In an interview for PBS NewsHour's Future of Work series, Massachusetts General Hospital Surgeon Ozanan Meireles, MD, and General Surgery Resident Daniel Hashimoto, MD, MS, discuss their work in developing software that is smart enough to offer sound advice in the midst of surgery.
Departments, Centers, & Programs:
15 Parkman Street
Boston, MA 02114-3117
Weight Center, MGH
50 Staniford Street
Boston, MA 02114-2517
North Shore Medical Center
81 Highland Ave
Salem, MA 01923
- MD, Sao Paulo State University School of Medicine - UNESP Botucatu
- Residency, Michigan State University
- Residency, Santa Casa de Misericordia de Sao Paulo
- Fellowship, UC San Diego Medical Center
American Board Certifications
- Surgery, American Board of Surgery
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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
- Artificial Intelligence in Surgery: Promises and Perils. Hashimoto DA, Rosman G, Rus D, Meireles OR. Ann Surg. 2018 Jul;268(1):70-76. doi: 10.1097/SLA.0000000000002693. PMID: 29389679
- Roux-en-Y gastric bypass is associated with an increased exposure to ionizing radiation. Nau P, Molina G, Shima A, Hani A, Meireles O.Surg Obes Relat Dis. 2015 Mar-Apr;11(2):308-12. doi: 10.1016/j.soard.2014.07.022. Epub 2014 Sep 16.PMID:25820075
- Transesophageal endoscopic myotomy (TEEM) for the treatment of achalasia: the United States human experience. Meireles OR, Horgan S, Jacobsen GR, Katagiri T, Mathew A, Sedrak M, Sandler BJ, Dotai T, Savides TJ, Majid SF, Nijhawan S, Talamini MA. Surg Endosc. 2013 May;27(5):1803-9.
- Broad clinical utilization of NOTES: is it safe? Horgan S, Meireles OR, Jacobsen GR, Sandler BJ, Ferreres A, Ramamoorthy S, Savides T, Katagiri T, Dotai T, Sedrak M, Majid SF, Nijhawan S, Talamini MA. Surg Endosc. 2013 Jun;27(6):1872-80