ArticlePatient safety in surgery2024
Artificial intelligence model for automated surgical instrument detection and counting: an experimental proof-of-concept study.
Article in Patient safety in surgery, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Technology Review: The Hidden Influence of AI in Orthopaedic Surgery.Journal of the American Academy of Orthopaedic Surgeons. Global research & reviews · 2026Review
- Artificial intelligence in urology training Enhancing annotation, feedback, and evaluation in robotic, laparoscopic, and endoscopic surgery.Canadian Urological Association journal = Journal de l'Association des urologues du Canada · 2026Review
- Optimizing AI implementation for surgery: recommendations for infrastructure and deployment in the operating room.NPJ digital medicine · 2026Article
- Quantifying the U.S. surgical instrument fleet and its reprocessing burden: implications for cost and sustainability in healthcare.BMC health services research · 2026Article
- Inadvertent guidewire retention after radial artery cannulation: report of two cases and safety lessons for vascular access practice.Patient safety in surgery · 2026Article
- The low data fidelity of human-based informatics in surgical instrument waste reduction: is it time for objective computer vision integration?BMC health services research · 2026Observational
- Artificial intelligence-based waste detection in robotic surgery: a pilot validation study of computer vision technology.Journal of robotic surgery · 2026Review
- Optimized Nursing Management for Ophthalmic Surgical Instruments: A Retrospective Study.Risk management and healthcare policy · 2026Article
- Deep learning-based object detection of restorative dental instruments with potential implications for workflow automation and infection control in dental supply units.Scientific reports · 2025Article
- Artificial intelligence and machine learning approaches for patient safety in complex surgery: a review.Patient safety in surgery · 2025Review
- Smart technologies and digital innovations for improving perioperative patient safety: a review.Patient safety in surgery · 2025Review
- A robust dataset for surgical instrument detection to aid autonomous robotic surgery.Data in brief · 2025Article
- Balancing Ethics and Innovation: Can Artificial Intelligence Safely Transform Emergency Surgery? A Narrative Perspective.Journal of clinical medicine · 2025Article
- Use of Multimodal Artificial Intelligence in Surgical Instrument Recognition.Bioengineering (Basel, Switzerland) · 2025Article
- Machine learning approaches for improvement of patient safety in surgery.Patient safety in surgery · 2024Article
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10 authors.
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Abstract
backgroundRetained surgical items (RSI) are preventable events that pose a significant risk to patient safety. Current strategies for preventing RSIs rely heavily on manual instrument counting methods, which are prone to human error. This study evaluates the feasibility and performance of a deep learning-based computer vision model for automated surgical tool detection and counting.
methodsA novel dataset of 1,004 images containing 13,213 surgical tools across 11 categories was developed. The dataset was split into training, validation, and test sets at a 60:20:20 ratio. An artificial intelligence (AI) model was trained on the dataset, and the model's performance was evaluated using standard object detection metrics, including precision and recall. To simulate a real-world surgical setting, model performance was also evaluated in a dynamic surgical video of instruments being moved in real-time.
resultsThe model demonstrated high precision (98.5%) and recall (99.9%) in distinguishing surgical tools from the background. It also exhibited excellent performance in differentiating between various surgical tools, with precision ranging from 94.0 to 100% and recall ranging from 97.1 to 100% across 11 tool categories. The model maintained strong performance on a subset of test images containing overlapping tools (precision range: 89.6-100%, and recall range 97.2-98.2%). In a real-time surgical video analysis, the model maintained a correct surgical tool count in all non-transition frames, with a median inference speed of 40.4 frames per second (interquartile range: 4.9).
conclusionThis study demonstrates that using a deep learning-based computer vision model for automated surgical tool detection and counting is feasible. The model's high precision and real-time inference capabilities highlight its potential to serve as an AI safeguard to potentially improve patient safety and reduce manual burden on surgical staff. Further validation in clinical settings is warranted.
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