, the use of the tool is practically demanded by prescribers, as they have become accustomed to its use. They therefore refer their patients to our center so that their scans can benefit from this additional in-house analysis. As far as our own doctors are concerned, as I mentioned earlier, the more transparent the interface in the workflow, the more it is used. As a result, 100% of lung scans now go through contextflow, benefiting from both medical and AI-assisted dual analysis. 

The feedback we’ve had from talking to doctors clearly shows that the tool has been adopted and used in the same way as the other AI tools we have in our fleet. We have a team of doctors who are forerunners in the adoption of AI, and they are aware of the benefits that artificial intelligence can bring them.

How would you like to see the contextflow solution evolve in the future?

I would love to see contextflow provide a solution for the detection of pulmonary embolism, as this is a real need for all emergency medical imaging departments. This will be of considerable help to emergency physicians and doctors, speeding up patient management and reducing the time lost in analysis. The contextflow team took our feedback seriously and is working towards this.

Still, we are very satisfied with the current solution. contextflow is continuously  improving the specificity and sensitivity of the nodule detection algorithm. Next, they plan to extend the possibilities of thoracic pathology analysis, not only for the lungs, but also for the vessels and heart, as well as for all organs located in the thoracic region. If contextflow could also provide analysis for these elements in the future, that would be a real asset.

AI is seen as the future, but it also raises fears. As a user, you may be both enthusiastic and reticent about certain applications. However, as a human being, you are aware of the implications and limits of AI. It can open the door to a wide range of possibilities. What is your opinion on this subject?

In today’s world, where everything is evolving rapidly – much faster than a human being can adapt – data, both medical and non-medical, is multiplying exponentially. The analysis of this data must also be multiplied.

However, human beings do not have the capacity to adapt instantly to such a flow of data. We may be able to do so in X years’ time, but today, we need solutions that support us in managing this data flow. Sorting and analyzing this data and information is crucial.

As far as I’m concerned, I can say that AI can raise concerns in certain areas. However, I believe that AI will not replace doctors. This is a fact that I have experienced by using these solutions for several years and observing them in our practice.

What is certain, however, is that the doctor who uses AI will replace the doctor who does not. And therein lies the real challenge. The world has evolved faster than human beings can adapt. It therefore needs technological tools. So, the doctor who integrates AI into his practice will surpass the doctor who doesn’t, plain and simple.

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