Canonical text (the exact bytes fingerprinted)

TRUVACE RECORD VERSION
record: TRV-2026-2109
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-21T18:12:39.664212Z
status: published
lens: trace
sector: health
headline: Automatic Detection and Counting of Surgical Instruments in Surgery Using AI-Based Computer Vision Model
dek: 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…
gain_title: 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.
problem_title: 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.
trace_subject: Automated surgical instrument counting with a YOLO computer vision model
gain_reading: 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.
gain_evidence: the model demonstrated a 72.7% success rate | can accurately detect and count surgical instruments within a predefined region of interest
problem_reading: 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.
problem_evidence: the model classified large instruments more successfully than smaller instruments, such as small hemostats and needles
quick_read: Researchers built a real-time object detection system on the YOLO architecture to detect and count surgical instruments in video, with the aim of giving the surgical team live feedback. They trained it on simulated open surgery footage and annotated images, then tested it on a new surgical video.

Instrument counting is tied to patient safety, so automated support could in principle reduce reliance on manual counts. However, the reported success rate is modest, small tools were handled worse, and the system has not been shown to work in actual operating rooms.
limitation: 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.
tag: Dual reading
key_points: The model was built on YOLO, a real-time object detection algorithm based on a convolutional neural network. | Training data included 100 minutes of simulated open surgery video with 600 instrument transfer events. | Images covering 6 instrument types were collected, plus 500 more from online surgeries, and annotated by a human expert. | Validation showed a 72.7% success rate, with weaker performance on small hemostats and needles. | Authors call for better small-instrument detection and validation in real operating rooms.
rundown: The dataset combined 100 minutes of simulated open surgery video, including 600 instrument transfer events, with 500 images of 6 different surgical instruments and another 500 images from online surgeries. A human expert labeled the names and positions of instruments in all images.

Eighty percent of the data was used for training and 20 percent for validation. The trained model was then tested on a new surgical video to count instruments within a predefined region of interest.

The authors conclude the YOLOv11-based model can detect and count instruments in a simulated open surgical environment. They state future work should improve detection of small instruments and validate the system in real operating room environments.
sources:
- peer_reviewed | Surgical Innovation | https://doi.org/10.1177/15533506261501665 | 2026-10-20
prev: 0000000000000000000000000000000000000000000000000000000000000000

sha256

f77480c1328b970a7d05fa9856ee1d0efde3eed3f513ba8b93fd92591b196617

previous

0000000000000000000000000000000000000000000000000000000000000000