Initial results in the automatic visual recognition of endometriosis lesions by artificial intelligence during laparoscopy: a proof-of-concept study.

Objective: To develop a machine learning method for the automatic recognition of endometriosis lesions during laparoscopic surgery and evaluate its feasibility and performance.

Design: Collecting and annotating surgical videos and training, validating, and testing a deep neural network.

Setting: Multicenter proof-of-concept study using surgical videos from expert centers in France, Hungary, Brazil, and Denmark.

Landmark-free automatic digital twin registration in robot-assisted partial nephrectomy using a generic end-to-end model.

Purpose: Augmented Reality in Minimally Invasive Surgery has made tremendous progress in organs including the liver and the uterus. The core problem of Augmented Reality is registration, where a preoperative patient's geometric digital twin must be aligned with the image of the surgical camera. The case of the kidney is yet unresolved, owing to the absence of anatomical landmarks visible in both the patient's digital twin and the surgical images.