Laparoscopic distal gastrectomy skill evaluation from video: a new artificial intelligence-based instrument identification system

The advent of Artificial Intelligence (AI)-based object detection technology has made identification of position coordinates of surgical instruments from videos possible. This study aimed to find kinematic differences by surgical skill level. An AI algorithm was developed to identify X and Y coordin...

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Bibliographic Details
Published in:Scientific Reports Vol. 14; no. 1; pp. 12432 - 8
Main Authors: Matsumoto, Shiro, Kawahira, Hiroshi, Fukata, Kyohei, Doi, Yasunori, Kobayashi, Nao, Hosoya, Yoshinori, Sata, Naohiro
Format: Journal Article
Language:English
Published: London Springer Science and Business Media LLC 30.05.2024
Nature Publishing Group UK
Nature Publishing Group
Nature Portfolio
Subjects:
692/308
692/4020
Algorithms
Artificial Intelligence
Biomechanical Phenomena
Clinical Competence
Female
Gastrectomy
Gastrectomy - methods
Gastrointestinal surgery
Humanities and Social Sciences
Humans
Kinematics
Laparoscopy
Laparoscopy - methods
Male
Medical instruments
Medicine
multidisciplinary
Q
R
ROC Curve
Science
Science (multidisciplinary)
Surgeons
Video Recording - methods
ISSN:2045-2322, 2045-2322
Online Access:Get full text
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Summary:The advent of Artificial Intelligence (AI)-based object detection technology has made identification of position coordinates of surgical instruments from videos possible. This study aimed to find kinematic differences by surgical skill level. An AI algorithm was developed to identify X and Y coordinates of surgical instrument tips accurately from video. Kinematic analysis including fluctuation analysis was performed on 18 laparoscopic distal gastrectomy videos from three expert and three novice surgeons (3 videos/surgeon, 11.6 h, 1,254,010 frames). Analysis showed the expert surgeon cohort moved more efficiently and regularly, with significantly less operation time and total travel distance. Instrument tip movement did not differ in velocity, acceleration, or jerk between skill levels. The evaluation index of fluctuation β was significantly higher in experts. ROC curve cutoff value at 1.4 determined sensitivity and specificity of 77.8% for experts and novices. Despite the small sample, this study suggests AI-based object detection with fluctuation analysis is promising because skill evaluation can be calculated in real time with potential for peri-operational evaluation.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-63388-y