aceAutomatic Detection and Counting of Surgical Instruments in Surgery Using AI-Based Computer Vision Model
IntroductionCounting surgical instruments during surgical procedures is critical for ensuring patient safety. Our goal is to develop a machine learning-based computer vision algorithm to create a reliable, automated surgical instrument counting support system. We hypothesize that our machine learning algorithm, based on the YOLO (You Only Look Once) model, can effectively perform instrument counting in surgery videos, providing real-time feedback to the surgical team.MethodsYOLO is a real-time object detection a…
A YOLO-based model detected and counted surgical instruments in simulated open surgery video, reaching a 72.7% success rate in validation and larger instruments were classified more reliably.
The model classified small instruments such as small hemostats and needles less successfully than large ones, and it was tested only in simulation, which limits confidence for real counting safeguards.
The system was evaluated only in a simulated open surgical environment within a predefined region of interest, and it performed worse on small instruments, so real operating room performance remains unvalidated.
Evidence
- Peer-reviewedSurgical Innovation2026-10-20
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Truvace Impact Record TRV-2026-2109, v1: “Automatic Detection and Counting of Surgical Instruments in Surgery Using AI-Based Computer Vision Model.” Truvace, 2026-10-21. /record/TRV-2026-2109 (accessed at citation time). sha256 f77480c1328b970a…
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