Welcome to SORG
Welcome to our new website. Our previous website can be accessed below:
From developing predictive tools for surgical outcomes to investigating the impact of social determinants of health, SORG seeks to push the bounds of our understanding in orthopaedic care and beyond.
The Center for Physical Artificial Intelligence brings together cutting edge technology and clinical expertise to develop groundbreaking devices that reshape the way we think about assessing health.
MGB Orthopaedic Registries
SORG is responsible for managing the Mass General Brigham Orthopaedic Registries, incorporating both traditional and novel methods to drive new insights into patient outcomes in orthopaedic surgery.
FARIL’s mission is to unite foot and ankle specialists from across the globe to advance the care of foot and ankle patients through scientific research, innovative product design, and surgeon education.
Artificial intelligence, machine learning, and big data have transformed surgical care delivery. We develop and provide the most advanced models for predicting surgical outcomes, classifying medical images, and improving surgery.
SORG at NASS 2021:
- New Book from SORG
This book deals with the simulation of the mechanical behavior of engineering structures, mechanisms and components. It presents a set of strategies and tools for formulating the mathematical equations and the methods of solving them using MATLAB. For the same mechanical systems, it also shows how to obtain solutions using a different approaches. It then compares the results obtained with the two methods. By combining fundamentals of kinematics and dynamics of mechanisms with applications and different solutions in MATLAB of problems related to gears, cams, and multilink mechanisms, and by presenting the concepts in an accessible manner, this book is intended to assist advanced undergraduate and mechanical engineering graduate students in solving various kinds of dynamical problems by using methods in MATLAB. It also offers a comprehensive, practice-oriented guide to mechanical engineers dealing with kinematics and dynamics of several mechanical systems.
- Notes from the Journal Club (10/25/2021)
Today in our journal club, we discussed a paper by Weissman et al. (2021) titled “Effects of Neighborhood-level Data on Performance and Algorithmic Equity of a Model That Predicts 30-day Heart Failure Readmissions at an Urban Academic Medical Center,” published in the Journal of Cardiac Failure.
We began our discussion with an overview of area-level indices derived from geocode Census data–these ranged from the Neighborhood Stress Score (NSS7), Area Deprivation Index (ADI), and Social Vulnerability Index (SVI). Weissman et al. found that model performance and algorithmic equity for predicting 30-day readmission in congestive heart failure patients was not significantly improved with the addition of ADI patients within their test dataset. We nevertheless found the paper to be a valuable example of investigating subgroup-level variation in predictive model performance and incorporating both measures of algorithmic equity and area-level indices into our own analyses.
Looking forward, we talked about how it would be important to consider where the addition of area-level data might provide the most value as we aim to produce clinically useful tools and insights and reduce algorithmic inequity. Our discussion also touched on additional recent papers by Vest (2021) and Rethorn (2020).
- Notes from recent Journal Club meetings
We are sharing notes from our regular collaborative journal club meetings—here are some updates from our last couple of meetings:
October 4, 2021:
In today’s journal club, we discussed a paper by Rajkomar et al (2018) in npj digital medicine titled “Scalable and accurate deep learning with electronic health records.” We discussed the approach taken within this study of using the entire electronic medical record data as the basis for deep learning prediction of medical events including inpatient mortality, extended length of stay, unplanned readmission, and discharge diagnoses. (The analysis was performed by Google with data from UCSF and UCM). The models leveraged FHIR-representations of the EHR data, avoiding specific selection of variables to use for prediction. They showed improvement over existing EHR-based predictive models that were used as baseline comparisons for the various prediction tasks.
We discussed our interest in learning more about how the performance of such models might vary for patients of different subgroups. The datasets on which the models were evaluated consisted of hospitalizations for broad sets of patients hospitalized for many different reasons. It would be helpful to compare these more general models’ performance to that of predictive models for patients with particular conditions or receiving particular treatments. In addition, it might be valuable to know whether particular subgroups of patients (by age, other demographics, reason for hospitalization) might present greater challenges for the models to predict outcomes. Another point of discussion was whether or not such models, once trained on data from a particular institution, might be generalizable to data at other institutions.
September 27, 2021:
In this session, we discussed a manuscript titled “Deep neural network improves fracture detection by clinicians” published in Proceedings of the National Academy of Sciences by Lindsey et. Al (collaboration between Imagen Tech and Hospital for Special Surgery).
Since Emergency medicine clinicians at the ER might not have the expertise to detect specific joint fractures, the authors decided to develop a deep learning model to detect wrist-related fractures from the X-ray images with the purpose of using the system in the emergency departments. Authors used the model’s prediction side-to-side the original image to help the clinicians, and demonstrated that the model performs both increases the detection accuracy, and speeds up the time-spent for diagnosis.
A point was raised in the discussion that how this model can be utilized, or even if it is needed. We discussed that some advanced hospitals like MGH have access to facilities and orthopedic surgeons that supervise clinicians before finalizing their decisions, and therefore, the fracture missing rate is smaller than other hospitals. We also talked about how model’s prediction might bias the clinicians to rely on the computer’s judgment instead of their own judgement, which makes having a highly-accurate and robust system even more important. We also discussed that the main limitation of studies such as this one is the closed-system (having in-house codes and datasets), which makes the 3rd party model validations impossible.
- Meet Dr. Joseph Schwab
Dr. Joseph Schwab, Chief of the Orthopaedic Spine Center at Massachusetts General Hospital and Director of SORG, is featured in a new video from the Department of Orthopaedic Surgery at MGH. Watch the video to hear Dr. Schwab discuss the importance of the multidisciplinary care that orthopaedic oncology patients receive at MGH and how he makes sure he focuses on the needs of his patients.
- New Book from SORG
This book provides a comprehensive review of the epidemiology, pathogenesis, diagnosis, and management of chordomas of the mobile spine and sacrum. Historically, knowledge about how to deliver such state-of-the-art care has not been widespread, resulting in inconsistent treatment, and, all too often, suboptimal outcomes for chordoma patients. This text is an important step towards broadening that knowledge, and, in turn, improving the care provided to chordoma patients. The book is divided into 4 parts comprising 16 chapters. The first part focuses on the pathophysiology and molecular drivers of chordoma. The second focuses on the epidemiology and clinical history, as well as the histological, oncologic, and radiographic work-up of chordoma. The third part focuses primarily on the technical aspects of surgery for chordoma. It is broken down by anatomic region, with the final two chapters focusing on the soft tissue and bony reconstruction following chordoma resection. The last part focuses on the exciting field of adjuvant therapies for chordoma.