Surgical Artificial Intelligence and Innovation Laboratory

The Massachusetts General Hospital Surgical Artificial Intelligence and Innovation Laboratory focuses on the use of artificial intelligence to reimagine the way surgery is performed.

Overview

The Massachusetts General Hospital Surgical Artificial Intelligence and Innovation Laboratory (SAIIL) is a multidisciplinary group comprised 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

Support Us

The Mass General Surgical Artificial Intelligence and Innovation Laboratory is reimagining the way surgery is performed. Learn more about how you can support the lab's work

Group Members

Director

Research Team

Key Collaborators

  • Daniela Rus, PhD

    Director, Computer Science and Artificial Intelligence Laboratory, MIT

  • Synho Do, PhD

    Director, Laboratory of Medical Imaging and Computation
    Mass General Assistant Medical Director, MGPO

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 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
 

Research Position

Mass General’s Surgical Artificial Intelligence and Innovation Laboratory in collaboration with Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory's (CSAIL) Distributed Robotics Lab are looking for a smart and energetic machine learning/computer vision postdoctoral candidate for a project aimed at revolutionizing surgical care. Candidates should have good knowledge of probability and inference, reinforcement learning, and basic knowledge in computer vision with some experience in one or more deep learning frameworks.

This is a two-year project with possibility for extension and the potential to grow toward both a research and a commercialization track. The project includes both inference/computationally novel aspects, as well as clinical ones. Computational elements include computer vision and modeling aspects, as well as predictive analytics to assist surgeons in their work.

Candidates will have the opportunity to work with novel datasets that combine streaming surgical data with datasets of clinical outcomes and procedural quality. The project has the potential to significantly redefine the delivery and quality of surgical care in both resource rich and poor settings.

Learn more about this position.

Skills

  • PhD in one of the EE/CS fields that leveraged probabilistic reasoning and/or machine learning techniques
  • Understanding of either probabilistic inference or statistical signal processing
  • Basic understanding and some experience with computer vision / robotic perception
  • Working knowledge with deep learning frameworks such as Tensorflow or Pytorch
  • Good programming skills in python/Matlab, some skills in C/C++
  • No medical knowledge necessary

How to Apply

Candidates should contact either Dr. Daniel Hashimoto or Dr. Guy Rosman with the following:

  1. Updated curriculum vitae
  2. Names and contact information for 2 or 3 individuals who can serve as personal and professional references
  3. A cover letter that explains your goals for your postdoctoral training, and specifics about how you think your goals fit into the overall theme of our work
  4. A statement regarding your work eligibility in the U.S., and whether you are looking for help in obtaining work permissions
  5. Candidates should be prepared to provide copies of publications that emanated from your dissertation or other related work (preprints are fine) on request

Supervising Faculty

  • Daniela Rus, PhD
    Andrew (1956) and Erna Viterbi Professor of Electrical Engineering and Computer Science
    Director, Computer Science and Artificial Intelligence Laboratory (CSAIL)
    Massachusetts Institute of Technology

  • Ozanan Meireles, MD, FACS
    Assistant Professor of Surgery
    Director, Surgical Artificial Intelligence and Innovation Laboratory (SAIIL)
    Massachusetts General Hospital | Harvard Medical School

Publications

The following are publications from the Mass General SAIIL team:

News

The following are recent news articles from Mass General SAIIL:

Contact

Surgical Artificial Intelligence and Innovation Laboratory (SAIIL)
Massachusetts General Hospital
15 Parkman Street, Wang Ambulatory Care Center (WAC)-339
Boston, MA 02114

Phone: 617-643-2040

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

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