Thales and NEHS DIGITAL have joined forces to speed up the hospital admissions process in a crisis by using new technology. This project will leverage Thales’s expertise in radiology and artificial intelligence (AI) and NEHS DIGITAL’s extensive footprint in the French hospital system.
The partners will develop a solution in record time using AI to analyse CT imagery of the chest and lungs. As soon as the images are taken, the AI service will make an initial recommendation, including a preliminary diagnosis and assessment of the level of criticality of any lung damage, so that medical staff can admit the patient to the appropriate ward and prioritise the most urgent cases. The project was selected after a call for projects launched by the French Ministry for the Armed Forces to combat COVID-19.
Databases of medical imagery relating to the COVID-19 epidemic are virtually non-existent today. NEHS DIGITAL, in partnership with the SFR, has now begun collecting anonymised chest scan data from around 100 hospitals in France as part of the FIDAC (French Imaging Database Against Coronavirus) project. To analyse these datasets, which must be as large as possible to ensure they are representative, Thales will set up an infrastructure to train the necessary algorithms using machine learning and develop an AI system that can make automated recommendations to healthcare professionals.
Deployment of the AI service developed by Thales will make use of NEHS DIGITAL’s extensive cybersecure telemedicine infrastructure Hosted on a secure cloud. The partners aim to deploy an initial demonstrator of this solution over the next three months using NEHS DIGITAL’s existing platform, which is already used by almost all French hospitals. The solution will be continuously updated as the number of available images increases.
A medical committee will validate the solution and monitor rollout in hospitals. This large-scale project is a first step towards more widespread use of AI to support the work of radiologists. Thanks to this collaborative approach by multiple stakeholders, and with the architecture already in place, the AI service could also help to diagnose other pathologies in the future.
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