Contact Information

Surgical Artificial Intelligence and Innovation Laboratory (SAIIL)

Wang Ambulatory Care Center (WAC)-339
Massachusetts General Hospital
15 Parkman Street
Boston, MA  02114

Phone: 617-724-5530

Email: saiil@mgh.harvard.edu

Accessible
Near Public Transit

For more information, please contact Ozanan Meireles, MD, or Daniel Hashimoto, MD.

Explore This Laboratory

    About the Lab

    The Massachusetts General Hospital Surgical Artificial Intelligence and Innovation Laboratory (SAIIL) is a multidisciplinary group composed of surgeons, engineers and data scientists who are passionate about redesigning the delivery of surgical care. The team is made up of surgeons in Mass General’s Department of Surgery and scientists from Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory (CSAIL). Together, our team has developed tools to help unlock the intraoperative phase of care.

    Our primary emphasis is on utilizing computer vision to investigate the intraoperative phase of care through real-time, automated surgical analysis. In other words, we use artificial intelligence (AI) to automatically analyze and interpret videos of operations as they are occurring. The goal is to teach the AI to understand what is happening in an operation, determine whether the risk for a postoperative complication is high, or even provide surgeons with additional data to improve operating room decisions.

    While the field of surgical research has improved its ability to study pre- and postoperative events and risk using claims data and patient registries, the intraoperative phase of care remains difficult to study.

    In a review of nationwide data, researchers estimated that major intraoperative adverse events (i.e. accidental damage to bowel or major blood vessels) can occur in 2% of all operations. Approximately 22 million general surgery operations are performed each year in the United States and 440,000 patients may experience an intraoperative adverse event a year. The cost of hospital admission is 41% higher in these patients, and the consequences of adverse events can impact their quality of life. An expected one-day stay could turn into a month-long hospitalization, additional procedures, prolonged rehabilitation, or a host of other life-altering consequences.

    Research Vision and Goals

    We will help big data realize personalized medicine for surgical patients. We envision a technology-enabled operating room that pulls data from prior operations for real-time clinical decision making, much like a GPS for surgeons.

    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 to:

    • Democratize surgical knowledge
    • Lower costs
    • Improve outcomes
    • Reduce morbidity and mortality

    Research Projects

    Our research focuses on using computer analysis to improve operations. By using artificial intelligence, we can work to improve surgical care.

    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 perioperative 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

    News

    AI and the Future of Surgery
    General Surgery News, April 2020

    Utilizing Computer Vision to Investigate the Intraoperative Phase of Care through Real-Time, Automated Surgical Analysis
    Intelligent Health Summit; Basel, Switzerland; September 2019
    A presentation by Mass General surgeon Ozanan Meireles, MD

    Artificial Intelligence in Surgery
    European Association of Endoscopic Surgeons; Seville, Spain; June 2019
    A keynote address given by Daniel Hashimoto, MD, MS

    How Will Artificial Intelligence Impact Surgical Patient Care? Part 2
    OR Manager, May 2019

    How Will Artificial Intelligence Impact Surgical Patient Care? Part 1
    OR Manager, April 2019

    Young Alumni Service Award
    UPenn Alumni, April 2019
    Daniel Hashimoto, MD, MS, awarded Young Alumni Service Award from the University of Pennsylvania School of Medicine for contributions to the field of surgery in artificial intelligence as well as for service to the Penn Medicine alumni community.

    Surgeons Seek Consensus on Bile Duct Injury Prevention
    General Surgery News, January 2019

    Season 2, Episode 3
    EY Better Innovations podcast, January 2019
    Mass General SAIIL Fellow Dan Hashimoto, MD, participates in a roundtable discussion on the use of artificial intelligence to analyze operative data

    To Boldly Go - Remote
    Harvard Medicine Magazine, January 2019
    Mass General SAIIL Fellow Dan Hashimoto, MD, discusses the intersection of artificial intelligence and virtual reality to train surgeons

    The Robots Are Coming. Will They Work With Us?
    PBS News, December 2018
    Mass General surgeon Ozanan Meireles, MD, and SAIIL Fellow Dan Hashimoto, MD, discuss their work developing artificial intelligence for use in the operating room

    Using Video Data for Safer Surgery
    TEDx, August 2018
    Mass General SAIIL Fellow Dan Hashimoto, MD, gave a TEDx Talk on using artificial intelligence in surgery to create the “collective surgical consciousness”

    Surgical Fingerprints: Real-Time Analysis and Summarization of Intraoperative Events
    World Medical Innovation Forum, April 2018
    Mass General SAIIL Fellow Dan Hashimoto, MD, presents the lab's work on artificial intelligence at the World Medical Innovation Forum

    Shrinking Data for Surgical Training
    MIT News, June 2017
    Mass General and MIT Innovators

    2017 40 Under 40 Healthcare Innovators
    MedTech Boston, April 2017
    Mass General SAIIL Fellow Dan Hashimoto, MD, named one of MedTech Boston’s "40 under 40 Healthcare Innovators"

    Publications

    The following are publications from the Mass General SAIIL team:

    Meet the Team

    Director

    Ozanan Meireles, MD, FACS
    General Surgeon, Mass General
    Assistant Professor of Surgery, Harvard Medical School

    Research Team

    Guy Rosman, PhD
    Associate Director of Engineering

    Yutong Ban, PhD
    Postdoctoral Research Fellow

    Jennifer A. Eckhoff, MD
    Surgical AI & Innovation Fellow

    Brendan J. Brady
    Research Assistant

    Elan Witkowski, MD, MPH
    General Surgeon, Mass General
    Instructor of Surgery, Harvard Medical School

    Key Collaborators

    Teodor Grantcharov, MD, PhD
    Professor of Surgery, University of Toronto
    Director, International Centre for Surgical Safety

    Daniel A. Hashimoto, MD, MS
    Associate Director of Research

    Quanzeng Li, PhD
    Associate Professor of Radiology

    Daniela Rus, PhD
    Director, Computer Science and Artificial Intelligence Laboratory, MIT

    Former Surgical AI & Innovation Fellows

    Daniel A. Hashimoto, MD, MS
    Associate Director of Research

    Thomas M. Ward, MD
    Surgical AI & Innovation Fellow


    https://www.generalsurgerynews.com/In-the-News/Article/03-20/AI-and-the-Future-of-Surgery/57511