Dr. Calvin Perumalla joined the Stanford's TECI Center team as a postdoctoral researcher in February 2021. He received his Masters and Doctorate degrees in Electrical Engineering from the University of South Florida. His graduate research work involved building a novel cardiac rhythm monitor with enhanced diagnostic capabilities. He was also involved in building machine learning models to detect cardiac abnormalities. Dr. Perumalla later spent two years working at a late-stage startup where he was involved in building AI models to detect anomalies in computer networks. He is passionate about using AI to improve the quality of human life and his current research interests include Computer Vision, Image Segmentation and Surgical Data Science.
Carla Pugh, Postdoctoral Faculty Sponsor
AI-Based Video Segmentation: Procedural Steps or Basic Maneuvers?
The Journal of surgical research
2022; 283: 500-506
INTRODUCTION: Video-based review of surgical procedures has proven to be useful in training by enabling efficiency in the qualitative assessment of surgical skill and intraoperative decision-making. Current video segmentation protocols focus largely on procedural steps. Although some operations are more complex than others, many of the steps in any given procedure involve an intricate choreography of basic maneuvers such as suturing, knot tying, and cutting. The use of these maneuvers at certain procedural steps can convey information that aids in the assessment of the complexity of the procedure, surgical preference, and skill. Our study aims to develop and evaluate an algorithm to identify these maneuvers.METHODS: A standard deep learning architecture was used to differentiate between suture throws, knot ties, and suture cutting on a data set comprised of videos from practicing clinicians (N=52) who participated in a simulated enterotomy repair. Perception of the added value to traditional artificial intelligence segmentation was explored by qualitatively examining the utility of identifying maneuvers in a subset of steps for an open colon resection.RESULTS: An accuracy of 84% was reached in differentiating maneuvers. The precision in detecting the basic maneuvers was 87.9%, 60%, and 90.9% for suture throws, knot ties, and suture cutting, respectively. The qualitative concept mapping confirmed realistic scenarios that could benefit from basic maneuver identification.CONCLUSIONS: Basic maneuvers can indicate error management activity or safety measures and allow for the assessment of skill. Our deep learning algorithm identified basic maneuvers with reasonable accuracy. Such models can aid in artificial intelligence-assisted video review by providing additional information that can complement traditional video segmentation protocols.
View details for DOI 10.1016/j.jss.2022.10.069
View details for PubMedID 36436286
Do Individual Surgeon Preferences Affect Procedural Outcomes?
Annals of surgery
OBJECTIVES: Surgeon preferences such as instrument and suture selection and idiosyncratic approaches to individual procedure steps have been largely viewed as minor differences in the surgical workflow. We hypothesized that idiosyncratic approaches could be quantified and shown to have measurable effects on procedural outcomes.METHODS: At the ACS Clinical Congress, experienced surgeons volunteered to wear motion tracking sensors and be videotaped while evaluating a loop of porcine intestines to identify and repair two pre-configured, standardized enterotomies. Video annotation was used to identify individual surgeon preferences and motion data was used to quantify surgical actions. Chi-square analysis was used to determine whether surgical preferences were associated with procedure outcomes (bowel leak).RESULTS: Surgeons' (N=255) preferences were categorized into four technical decisions. Three out of the four technical decisions (repaired injuries together, double layer closure, corner-stitches versus no corner-stitches) played a significant role in outcomes, P<0.05. Running versus interrupted did not affect outcomes. Motion analysis revealed significant differences in average operative times (leak-6.67 min vs. no leak-8.88 min, P=0.0004) and work effort (leak-path length=36.86cm vs. no leak-path length=49.99cm, P=0.001). Surgeons who took the riskiest path but did not leak had better bimanual dexterity (leak=0.21/1.0 vs. no leak=0.33/1.0, P=0.047) and placed more sutures during the repair (leak=4.69 sutures vs. no leak=6.09 sutures, P=0.03).CONCLUSION: Our results show that individual preferences affect technical decisions and play a significant role in procedural outcomes. Future analysis in more complex procedures may make major contributions to our understanding of contributors to procedure outcomes.
View details for DOI 10.1097/SLA.0000000000005595
View details for PubMedID 35861074
- Artificial intelligence in surgery: A research team perspective. Current problems in surgery 2022; 59 (6): 101125
- In Brief. Current problems in surgery 2022; 59 (6): 101127
Performance assessment using sensor technology.
Journal of surgical oncology
2021; 124 (2): 200-215
Over the past 30 years, there have been numerous, noteworthy successes in the development, validation, and implementation of clinical skills assessments. Despite this progress, the medical profession has barely scratched the surface towards developing assessments that capture the true complexity of hands-on skills in procedural medicine. This paper highlights the development implementation and new discoveries in performance metrics when using sensor technology to assess cognitive and technical aspects of hands-on skills.
View details for DOI 10.1002/jso.26519
View details for PubMedID 34245582