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

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

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

Surgical video sequences were collected from 112 patients who underwent laparoscopic procedures for suspected endometriosis between January 2020 and August 2023. Sequences with identifiable endometriosis lesions were included, while poor-quality images and sequences with prior surgical manipulation were excluded.

A deep neural network based on YOLOv5 was trained to detect and classify nine visual classes of endometriosis lesions (superficial black, superficial red, superficial white, superficial subtle, filmy adhesions, dense adhesions, deep endometriosis, ovarian endometrioma, and ovarian chocolate fluid).

The model performance was good for the “superficial black,” “superficial subtle,” and “ovarian chocolate fluid” classes (F1 score = 0.94, 0.74, and 0.75, respectively), acceptable for the “dense adhesion,” “ovarian endometrioma” and “deep endometriosis” classes (F1 score = 0.70, 0.63 and 0.632, respectively), and weak for the “superficial red,” “superficial white,” and “filmy adhesions” classes (F1 score = 0.25, 0.18, 0.16 and 0.02, respectively). However, while these results highlight the model's strong potential in identifying most lesions in at least one frame of each sequence, they underscore the need for further refinement to improve accuracy and precision.

This study demonstrates the feasibility of applying artificial intelligence for visual recognition of endometriosis during laparoscopic surgery. While the initial results are encouraging, further development is needed to enhance the model performance and standardize the annotation methods. The integration of AI in surgical practice holds promise for assisting in endometriosis diagnosis and improving surgical outcomes.