Evidence map›Paper›PMID 40825893›Full record

ArticleSurgical endoscopy2025

Using artificial intelligence to model expert panel diagnosis of cholecystitis severity.

Griffin H Olsen, Emmett D Goodman, Josiah G Aklilu, Sebastiano Bartoletti, Kay S Hung, Janice H Yang, Eric C Sorenson, Jeffrey K Jopling, Serena Y Yeung, Dan E Azagury

Abstract read
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In one paragraph

Article in Surgical endoscopy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
–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

1 citing paper in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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

10 authors.

Griffin H OlsenIntermountain Healthcare Delivery Institute, Intermountain Health, Salt Lake City, UT, USA.
Emmett D GoodmanDepartment of Biomedical Data Science, Stanford University, Stanford, CA, USA.
Josiah G AkliluDepartment of Biomedical Data Science, Stanford University, Stanford, CA, USA.
Sebastiano BartolettiDepartment of Surgery, Stanford University School of Medicine, 300 Pasteur Drive, Room H3591, Stanford, CA, 94305-5641, USA.
Kay S HungDepartment of Surgery, Stanford University School of Medicine, 300 Pasteur Drive, Room H3591, Stanford, CA, 94305-5641, USA. kayhung@stanford.edu.ORCID http://orcid.org/0009-0001-0724-0379
Janice H YangDepartment of Biomedical Data Science, Stanford University, Stanford, CA, USA.
Eric C SorensonDepartment of Surgery, Intermountain Medical Center, Murray, UT, USA.
Jeffrey K JoplingDepartment of Surgery, Johns Hopkins School of Medicine, Baltimore, MD, USA.
Serena Y YeungDepartment of Biomedical Data Science, Stanford University, Stanford, CA, USA.
Dan E AzaguryDepartment of Surgery, Stanford University School of Medicine, 300 Pasteur Drive, Room H3591, Stanford, CA, 94305-5641, USA.

Funding

POSTDOCTORAL TRAINING IN MEDICAL INFORMATION SCIENCEST15LM007033 · NLM · STANFORD UNIVERSITY · PI SYLVIA KATINA PLEVRITIS · 1985 to 2026
$25.5M
National Science Foundation 2026498NLM NIH HHS T15 LM007033U.S. National Library of Medicine T15LM007033
6 · The paper itself

Abstract

backgroundDetermining cholecystitis severity via the clinically validated Parkland Grading Scale (PGS) is useful for predicting case difficulty and likelihood of postoperative complications. A panel assessment by multiple surgeons can reduce variation in PGS due to subjectivity, but is time-consuming. An artificial intelligence (AI) model trained on the assessments of an expert clinician panel may improve efficiency and reduce variability in diagnosis in image-based assessments.

methodsLaparoscopic cholecystectomy videos were obtained from one public and two private data sources. Representative frames were chosen for PGS grading and manually labeled. Three surgical experts independently assigned PGS scores to the selected frames. They then convened as a panel to decide on the score if those were discrepant at individual scoring. Weighted Cohen's kappa statistic was measured for inter-rater variability. Two AI models were developed for automated PGS grading and their accuracy and interpretability evaluated.

results319 videos were compiled. Three surgical experts independently assigned identical PGS grades for 51% of cases, and weighted Cohen's kappa statistics ranged between 0.76 and 0.83. The accuracy of Model A using absolute agreement with the expert panel's consensus was 69%, and weighted Cohen's kappa statistic was 0.62. The accuracy of Model B using absolute agreement with the panel's consensus was 72%, and weighted Cohen's kappa statistic was 0.77. Interpretability analysis was conducted. Three anatomical structures played a key role in Model B's grading of cholecystitis severity: the appearance of the gallbladder, liver, and omentum had notable impact on performance.

conclusionsA transformer-based AI model can be trained on consensus from an expert panel to predict ratings of cholecystitis severity (Parking Grading Scale), performing competitively with some individual experts at predicting PGS when compared to the panel-based ground truth. However, variance and subjectivity of PGS remain, thus presenting its limitations as a ground truth for computer vision-based models.

Indexed as

Artificial IntelligenceCholecystectomy, LaparoscopicCholecystitisHumansObserver VariationSeverity of Illness IndexVideo RecordingArtificial intelligenceCholecystitisComputer visionDiagnosis predictionInter-rater variability

Identifiers

PMID40825893
PMCPMC12500764

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

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.