Radiology is struggling. Exponentially increasing quantities of data make it difficult to index and search for relevant information when it’s needed most. Radiologists’ workload is also rapidly increasing, exacerbated by the global radiologist shortage. What’s more, new treatments require more complex diagnoses. When faced with a difficult case, radiologists must currently wait to discuss with colleagues, consult reference books or guess search terms in text-based resources. This frustrating, time-consuming process leads to delays, missed findings and high overtime expense.
contextflow develops deep learning-based tools to improve radiology workflows, saving time while increasing reporting quality. SEARCH is a 3D image-based search engine designed to help reduce search time for difficult cases. Lung diseases are particularly hard to diagnose; they are characterised by the combination and distribution of 40 anomalous patterns observed in lung CTs. SEARCH can already detect 19 disease patterns, instantly linking a 3D image to reference cases with similar findings, case statistics and reference information necessary for differential diagnosis. TRIAGE is a separate tool that automatically detects disease patterns in scans so that doctors quickly identify time-critical patients. contextflow aims to reduce the time radiologists spend searching for information, allowing for both faster and higher-quality diagnostics.
The EIC project will complete the remaining technical and business development activities before product launch: developing methods to detect lung diseases based on the anomalous lung disease patterns already detected by the software; and identifying signatures indicative of diseases in medical data to support personalised treatment decisions. As the coronavirus (COVID-19) is characterised by distributions of anomalous patterns that contextflow already detects, developing disease detection for COVID-19 infections is a high priority.
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