Even for specialized physicians, the visual versatility of some Endometriosis lesions makes them hard to identify. Further, the surgery environment obviates the detection of subtle lesions which form the most common type of Endometriosis.
Aiming to pass on the immense experience of the pioneer surgeons, we are working to configure a system for automatic detection of Endometriosis which is able to work real-time during laparasocopic surgeries using a customized machine-learning model adapted to recognize subtle Endometriosis lesions based on a unique and extensive dataset.
Dr. Seckin, March 8, 2019 - Lenox Hill Hospital, NYC
Technical Lead
R&D