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- Title
Development, deployment and scaling of operating room-ready artificial intelligence for real-time surgical decision support.
- Authors
Protserov, Sergey; Hunter, Jaryd; Zhang, Haochi; Mashouri, Pouria; Masino, Caterina; Brudno, Michael; Madani, Amin
- Abstract
Deep learning for computer vision can be leveraged for interpreting surgical scenes and providing surgeons with real-time guidance to avoid complications. However, neither generalizability nor scalability of computer-vision-based surgical guidance systems have been demonstrated, especially to geographic locations that lack hardware and infrastructure necessary for real-time inference. We propose a new equipment-agnostic framework for real-time use in operating suites. Using laparoscopic cholecystectomy and semantic segmentation models for predicting safe/dangerous ("Go"/"No-Go") zones of dissection as an example use case, this study aimed to develop and test the performance of a novel data pipeline linked to a web-platform that enables real-time deployment from any edge device. To test this infrastructure and demonstrate its scalability and generalizability, lightweight U-Net and SegFormer models were trained on annotated frames from a large and diverse multicenter dataset from 136 institutions, and then tested on a separate prospectively collected dataset. A web-platform was created to enable real-time inference on any surgical video stream, and performance was tested on and optimized for a range of network speeds. The U-Net and SegFormer models respectively achieved mean Dice scores of 57% and 60%, precision 45% and 53%, and recall 82% and 75% for predicting the Go zone, and mean Dice scores of 76% and 76%, precision 68% and 68%, and recall 92% and 92% for predicting the No-Go zone. After optimization of the client-server interaction over the network, we deliver a prediction stream of at least 60 fps and with a maximum round-trip delay of 70 ms for speeds above 8 Mbps. Clinical deployment of machine learning models for surgical guidance is feasible and cost-effective using a generalizable, scalable and equipment-agnostic framework that lacks dependency on hardware with high computing performance or ultra-fast internet connection speed.
- Subjects
DECISION support systems; MOBILE apps; WORLD Wide Web; HUMAN services programs; MEDICAL errors; RESEARCH funding; LAPAROSCOPIC surgery; ARTIFICIAL intelligence; EVALUATION of human services programs; CHOLECYSTECTOMY; DESCRIPTIVE statistics; INTERNET; COMPUTER-assisted surgery; SEMANTIC differential scale; LONGITUDINAL method; RESEARCH; LATENT semantic analysis; MATHEMATICAL models; ACCURACY; MACHINE learning; APPLICATION software; THEORY
- Publication
NPJ Digital Medicine, 2024, Vol 7, Issue 1, p1
- ISSN
2398-6352
- Publication type
Article
- DOI
10.1038/s41746-024-01225-2