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Published in: Surgical Endoscopy 6/2024

25-04-2024 | Artificial Intelligence

Implementation of artificial intelligence-based computer vision model in laparoscopic appendectomy: validation, reliability, and clinical correlation

Authors: Danit Dayan, Nadav Dvir, Haneen Agbariya, Eran Nizri

Published in: Surgical Endoscopy | Issue 6/2024

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Abstract

Background

Application of artificial intelligence (AI) in general surgery is evolving. Real-world implementation of an AI-based computer-vision model in laparoscopic appendectomy (LA) is presented. We aimed to evaluate (1) its accuracy in complexity grading and safety adherence, (2) clinical correlation to outcomes.

Methods

A retrospective single-center study of 499 consecutive LA videos, captured and analyzed by ‘Surgical Intelligence Platform,’ Theator Inc. (9/2020–5/2022). Two expert surgeons viewed all videos and manually graded complexity and safety adherence. Automated annotations were compared to surgeons’ assessments. Inter-surgeons’ agreements were measured. Since 7/2021 videos were linked to patients’ admission numbers. Data retrieval from medical records was performed (n = 365). Outcomes were compared between high and low complexity grades.

Results

Low and high complexity grades comprised 74.8 and 25.2% of 499 videos. Surgeons’ agreements were high (76.9–94.4%, kappa 0.77/0.91; p < 0.001) for all annotated complexity grades. Surgeons’ agreements were also high (96.0–99.8%, kappa 0.78/0.87; p < 0.001) for full safety adherence, whereas agreement was moderate in partial safety adherence and none (32.8–58.8%). Inter-surgeons’ agreements were high for complexity grading (kappa 0.86, p < 0.001) and safety adherence (kappa 0.88, p < 0.001). Comparing high to low grade complexity, preoperative clinical features were similar, except larger appendix diameter on imaging (13.4 ± 4.4 vs. 10.5 ± 3.0 mm, p < 0.001). Intraoperative outcomes were significantly higher (p < 0.001), including time to achieve critical view of safety (29.6, IQR 19.1–41.6 vs. 13.7, IQR 8.5–21.1 min), operative duration (45.3, IQR 37.7–65.2 vs. 25.0, IQR 18.3–32.7 min), and intraoperative events (39.4% vs. 5.9%). Postoperative outcomes (7.4% vs. 9.2%) including surgical complications, mortality, and readmissions were comparable (p = 0.6), except length of stay (4, IQR 2–5.5 vs. 1, IQR 1-2 days; p < 0.001).

Conclusion

The model accurately assesses complexity grading and full safety achievement. It can serve to predict operative time and intraoperative course, whereas no clinical correlation was found regarding postoperative outcomes. Further studies are needed.
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Metadata
Title
Implementation of artificial intelligence-based computer vision model in laparoscopic appendectomy: validation, reliability, and clinical correlation
Authors
Danit Dayan
Nadav Dvir
Haneen Agbariya
Eran Nizri
Publication date
25-04-2024
Publisher
Springer US
Published in
Surgical Endoscopy / Issue 6/2024
Print ISSN: 0930-2794
Electronic ISSN: 1432-2218
DOI
https://doi.org/10.1007/s00464-024-10847-2

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