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.

Object Detection in Laparoscopic Surgery: A Comparative Study of Deep Learning Models on a Custom Endometriosis Dataset.

Laparoscopic surgery for endometriosis presents unique challenges due to the complexity of and variability in lesion appearances within the abdominal cavity. This study investigates the application of deep learning models for object detection in laparoscopic videos, aiming to assist surgeons in accurately identifying and localizing endometriosis lesions and related anatomical structures. A custom dataset was curated, comprising of 199 video sequences and 205,725 frames.

Is minor surgery safe during the COVID-19 pandemic? A multi-disciplinary study.

Background: To assess the risk of postoperative SARS-CoV-2 infection during the COVID-19 pandemic.

Methods: The CONCEPTION study was a cohort, multidisciplinary study conducted at Conception University Hospital, in France, from March 17th to May 11th, 2020. Our study included all adult patients who underwent minor surgery in one of the seven surgical departments of our hospital: urology, digestive, plastic, gynecological, otolaryngology, gynecology or maxillofacial surgery.