Evidence map›Paper›PMID 38664295›Full record

ArticleSurgical endoscopy2024

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

Danit Dayan, Nadav Dvir, Haneen Agbariya, Eran Nizri

Abstract readValidation Study
PubMed Publisher
In one paragraph

Article in Surgical endoscopy, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Danit DayanDivision of General Surgery, Affiliated to Sackler Faculty of Medicine, Tel Aviv Medical Center, Tel Aviv University, 6, Weizman St., 6423906, Tel Aviv-Yafo, Israel. danitd.75@gmail.com.ORCID 0000-0003-3065-741X
Nadav DvirDivision of General Surgery, Affiliated to Sackler Faculty of Medicine, Tel Aviv Medical Center, Tel Aviv University, 6, Weizman St., 6423906, Tel Aviv-Yafo, Israel.
Haneen AgbariyaDivision of General Surgery, Affiliated to Sackler Faculty of Medicine, Tel Aviv Medical Center, Tel Aviv University, 6, Weizman St., 6423906, Tel Aviv-Yafo, Israel.
Eran NizriDivision of General Surgery, Affiliated to Sackler Faculty of Medicine, Tel Aviv Medical Center, Tel Aviv University, 6, Weizman St., 6423906, Tel Aviv-Yafo, Israel.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundApplication 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.

methodsA 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.

resultsLow 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).

conclusionThe 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.

Indexed as

AppendectomyArtificial IntelligenceLaparoscopyAdultAppendicitisClinical CompetenceFemaleHumansMaleMiddle AgedReproducibility of ResultsRetrospective StudiesVideo RecordingAppendectomyArtificial intelligenceComplexityComputer visionLaparoscopySafety

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.