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. 2025 Mar 31;19(1):131.
doi: 10.1007/s11701-025-02284-7.

Optimizing intraoperative AI: evaluation of YOLOv8 for real-time recognition of robotic and laparoscopic instruments

Sébastien Frey #  1   2   3   4 , Federica Facente #  5   6   7 , Wen Wei  7 , Ezem Sura Ekmekci  6 , Eric Séjor  8   7 , Patrick Baqué  5   8 , Matthieu Durand  5   9   10 , Hervé Delingette  6 , François Bremond  11 , Pierre Berthet-Rayne  7 , Nicholas Ayache  6
Affiliations

Optimizing intraoperative AI: evaluation of YOLOv8 for real-time recognition of robotic and laparoscopic instruments

Sébastien Frey et al. J Robot Surg. .

Abstract

The accurate recognition of surgical instruments is essential for the advancement of intraoperative artificial intelligence (AI) systems. In this study, we assessed the YOLOv8 model's efficacy in identifying robotic and laparoscopic instruments in robot-assisted abdominal surgeries. Specifically, we evaluated its ability to detect, classify, and segment seven different types of surgical instruments. A diverse dataset was compiled from four public and private sources, encompassing over 7,400 frames and 17,175 annotations that represent a variety of surgical contexts and instruments. YOLOv8 was trained and tested on these datasets, achieving a mean average precision of 0.77 for binary detection and 0.72 for multi-instrument classification. Optimal performance was observed when the training set of a specific instrument reached 1300 instances. The model also demonstrated excellent segmentation accuracy, achieving a mean Dice score of 0.91 and a mean intersection over union of 0.86, with Monopolar Curved Scissors yielding the highest accuracy. Notably, YOLOv8 exhibited superior recognition performance for robotic instruments compared to laparoscopic tools, a difference likely attributed to the greater representation of robotic instruments in the training set. Furthermore, the model's rapid inference speed of 1.12 milliseconds per frame highlights its suitability for real-time clinical applications. These findings confirm YOLOv8's potential for precise and efficient recognition of surgical instruments using a comprehensive multi-source dataset.

Keywords: Computer vision; Computer-assisted surgery; Instrument segmentation; Robotic surgery; Surgical instrument detection; YoloV8.

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Conflict of interest statement

Declarations. Conflict of interest: The authors declare no competing interests. Ethical approval: This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of University Hospital of Nice. Consent to participation: Informed consent was obtained from all individual participants included in the study.

References

    1. Fairag M, Almahdi RH, Siddiqi AA, Alharthi FK, Alqurashi BS, Alzahrani NG, Alsulami A, Alshehri R (2024) Robotic revolution in surgery: diverse applications across specialties and future prospects review article. Cureus 16(1):e52148. https://doi.org/10.7759/cureus.52148 - DOI - PubMed - PMC
    1. Sejor E, Berthet-Rayne P, Frey S (2022) Calling on the next generation of surgeons. Surg Innov 30:15533506221124500. https://doi.org/10.1177/15533506221124501 - DOI
    1. Rodler S, Ganjavi C, De Backer P, Magoulianitis V, Ramacciotti LS, De Castro Abreu AL, Gill IS, Cacciamani GE (2024) Generative artificial intelligence in surgery. Surgery 175(6):1496–1502. https://doi.org/10.1016/j.surg.2024.02.019 - DOI - PubMed
    1. Gumbs AA, Grasso V, Bourdel N, Croner R, Spolverato G, Frigerio I, Illanes A, Abu Hilal M, Park A, Elyan E (2022) The advances in computer vision that are enabling more autonomous actions in surgery: a systematic review of the literature. Sensors (Basel) 22(13):4918. https://doi.org/10.3390/s22134918 - DOI - PubMed
    1. Mascagni P, Vardazaryan A, Alapatt D, Urade T, Emre T, Fiorillo C, Pessaux P, Mutter D, Marescaux J, Costamagna G, Dallemagne B, Padoy N (2022) Artificial intelligence for surgical safety: automatic assessment of the critical view of safety in laparoscopic cholecystectomy using deep learning. Ann Surg 275(5):955–961. https://doi.org/10.1097/SLA.0000000000004351 - DOI - PubMed

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