contextflow launches a new version of SEARCH Lung CT

News Press

2022-03-22

Thanks to radiologists’ feedback, the latest version of SEARCH Lung CT now includes 3 new developments: 1) consolidation pattern is included as part of the quantitative image analysis results in the Insights Screen; 2) disease patterns can be shown color-coded in your viewer using DICOM secondary capture, 3) nodule detection results can be provided as a TID1500-compliant DICOM SR object.

1) The Insights Screen now provides lung coverage values and distribution maps for 7 image patterns + visualization and measurements of detected lung nodules (top center above). The image patterns include: consolidation, effusion, emphysema, ground-glass opacity, honeycombing, pneumothorax, and reticular pattern.

2) These same 7 image patterns can be seen color-coded in your viewer using DICOM secondary capture…no need to click outof your viewer! (see below)

3) SEARCH Lung CT also provides nodule detection results in form of a DICOM Enhanced Structured Report object that follows DICOM Structured Reporting template TID1500.

The DICOM SR object lists all detected pulmonary nodules and provides the following information for those: location reference, long-axis diameter, short-axis diameter, average diameter, and volume.

The object is intended to be sent to and parsed by structured reporting systems or PACS for seamless integration of nodule detection results into the radiologists’ reporting workflow. If supported by your PACS, this feature allows you to accept/reject contextflow’s nodule results within your viewer.

For more information or to schedule a personalized demo, click here.

Oncological imaging – AI is indispensable for lung tissue classification

News Press

2022-06-28

Evangelisches Klinikum Niederrhein uses SEARCH Lung CT for distribution and volumetric quantifications of disease patterns

Has artificial intelligence (AI) come to stay? The answer is clearly yes, because from laboratory medicine to radiology, it is helping medical professionals obtain plausible, quantifiable and reproducible results in significantly less time. And as a result, it is easing the burden on medical departments suffering from large workloads. AI will actually take over many activities in the future in areas where it is readily applicable. “Repetitive work in our specialties, such as determining and matching values, is an excellent domain for the use of machines,” says Prof. Dr. Jörg Michael Neuerburg, Chief Physician of the Central Department of Diagnostic and Interventional Radiology at the Evangelisches Klinikum Niederrhein (EvKIN) Ev. Hospital BETHESDA.

The network is an academic teaching hospital, part of the University of Düsseldorf, and has five locations in the Ruhr region, including Ev. Klinikum Duisburg-Nord, Johanniter Krankenhaus Oberhausen, Herzzentrum Duisburg-Meiderich, Ev. Klinikum Dinslaken and Ev. Krankenhaus BETHESDA zu Duisburg. As a maximum care provider, the hospital operates a thorax center with two pulmonology departments as well as a heart center. The radiology department employs 16 radiologists and 9 neuroradiologists, serving half a million people from the Lower Rhine to the Ruhr region.

Artificial intelligence must be embedded

For AI to be accepted, reproducibility of results is an important factor; according to Prof. Neuerburg; however, complete integration into the usual radiological workflow is an absolute must. The problem with AI systems, as well as their predecessors – CAD systems, has always been implementation into the workflow: “If a separate program has to be opened and the images also have to be sent to another computer, the workflow is delayed. Radiology, like all other departments, is measured by throughput. If AI means additional work, acceptance is low. This problem has been solved very well by contextflow in collaboration with VISUS; SEARCH Lung CT is perfectly integrated into our workflow,” says Prof. Neuerburg.

The introduction of AI as a joint task

The radiologists at EvKIN work with the JiveX PACS from VISUS (Compugroup) and the ORBIS hospital information system from Dedalus HealthCare, both of which are already closely integrated. This provided good conditions for installing SEARCH Lung CT from contextflow in coordination with the pulmonologists. After the initial problems on the part of the legal department regarding data transfer were solved (it had to be ensured that no data protection guidelines would be affected during data transfer to other servers), the integration of the new program succeeded very quickly and without affecting ongoing operations thanks to the cooperation of the in-house IT, VISUS and contextflow.

The pitfalls of oncological imaging

In radiology, oncological imaging is a never-ending challenge: the targeted search for the tumor, its standardized classification, in the case of treatment, the assessment of its progress (or not). “Let’s take a patient with a large brain hemorrhage as an example. To answer the question of whether tumors were already present, the current examination must be compared with the previous examination. Of course, artificial intelligence can absolutely answer that much faster and more efficiently than we can,” says Prof. Neuerburg, describing a current case for the use of AI.

The chief radiologist already has broad experience with AI systems and uses three programs in his department: BoneXpert, an AI-based bone age determination system used in pediatrics to identify growth retardation or acceleration, and in forensics to determine the age of delinquent juveniles. Further AI support is provided by Transpara – a mammography screening tool that uses a graduation from zero to ten to indicate the probability of developing breast cancer.

For lung diagnostics, radiologists at EvKIN have been relying on SEARCH Lung CT for the past year to improve the overall quality and quantity of lung diagnostics, and in particular, to assist in assessing the distribution pattern of emphysema. “These distribution patterns are important to pulmonologists because they serve as the basis for setting valves to adequately ventilate the lungs. Therefore, we have adapted our findings to provide quantitative results on the extent to which, for example, the upper lobe is ventilated differently than the middle lobe after valve placement,” the radiologist explains.

In addition, SEARCH Lung CT is used for lung nodule detection staging during follow-ups. Previous examinations are compared with the current results to identify increasing structural densities. In addition, the system detects new nodules and measures the volumes of the existing ones; thus, it enables an assessment of a treatment’s progress. Thus, the radiologists at EvKIN mainly rely on quantitatively measurable changes. “SEARCH Lung CT is currently still in the development phase, so we use the tool as an add-on and report the volume information as a supplement to our findings without these values being standardized in the workflow,” explains Prof. Neuerburg.

On the expert’s wishlist is the expansion of the software to include the pleural region, for example, to detect occupational diseases such as mesothelioma, which occurs after exposure to asbestos. At the moment, the tool analyzes the lungs, but not all structures. Therefore, the radiologist’s additional visual findings are still necessary at this stage, especially since in the case of bronchial carcinomas, a look at the adrenal glands or the liver is also advisable to see whether metastases of the primary tumor have formed in the abdomen.

Standardization and classification pave the way for AI 

After a long lead-up, radiology is now moving swiftly toward standardized findings. What began years ago with the BI-RADS classifications in breast cancer screening has now become established via the PI-RADS classification in prostate imaging and the LI-RADS and ACR classifications in lung screening: a stringent and institution-independent staging system. This refers to the classification of tumors into specific disease stages, which subsequently require different treatment. For example, metastasized diseases are not only removed surgically; whereas early stages related to the organ can certainly be treated surgically, depending on the histology. “This is the direction in which AI will develop and have a significant impact on the lives of radiologists in the future. Because this is where the volumetry of lesions and volumetric comparison come into play, which is now mandatory for tumor center certification as part of oncology standardized staging.

“On the way to standardized reporting in radiology, the software from contextflow will therefore provide us with important support,” concludes Prof. Neuerburg, hinting at the latest paths in medical imaging.

Onkologische Bildgebung – KI zur Lungengewebeklassifizierung unverzichtbar

News Press

2022-06-28

Ev. Klinikum Niederrhein nutzt SEARCH Lung CT für volumetrische Bestimmungen und Übersicht über Verteilungsmuster

Ist die Künstliche Intelligenz (KI) gekommen, um zu bleiben? Die Antwort lautet eindeutig ja, denn von der Labormedizin bis zur Radiologie unterstützt sie Mediziner dabei, plausible, quantifizierbare und reproduzierbare Ergebnisse in deutlich kürzerer Zeit zu erhalten. Damit entlastet sie die unter großem Arbeitsaufkommen leidenden medizinischen Abteilungen. KI wird viele Tätigkeiten in den Bereichen, wo sie gut einsetzbar ist, künftig auch tatsächlich übernehmen. „Sich wiederholende Arbeiten in unseren Fachgebieten wie beispielsweise Werte zu ermitteln und abzugleichen, sind eine hervorragende Domäne für den Einsatz von Maschinen“, ist Prof. Dr. Jörg Michael Neuerburg, Chefarzt der zentralen Abteilung für Diagnostische und Interventionelle Radiologie, Evangelisches Klinikum Niederrhein (EvKlN) Ev. Krankenhaus BETHESDA, überzeugt.

Der Verbund ist Akademisches Lehrkrankenhaus der Universität Düsseldorf und hat mit dem Ev. Klinikum Duisburg-Nord, dem Johanniter Krankenhaus Oberhausen, dem Herzzentrum Duisburg-Meiderich, dem Ev. Krankenhaus Dinslaken und dem Ev. Krankenhaus BETHESDA zu Duisburg fünf Standorte im Ruhrgebiet. Als Maximalversorger betreibt das Klinikum ein Thoraxzentrum mit zwei pulmologischen Abteilungen sowie ein Herzzentrum. In der radiologischen Abteilung arbeiten 16 Radiologen und 9 Neuroradiologen, die ein Einzugsgebiet von einer halben Million Menschen vom Niederrhein bis zum Ruhrgebiet betreuen.

Künstliche Intelligenz muss eingebettet sein

Für die Akzeptanz beim Einsatz von KI ist nicht nur die Reproduzierbarkeit der Ergebnisse ein wichtiger Faktor, sondern laut Prof. Dr. Neuerburg auch die vollständige Integration in den gewohnten radiologischen Workflow ein absolutes Muss. Das Problem der KI-Systeme wie auch ihrer Vorläufer, den CAD-Systemen, war immer die Implementierung in den Arbeitsablauf: „Wenn ein separates Programm geöffnet werden muss und die Bilder möglicherweise zusätzlich an einen anderen Rechner geschickt werden müssen, verzögert sich der Arbeitsablauf. Die Radiologie, wie auch alle anderen Abteilungen, wird nach Durchsatz bemessen. Wenn KI zusätzliche Arbeit bedeutet, ist die Akzeptanz gering. Dieses Problem hat contextflow in Zusammenarbeit mit VISUS sehr gut gelöst,      SEARCH Lung CT ist bestens in unseren Workflow integriert“, sagt Prof. Neuerburg.

Die Einführung von KI als Gemeinschaftsaufgabe

Die Radiologen am EvKIN arbeiten mit dem JiveX-PACS von VISUS (Compugroup) und dem Krankenhaus-Informationssystem ORBIS von Dedalus HealthCare, die beide bereits eng verzahnt sind. Damit waren gute Voraussetzungen gegeben, um in Abstimmung mit den Pneumologen SEARCH Lung CT von contextflow zu installieren. Nachdem die anfänglichen Probleme von Seiten der Rechtsabteilung bezüglich des Datentransfers gelöst wurden – es musste sichergestellt werden, dass bei der Datenübertragung auf andere Server keine Datenschutzrichtlinien tangiert würden –, gelang die Integration des neuen Programms dank des Zusammenwirkens der hauseigenen IT und der Firmen VISUS und contextflow sehr schnell und ohne den laufenden Betrieb zu beeinträchtigen.

Die Tücken der onkologischen Bildgebung

In der Radiologie ist die onkologische Bildgebung eine stets wiederkehrende Herausforderung: die gezielte Suche nach dem Tumor, seine standardisierte Bestimmung und im Fall der Therapie die Verlaufsbeurteilung. „Nehmen wir als Beispiel einen Patienten mit einem metastasierten Bronchialkarzinom. Um die Frage zu beantworten, ob bereits Rundherde bestanden, muss die aktuelle Untersuchung mit der Voruntersuchung verglichen werden. Das kann natürlich die Künstliche Intelligenz viel schneller und viel effizienter beantworten als wir“, beschreibt Prof. Neuerburg einen aktuellen Fall für den Einsatz von KI.

Der Chefradiologe hat bereits breite Erfahrung mit KI-Systemen und setzt in seiner Abteilung drei Programme ein: BoneXpert, ein KI-basiertes System zur Bestimmung des Knochenalters, das in der Pädiatrie Anwendung findet, um Wachstumsverzögerungen oder -beschleunigungen zu ermitteln, sowie in der Forensik zur Altersbestimmung straffällig gewordener Jugendlicher. Weitere KI-Unterstützung liefert Transpara – ein Mammographie Screening Tool, das mittels einer Graduierung von Null bis Zehn die Wahrscheinlichkeit angibt, an Brustkrebs zu erkranken.

Für die Lungendiagnostik bauen die Radiologen im EvKIN seit einem Jahr auf SEARCH Lung CT, um die Lungendiagnostik qualitativ und quantitativ insgesamt zu verbessern und insbesondere bei der Abschätzung des Verteilungsmusters von Emphysemen Unterstützung zu erhalten. „Diese Verteilungsmuster sind für die Pneumologen wichtig, weil sie als Grundlage dafür dienen, Ventile zur ausreichenden Belüftung der Lunge zu setzen. Daher haben wir unsere Befunde angepasst und liefern quantitative Ergebnisse, inwieweit beispielsweise nach der Ventilsetzung der Oberlappen anders belüftet wird als der Mittellappen“, erklärt der Radiologe die Vorgehensweise.

Außerdem wird SEARCH Lung CT im Rahmen des Stagings für die Rundherderkennung im Follow-up genutzt. Dazu werden die Voruntersuchungen mit den aktuellen Ergebnissen verglichen, um modulare Strukturverdichtungen zu identifizieren. Zudem erkennt das System neue Herde und misst die Volumina der bestehenden; so ermöglicht es eine Beurteilung des Therapieverlaufs. Es sind also die quantitativ messbaren Veränderungen, auf die sich die Radiologen am EvKIN derzeit hauptsächlich stützen. „SEARCH Lung CT ist momentan noch in der Entwicklungsphase, so dass wir das Tool als Add-on nutzen und die Volumenangabe als Ergänzung in unserem Befund angeben, ohne dass diese Werte standardisiert in den Workflow eingegangen sind“, erklärt Prof. Neuerburg.

Auf der Wunschliste des Experten steht die Erweiterung der Software um den Bereich der Pleura, um zum Beispiel Berufserkrankungen wie das Mesotheliom, das nach Asbestexpositionen auftritt, zu erkennen. Im Augenblick analysiert das Tool die Lunge, allerdings auch nicht alle Strukturen. Daher ist die zusätzliche visuelle Befundung des Radiologen in dieser Phase weiterhin nötig, zumal bei Bronchialkarzinomen auch ein Blick auf die Nebennieren oder die Leber angeraten ist, um zu schauen, ob sich im Abdomen Metastasen des primären Tumors gebildet haben.

Standardisierung und Klassifikation bereiten den Weg für KI 

Nach einem langen Vorlauf bewegt sich die Radiologie inzwischen zügig hin zu standardisierten Befunden. Was mit den BI-RADS-Klassifikationen im Brustkrebs-Screening vor Jahren begonnen hat, hat sich über die PI-RADS-Klassifikation bei der Prostatabildgebung bis zu den LI-RADS-Klassifikationen bei der Leberbildgebung etabliert: ein stringentes und institutsunabhängiges Stagingsystem. Darunter ist die Einteilung von Tumorerkrankungen in bestimmte Krankheitsstufen zu verstehen, die in der Folge unterschiedliche Therapiekonzepte erfordern. So werden metastasierte Erkrankungen beispielsweise nicht nur operativ entfernt, wohingegen auf das Organ bezogene frühe Stadien in Abhängigkeit von der Histologie durchaus operativ zu therapieren sind. „In diese Richtung wird sich die KI entwickeln und das Leben der Radiologen künftig maßgeblich beeinflussen. Denn hier kommt die Volumetrie von Läsionen, der volumetrische Vergleich, ins Spiel, der im Rahmen des onkologischen standardisierten Stagings für die Zertifizierung von Tumorzentren mittlerweile vorgeschrieben ist.

„Auf dem Weg zur standardisierten Befundung in der Radiologie wird die Software von contextflow uns also wichtige Unterstützung liefern“, skizziert Prof. Neuerburg abschließend die neuen Pfade in der Bildgebung.

Alphatron Medical & contextflow announce breakthrough for immediate use of AI without barriers

News Press

2022-05-18

Immediate access via existing IT infrastructure contributes to the speed of innovation in radiology 

Workflow specialists Alphatron Medical Systems B.V. and chest CT experts contextflow GmbH have partnered to easily deliver contextflow SEARCH Lung CT clinical decision support to hospitals throughout the Netherlands via an existing DICOM mail network to which all Dutch hospitals are already connected.

contextflow develops clinical decision support (CDS) tools together with radiologists to help keep their constantly increasing workload manageable and improve patient care. The company’s core technology automatically detects, quantifies and visualizes 7 disease patterns and lung nodules in CTs of the lungs, displaying relevant information directly in the radiologist’s PACS viewer. In addition, contextflow provides similar patient reference cases and differential diagnosis literature for 19 lung disease patterns within seconds. In a recent clinical study at the Medical University of Vienna, radiologists experienced a general timesavings of 31% when reading reports with contextflow SEARCH Lung CT available (publication coming soon).

Alphatron Medical is known throughout the Netherlands for securely sharing medical images and records digitally via its DICOM mail network. Now radiologists who want to obtain contextflow’s quantitative results for a particular patient can do so by uploading a CT of the lungs to Alphatron Medical’s DICOM mail network from their existing PACS. contextflow will analyze the CTs and deliver quantitative results for lung nodules and disease patterns back to the requesting radiologist in DICOM format. This allows radiologists to test contextflow’s system immediately without having to undergo a lengthy testing and implementation process.

Regarding the announcement, contextflow Chief Commercial Officer Marcel Wassink explains: “Implementation of AI tools has so far been a lengthy process that leads to a lot of frustrations for radiologists who are eager to try out AI. Alphatron Medical allows us to get our quantitative thoracic CT results to the point of care much faster and without hassle in a system that is already known and trusted nationwide.”

Alphatron Director of Enterprise Imaging Patrick Zondag continues: “It’s great to see that we can continue to expand the success of the DICOM mail network and make new innovations available to all healthcare providers in an approachable way.”

The test feature will be available to all radiology departments in Alphatron Medical’s DICOM mail network. For more information, contact Alphatron Medical B.V. at +31 88 – 55 06 200 or info@alphatronmedical.com.

About Alphatron

The Enterprise Imaging division of Alphatron Medical develops and supplies software solutions that improve healthcare workflows. The specialists at Alphatron Medical develop complete solutions together with their customers by means of smart applications and integration of software applications. Alphatron Medical’s best-known products include the Enterprise Imaging solutions JiveX Healthcare Content Management, JiveX PACS and the nationwide DICOM mail network (Twiin Portal). For more information, visit www.alphatronmedical.com.

Künstliche Intelligenz hilft, Lungenerkrankungen besser zu differenzieren

News Press

2022-04-25

Radiologische Gemeinschaftspraxis Calw-Leonberg setzt auf SEARCH Lung CT von contextflow

Nicht nur Standarduntersuchungen, auch hochaufgelöste Dünnschichtuntersuchungen im CT, beispielsweise zur Diagnostik von Lungenerkrankungen am Stützgerüst, gehören zum klinischen Alltag der Gemeinschaftspraxis in Calw-Leonberg. Mit SEARCH Lung CT gelingt es ab sofort, bessere Untersuchungen durchzuführen – sowohl quantitativ als auch qualitativ.  

Jeden Tag müssen Tausende von Bildern in der Praxis gesichtet und mit Voruntersuchungen verglichen werden. Hinzu kommen das Erkennen und Ausmessen von Erkrankungsmustern und Lungenherden – auch zeitlich eine große Herausforderung. „Die KI-gestützte Software liefert uns neben einer präzisen und schnelleren Diagnostik ein Plus an Sensitivität und Sensibilität“, sagt Dr. Ekkehard Scholtz, Radiologe in der Radiologischen Gemeinschaftspraxis Calw und Leonberg. Er ist zuversichtlich, mit der neuen Software künftig rund 30 Prozent Zeit zu gewinnen, die er dann für seine Patienten einsetzen kann.

Einfaches Arbeiten mit SEARCH Lung CT 

„Für Anomalien der Lunge bietet SEARCH Lung CT ein sehr großes Portfolio an Texturanalysen. Die Segmentierung von Auffälligkeiten funktioniert äußerst gut. Neben der Musterbeschreibung und Evaluierung möglicher Differenzialdiagnosen liefert das System zudem eine Referenzierung zur aktuellen Literatur“, beschreibt Markus Krenn, Chef-Produktmanager bei contextflow, die Vorzüge des Produkts.

Und so einfach funktioniert es: Nachdem der Radiologe den fraglichen Bereich markiert hat, öffnet sich die Benutzeroberfläche von contextflow und bietet eine Analyse der Lunge: Neben Krankheitsmustern, der Verteilung von Rundherden und ihrem Volumen steht eine Auswahlliste möglicher Erkrankungen zur Verfügung. Der Radiologe bewertet die Messungen und Vorschläge des Systems, unterstützt durch grafisch dargestellte Verteilungsmuster in der Lunge und bereitgestellte Volumenmessungen. Die fertige Analyse des Bereichs wird abschließend automatisch als PDF-Befund generiert.

Um die Befundung für den Radiologen weiter zu erleichtern, arbeitet contextflow derzeit an einem Update, das Lungenbefunde nicht nur auflistet, sondern auch vergleicht im Laufe der Zeit. „Den Kliniker interessiert vor allem, wie sich die Metastasen verhalten – werden sie größer oder kleiner –, um das Therapieansprechen zu beurteilen. Eine anspruchsvolle Aufgabe, denn die Herde verhalten sich oft widersprüchlich. Hier ist das Programm eine unschätzbare Hilfe“, äußert sich Dr. Scholtz zufrieden.

Reibungslose Integration in die bestehende IT-Infrastruktur

Die radiologische Gemeinschaftspraxis Calw und Leonberg hat sehr früh auf die vollständige Digitalisierung aller Praxisvorgänge gesetzt. „Wir arbeiten vollständig digital – von der Verteilung der Untersuchungsaufträge bis zur Anmeldung an die Geräte, von der Spracherkennung, Bild- und Materialverwaltung bis zum Dosismanagement. In unserem System laufen viele Unterfunktionen zusammen und SEARCH Lung CT ist eine solche tiefintegrierte Unterfunktion“, beschreibt Dr. Scholtz die hauseigene IT-Architektur. Dafür sorgte die sehr gute Zusammenarbeit zwischen contextflow, der internen IT und dem Softwarehersteller Medigration, einem Unternehmen der bender gruppe, mit dem die Gemeinschaftspraxis seit vielen Jahren zusammenarbeitet.

Im klinischen Alltag sind die Radiologen dankbar für alles, was Zeit einspart und die Diagnostik verbessert. „Das System ist perfekt integriert und der Workflow so leichtgängig, dass es praktisch keine Einarbeitungszeit gibt. Wir alle nutzen es automatisch, weil wir als Befunder einen echten Benefit für unsere Patienten bekommen“, so Dr. Scholtz abschließend.

Artificial intelligence helps to better differentiate lung diseases

News Press

2022-04-25

The radiology group practice CALW-Leonberg relies on SEARCH Lung CT from contextflow

Radiological diagnostics has continued to grow in recent years and is playing its part in improving patient care with ever greater precision. But this progress also has its downsides: Radiologists sift through and compare thousands of image slices from CTs and MRIs with increasingly better quality and thinner slices. This improved image quality is better for patients, of course, but it also results in more workload for radiologists. So what can be done to manage this workload in everyday radiology?

“We decided to purchase contextflow SEARCH Lung CT (software) for lung diagnostics, because the computer is an ideal tool to manage such challenges. All practices are under great economic pressure, and trends show that in the future, even more, not fewer, examinations will have to be managed in a shorter time,” says Dr. Ekkehard Scholtz, radiologist at the Calw and Leonberg radiology group practice, which serves not only outpatients there, but also offers radiology services as far away as Stuttgart. At the Calw location, outpatients are cared for in the city center; and in Leonberg, where the practice premises are located in the district hospital (Klinikverbund Südwest), the radiologists take care of outpatients and inpatients in the hospital.

At the radiology group practice in Calw-Leonberg, clinical routine includes not only standard examinations, but also high-resolution, thin-slice CT examinations used for the diagnosis of lung diseases and lung nodules. To better differentiate these diseases, the practice now uses software from contextflow.

Complexities of lung disease 

In contrast to X-rays, which provide a very good but simple overview of the condition of the lungs, CTs provide high-resolution insights in the sub-millimeter range and thus enable the diagnosis of nodules, tumors, inflammations, malformations, injuries and fractures, including those of the ribs. “Despite all the technological and medical advances, lung disease remains a major challenge for us to this day. The renaissance of CT is also due to the fact that we can distinguish diseased lung very precisely, but unfortunately not one hundred percent. The patterns of the different types of lung disease overlap or mix, which makes it difficult to make a clear diagnosis,” explains Dr. Scholtz. “So we looked for software that could help us make a precise diagnosis. After all, the strength of AI is precisely to recognize patterns. In this respect, we are delighted to be using contextflow’s software.”

Expectations meet reality 

CALW-Leonberg expects quantitative and qualitative improvements through the use of the new software. “Some days, our radiologists see between 20,000 and 30,000 high-resolution slices. In the case of the lungs, these are millimeter slices in three planes, so there are quickly up to 500 images that come together. These have to be viewed and, in some cases, compared with previous examinations. In addition, ILDs and any nodules must be measured. The AI is intended to help manage this workload. Qualitatively, it is a good inspection tool that sees findings that we might miss in routine clinical practice. Its use therefore provides better sensitivity and sensibility.”

So far, SEARCH Lung CT has delivered exactly what radiologists expected it to: more and better quality exams can be performed in less time. “We’re still in the early stages, and I estimate that when it’s fully functional, we’ll save about 30 percent of reading time with the new program. Above all, that means we will be able to help more patients,” says Scholtz confidently.

Where SEARCH Lung CT helps 

Apart from the sheer mass of images to be processed, nodules and lung metastases have to be diagnosed and not only compared with previous examinations and older images in a high resolution, but also the nodules have to be measured manually with millimeter precision. This task can now be made easier with use of the software. Another aspect is pattern recognition, which plays such a big role in detecting lung diseases. The software helps to better differentiate the individual components of the various disease patterns that show up mixed or overlapping in the CT images. This is very important for targeted patient care, because there are lung diseases that end in fibrosis, a severe loss of function of the lung. Recently, however, therapeutics have come on the market that can influence the course of the disease and bring certain forms of fibrosis to a halt. In this respect, it is important to find out which form of fibrosis the patient is suffering from and whether they can be helped with these new therapeutic agents. So if it is possible to train pattern recognition in ILDs accordingly and thus obtain more differentiated statements, that will be a great benefit for patients. 

How the system works

To use SEARCH Lung CT, the radiologist highlights an area of interest in a lung CT (in their native viewer), and then contextflow’s user interface opens in a new browser tab. “The home screen opens up and offers an analysis of the lung: disease patterns and nodules, their distribution and volume as well as a shortlist of possible diseases,” describes Scholtz. The radiologist takes over the evaluation of these measurements and suggestions, supported by heatmaps indicating the distribution of these disease patterns and their measurements by volume. An updated version of the software under development will display the disease patterns and nodules in enlarged form, measure their volume and then compare them with the disease patterns and nodules from the previous examinations. This is helpful because clinicians are most interested in how anomalies change over time in order to assess the patient’s treatment response. This is all the more challenging because there are often many nodules that also show contradictory behavior in that some get smaller while others get larger. At this point, the program is an invaluable help. 

Smooth integration into existing IT

“Our goal from the beginning was to integrate SEARCH Lung CT into our existing IT infrastructure and not simply install another program. This process was challenging, but in the meantime the software has been integrated in such a way that we can activate SEARCH Lung CT from within our routine program,” Scholtz explains. The lung area to be diagnosed is marked in the radiologist’s viewer, and the finished analysis of the selected region is automatically generated as a PDF report. The prerequisite for this seamless integration was the very good cooperation between contextflow, our in-house IT and the IT provider Medigration from Bender Group, with whom CALW-Leonberg has been working for many years. “We work completely digitally – from the distribution of examination orders to the registration to the devices; from speech recognition, image and material management to dose management. There are a large number of sub-functions that converge in this system, and SEARCH Lung CT is one such deeply integrated sub-function.”

On a day-to-day clinical basis, radiologists are grateful for anything that saves time and improves diagnostics. “The system is perfectly integrated, and the workflow is so smooth that there is virtually no learning curve. We all use it automatically because we as diagnosticians get a real benefit for our patients,” says a satisfied Dr. Scholtz.

Looking toward the future

Since even the best solution still leaves something to be desired, contextflow is currently working on a feature that not only lists lung findings, but also compares them over time. In addition, the software is looking to move from the pattern-level to the disease-level, meaning it will also immediately provide suggestions as to which diseases these patterns may belong to for a given patient.

contextflow Welcomes Jacob Visser as Clinical Advisor

News Press

2022-02-16

The addition of Jacob is an important part of contextflow’s mission to improve radiology workflows by developing software in close collaboration with leading clinical radiologists.

contextflow GmbH, the Vienna-based developer of deep learning-based software for medical image analysis, is thrilled to announce the addition of Jacob Visser as Clinical Advisor. Jacob will make contributions around high-quality annotated data, user interface (UI) design, and the clinical impact of patterns/diseases for product development. 

Jacob holds several distinguished positions at Erasmus MC in Rotterdam, NL: Assistant Professor of Value-Based Imaging, Chief Medical Information Officer, and Head of Imaging IT and Value-Based Imaging. He is also a member of the European Society of Radiology’s (ESR) Value-Based Radiology Subcommittee, and serves as a member of the European Society of Medical Imaging Informatics’ (EuSoMii) Scientific Committee. As if his schedule weren’t busy enough, Jacob contributes to the Radiological Society of North America’s (RSNA) Working Group for Common Data Elements.

As contextflow CEO & Co-Founder Markus Holzer puts it, “Jacob’s resume reflects both his skills as a practicing radiologist and the trust the radiology community has in his abilities to translate new technologies into improved patient outcomes. We’re incredibly lucky to benefit from his guidance.” 

Regarding his inspiration to join the team, Jacob says, “I have been following contextflow’s progress practically since the company’s founding, and their traction in the area of lung CT is impressive. Being able to shape clinical decision support tools that myself and colleagues can benefit from in clinical practice is a big motivator. We’re literally shaping the future.”

Radiologists at the University of Innsbruck rely on SEARCH Lung CT from contextflow

News Press

2022-02-08

Almost every second computed tomography (CT) scan of the lung shows abnormal findings. Most of the time these are pulmonary nodules, one possible indicator of cancer. But which findings are harmless and which need to be checked and treated? Assessing lung CTs is no simple task, but radiologists are increasingly finding support in new technologies. contextflow SEARCH Lung CT is a clinical decision support system that detects, visualizes and quantifies lung anomalies and pulmonary nodules. “In detail, it provides location and extent of changes and heat maps for six image patterns, as well as visualizations and measurements of detected pulmonary nodules. In addition, the tool analyzes and classifies 19 image patterns in selected regions of a scan; retrieves visually-similar, expert-verified reference cases; and provides relevant links to literature, guidelines and differential diagnoses,” explains Markus Krenn, Chief Product Officer at contextflow.

A deep learning-based solution to simplify daily work

Since October 2021, a team of radiologists led by PD Mag. Dr. Med. Univ. Gerlig Widmann, Managing Senior Physician at the University Department of Radiology at the Medical University of Innsbruck, has been using SEARCH Lung CT. “We expect significant added value for our work and the patients, particularly on account of the system’s ability to quantify lung anomalies. The software provides us with percentages of pathological changes, visualizes the dynamics of these changes over time and suggests reference cases with similar findings and diagnostic literature for differential diagnosis,” says Dr. Widmann. The University Department of Radiology at the Medical University of Innsbruck is one of the largest institutions for radiological diagnostics in Austria and treats the vast majority of lung patients in Tyrol in close cooperation with the departments of oncology, thoracic surgery, pneumology and the lung department of Natters Hospital. The first experiences with SEARCH Lung CT have been thoroughly positive, as the managing senior physician continues: “The segmentation of abnormalities such as shadows, reticular patterns or emphysema works extremely well. The platform is very clearly structured with references to current literature, including pattern description and a list of possible differential diagnoses. You can clearly see that there is a valid reference data set behind the AI.”

Last but not least, Dr. Widmann also expects to be able to establish a diagnosis more quickly: in a recent study at the Medical University of Vienna, the average report reading time was 31% shorter when SEARCH Lung CT was available for use*. These findings were true for both young and experienced radiologists.

*Publication forthcoming

Integration with Dedalus DeepUnity PACS ensures smooth workflow

“The implementation of SEARCH Lung CT was simple, quick and straightforward. The cooperation between our IT and contextflow was exemplary,” says Dr. Widmann happily.

The clinical decision support software is seamlessly integrated into the hospital’s image data management system (PACS). A radiologist wishing to use SEARCH Lung CT only has to click one button to seamlessly continue working with the tool. The images are then automatically evaluated and transferred to the report. “It’s all very simple. So far we are very satisfied with SEARCH Lung CT,” concludes Dr. Widmann.