Project description DEENESFRITPL Combination of deep learning-based tools to improve radiology workflow and reporting quality The EU-funded Contextflow project proposes to develop deep learning-based tools to improve radiology workflows and save time while improving reporting quality, capitalising on the existing SEARCH and TRIAGE tools. SEARCH is a 3D image-based tool designed to reduce search time for difficult cases. Lung diseases are hard to diagnose and SEARCH can detect 19 disease patterns, linking a 3D image to reference cases with similar findings. TRIAGE is a tool that automatically detects disease patterns to quickly identify time-critical patients. The current project aims to complete technical and business development before product launch. The coronavirus (COVID-19) is characterised by anomalous lung patterns that Contextflow is capable of detecting, presenting opportunity for efficient high-priority disease diagnostics. Show the project objective Hide the project objective Objective 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 (competitors only 1-3), 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. Fields of science natural sciencescomputer and information sciencessoftwaremedical and health sciencesclinical medicineradiologymedical and health scienceshealth sciencesinfectious diseasesRNA virusescoronaviruses Programme(s) H2020-EU.2.3. - INDUSTRIAL LEADERSHIP - Innovation In SMEs Main Programme H2020-EU.3. - PRIORITY 'Societal challenges H2020-EU.2.1. - INDUSTRIAL LEADERSHIP - Leadership in enabling and industrial technologies Topic(s) EIC-SMEInst-2018-2020 - SME instrument Call for proposal H2020-EIC-SMEInst-2018-2020 See other projects for this call Sub call H2020-EIC-SMEInst-2018-2020-3 Funding Scheme SME-2b - SME Instrument (grant only and blended finance) Coordinator CONTEXTFLOW GMBH Net EU contribution € 1 205 872,50 Address Margaretenstrasse 70/2/8 1050 Wien Austria See on map Region Ostösterreich Wien Wien Activity type Private for-profit entities (excluding Higher or Secondary Education Establishments) Links Contact the organisation Opens in new window Participation in EU R&I programmes Opens in new window HORIZON collaboration network Opens in new window Other funding € 516 802,50
