Investigator, Other (M) Surgery, Mass General Research Institute

Consultant Surgery-N/A, Massachusetts General Hospital

Director Surgical Artificial Intelligence and Innovation Laboratory, Massachusetts General Hospital

MD Sao Paulo State University 1999

esophageal achalasia; esophageal sphincter, lower; esophagoscopy; gastroscopes; gastroscopy; gastrostomy; laparoscopes; linitis plastica; natural orifice endoscopic surgery; pneumoperitoneum, artificial

Surgical Artificial Intelligence and Innovation Laboratory

Principal Investigator: Ozanan R. Meireles

Our primary emphasis is on utilizing computer vision to investigate the intraoperative phase of care through real-time, automated surgical analysis, empowering Artificial Intelligence to understand what is happening in an operation, reasoning and infer predictions.

We are building technology as the foundation for a worldwide database of surgical cases. A surgeon learns and improves one operation at a time. An AI system can learn from thousands of cases simultaneously. It allows for the collection, analysis and sharing of quantitative evidence in real-time across multiple surgeons -- a “collective surgical consciousness.” The goals of our research in surgery are, the democratize surgical knowledge, lowering costs, improve outcomes, and reduce morbidity and mortality.

Artificial Intelligence for Risk Prediction from Intraoperative Events
This study will utilize our team's previously developed computer vision-based analysis of intraoperative video to integrate quantitative intraoperative data with peri-operative data to improve the prediction of patient-specific complications and readmissions for patients undergoing laparoscopic cholecystectomy. Funding: CRICO Risk Management Foundation

Automated Intraoperative POEM Analysis: A Machine Learning Approach
The goal of this study is to develop artificial intelligence to generate compact segmentation and summarization of an endoscopic surgical procedure (per oral endoscopy myotomy) in real-time. This study builds off our initial pilot approach utilizing support vector machines for visual classification in sleeve gastrectomy and pivots to the use of deep learning for our visual model. Funding: Natural Orifice Surgery Consortium for Assessment and Research