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Record W4408894938 · doi:10.1055/s-0045-1805444

Artificial Intelligence and Endoscopist Diagnostic Agreement as a Framework for Colorectal Polyp Optical Diagnosis Implementation

2025· article· en· W4408894938 on OpenAlexaff
Megan Oleksiw, Roupen Djinbachian, Daniel von Renteln

Bibliographic record

VenueEndoscopy · 2025
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Screening and Detection
Canadian institutionsCentre Hospitalier de l’Université de Montréal
Fundersnot available
KeywordsMedicineColonoscopyColorectal PolypEndoscopyGeneral surgeryRadiologyColorectal cancerInternal medicine

Abstract

fetched live from OpenAlex

Aims Artificial intelligence (AI) has enabled the development of computer-aided diagnosis (CADx) systems which offer real-time endoscopic pathology prediction of colorectal polyps [ 1 ]. However, the clinical benefit of CADx assisted optical diagnosis remains questionable due to limited diagnostic improvement compared to CADx unassisted optical diagnosis [ 2 ]. This study aimed to assess diagnostic performance of a novel CADx implementation framework in which the high/low endoscopist confidence-based approach was replaced by a CADx and endoscopist agreement-based approach. Methods We performed a secondary analysis of a large prospective cohort undergoing optical polyp diagnosis at our center. Polyps measuring≤5mm (diminutive) with available CADx unassisted and CADx assisted optical polyp diagnosis documentation were included in our analysis. For CADx assisted cases, first the CADx diagnostic output was documented, followed by the endoscopist’s final diagnosis after seeing the CADx output. Only cases where endoscopist and CADx agreed on a diagnosis were retained. Primary outcome was sensitivity for adenoma diagnosis of CADx assisted optical diagnosis based on the agreement framework versus CADx unassisted optical diagnosis, using pathology results as a reference standard. Secondary outcomes included accuracy, diagnostic characteristics, and optical diagnosis surveillance interval agreement with pathology-based United States Multi-Society Task Force (USMSTF) guidelines. Results A total of 810 polyps, of which 444 and 366 underwent CADx assisted and CADx unassisted optical diagnosis respectively, were included in our analysis. In CADx assisted cases, endoscopists and CADx agreed in their diagnosis in 72.3% of cases, meaning 321/444 diminutive polyps could undergo optical diagnosis according to an agreement-based approach. Sensitivity for adenoma diagnosis using agreement-based CADx assisted optical diagnosis was 93.4% (95% CI 89.9-96.8) versus 83.8% (95% CI 77.9-89.7) for the 366 polyps undergoing CADx unassisted optical diagnosis (p=0.008). Diagnostic accuracy of agreement-based CADx assisted and CADx unassisted optical diagnosis was 82.2% (95% CI 78.0-86.3) and 74.8% (95% CI 69.5-80.0) respectively (p=0.026). Excluding cases with CADx and endoscopist disagreement from CADx assisted optical diagnosis filtered out cases with low diagnostic accuracy (123 polyps; accuracy 35.0% (95% CI 26.2-43.8)). Conclusions The CADx and endoscopist agreement framework effectively filters out cases with low diagnostic accuracy from undergoing CADx assisted optical diagnosis. Applying this framework allows endoscopists and CADx to work in synergy resulting in diagnostic performance superior to CADx unassisted OD performance. Publication History Article published online: 27 March 2025 © 2025. European Society of Gastrointestinal Endoscopy. All rights reserved. Georg Thieme Verlag KG Oswald-Hesse-Straße 50, 70469 Stuttgart, Germany

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.021
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0040.003
Open science0.0040.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.028
GPT teacher head0.370
Teacher spread0.343 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations2
Published2025
Admission routes1
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