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

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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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Data availability

All data of this study are available within the article.

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Acknowledgements

We would like to thank Theator Inc. (Palo Alto, California, USA) for providing the automated annotation data in an organized database.

Funding

No funding was received for this study.

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Authors and Affiliations

  1. Division of General Surgery, Affiliated to Sackler Faculty of Medicine, Tel Aviv Medical Center, Tel Aviv University, 6, Weizman St., 6423906, Tel Aviv-Yafo, Israel

    Danit Dayan, Nadav Dvir, Haneen Agbariya & Eran Nizri

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  1. Danit Dayan
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  2. Nadav Dvir
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  3. Haneen Agbariya
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  4. Eran Nizri
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Contributions

DD: initiated and designed the study; viewed all videos and performed validation and reliability examination; performed data curation and statistical analysis; supervised the study, wrote the manuscript original draft and edited manuscript draft. ND and HA performed clinical data collection. EN viewed all videos and performed validation and reliability examination; performed statistical analysis; and edited manuscript draft.

Corresponding author

Correspondence to Danit Dayan.

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Disclosures

Drs. Danit Dayan, Nadav Dvir, Haneen Agbaryia, and Eran Nizri have no conflicts of interest or financial ties to disclose.

Ethical approval

This study was approved by the Tel-Aviv medical center Institutional Review Board (approval number 0014–22-TLV) and was performed in accordance with the ethical standards of the institutional and /or national research committee and with the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards. A waiver of informed consent was given by the Institutional Review Board because the study is retrospective, and all videos and data were de-identified.

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Dayan, D., Dvir, N., Agbariya, H. et al. Implementation of artificial intelligence-based computer vision model in laparoscopic appendectomy: validation, reliability, and clinical correlation. Surg Endosc 38, 3310–3319 (2024). https://doi.org/10.1007/s00464-024-10847-2

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