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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.
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:
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
General Surgeon, Mass GeneralAssistant Professor of Surgery, Harvard Medical School
Surgical Artificial Intelligence and Innovation Fellow
General Surgeon, Mass GeneralInstructor of Surgery, Harvard Medical School
Director, Computer Science and Artificial Intelligence Laboratory, MIT
Director, Laboratory of Medical Imaging and ComputationMass General Assistant Medical Director, MGPO
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 EventsThis 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 ApproachThe 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
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.
How to Apply
Candidates should contact either Dr. Daniel Hashimoto or Dr. Guy Rosman with the following:
The following are publications from the Mass General SAIIL team:
The following are recent news articles from Mass General SAIIL:
Surgical Artificial Intelligence and Innovation Laboratory (SAIIL)Massachusetts General Hospital15 Parkman Street, Wang Ambulatory Care Center (WAC)-339Boston, MA 02114
For more information, please contact Ozanan Meireles, MD, or Daniel Hashimoto, MD
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