Artificial intelligence for the evaluation of profiles, trajectories and management of vulnerability in health: COVID-19, frailty of the elderly and cancer
Detection of endometriosis lesions from MRI images
Develop AI software to detect and characterize endometriosis on pelvic MRI
Endometriosis is a disease that is still poorly known and poorly diagnosed. The average length of time before a patient is diagnosed is 7 years after the onset of the first symptoms and 75% of patients receive at least one erroneous diagnosis. Diagnostic errors are partly due to the difficulty of analyzing pelvic MRI images and the lack of specialized radiologists, who represent less than 1% of radiologists in France and are poorly distributed throughout the country.
The project employs a set of machine learning technologies to recognize and model the organs of the female pelvis and classify the different types of endometriosis lesions according to their location.
Elise Mekkaoui () & Raphaelle Taub ()