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The role of AI models in enhancing choledocholithiasis diagnosis: A systematic review and meta‑analysis

  • Authors:
    • Panagiotis G. Doukas
    • Sotirios G. Doukas
    • Arkady Broder
  • View Affiliations / Copyright

    Affiliations: Department of Medicine, Rutgers‑Robert Wood Johnson Medical School/Saint Peter's University Hospital, New Brunswick, NJ 08901, USA, Section of Gastroenterology and Hepatology, Department of Medicine, Rutgers‑Robert Wood Johnson Medical School/Saint Peter's University Hospital, New Brunswick, NJ 08901, USA
    Copyright: © Doukas et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
  • Article Number: 221
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    Published online on: September 16, 2025
       https://doi.org/10.3892/etm.2025.12971
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Abstract

Accurate choledocholithiasis (CDL) diagnosis is essential to avoid delayed treatment, prevent complications and reduce unnecessary interventions. Traditional guidelines aid in risk stratification but may lack precision. Notably, artificial intelligence (AI) and machine learning (ML) offer innovative tools that may enhance the accuracy and timeliness of CDL prediction. The present study aimed to systematically evaluate the diagnostic performance of AI‑assisted tools in predicting CDL and to compare it to traditional guideline‑based methods. A comprehensive search was conducted in MEDLINE, EMBASE, PubMed and Web of Science, identifying 578 studies. After screening and application of the inclusion criteria, 11 studies were analyzed. A bivariate random‑effects model was used to pool sensitivity, specificity and positive likelihood ratios (LR+). Summary receiver operating characteristic (SROC) curves were also generated. Meta‑analysis showed an overall high pool sensitivity and specificity of AI‑assisted models: 83.2% [95% confidence interval (CI): 68.9; 91.8] and 91.1% [95% CI: 84.7; 95.0], respectively. The LR+ from the common effect model was 8.39 [95% CI: 7.4; 9.5], suggesting that AI models have a moderately strong ability to predict CDL. AI models demonstrated higher diagnostic performance than traditional American Society for Gastrointestinal Endoscopy guidelines, as evidenced by SROC comparisons. In conclusion, AI‑assisted tools show promise in enhancing CDL diagnosis through high sensitivity and specificity. Innovative AI and ML tools may serve as predictive tools and therapeutic decision‑support systems deserving further clinical validation.
View Figures

Figure 1

PRISMA 2020 flow diagram of study
selection for meta-analysis of AI models in choledocholithiasis
diagnosis. This diagram illustrates of the structured
identification, screening, eligibility assessment, inclusion
process in this review. To ensure transparency, reproducibility of
the literature search and selection methodology, this flow chart
documents the number of records retrieved, excluded at each stage,
and the full articles assessed for eligibility and the final group
of studies included in the quantitative synthesis. AI, artificial
intelligence.

Figure 2

Forest plots displaying (A) pooled
sensitivity estimates and (B) pooled specificity estimates across
included studies, for AI-assisted models in choledocholithiasis
diagnosis. Each square represents an individual study estimate with
95% CI bars, scaled by sample size (total n=7,053). Pooled
estimates were calculated using a bivariate random-effects model.
AI models demonstrated pooled sensitivity 83.2% (95% CI 68.9-91.8)
and specificity 91.1% (95% CI 84.7-95.0). These plots highlight a
consistently strong diagnostic accuracy among diverse machine
learning models. AI, artificial intelligence; CI, confidence
interval.

Figure 3

(A) Forest plot of LR+ for AI-based
diagnostic models across the 11 included studies (>7,000
patients), illustrating their ability to correctly ‘rule in’ CDL
when the test result is positive. The pooled LR⁺ from the
common-effect model was 8.39 (95% CI: 7.41-9.50), indicating that
AI-assisted tools substantially increase the post-test probability
of disease compared to pre-test estimates. (B) Forest plot
summarizing PPV estimates from studies evaluating AI-based
diagnostic tools for CDL. Pooled PPV was 87.7% (95% CI: 77.4-93.7),
demonstrating strong predictive performance in clinical practice.
Cis for individual studies highlight variability related to patient
selection, disease prevalence, and input variable differences, but
overall estimates indicate robust diagnostic accuracy across
diverse populations and model architectures. CDL,
choledocholithiasis; CI, confidence interval; LR+, positive
likelihood ratio; PPV, positive predictive value. AI, artificial
intelligence.

Figure 4

(A) Bivariate random-effects SROC
curve of AI-assisted diagnostic tools derived from 11 studies
involving over 7,000 individuals. The pooled AUC was 0.94 (95% CI:
0.83-0.98), indicating high discriminative performance across
diverse clinical populations. Threshold effect testing showed no
significant heterogeneity (P>0.05). Using Deeks' test, funnel
plot analysis for publication bias did not demonstrate significant
asymmetry (t=1.84, P=0.13). (B) Comparative SROC curves of
AI-assisted models vs. the 2019 ASGE high-risk criteria. AI models
demonstrated significantly higher pooled diagnostic accuracy with
an AUC of 0.91 (97.5% CI: 0.76-0.96) compared to ASGE criteria (AUC
0.76; 97.5% CI: 0.72-0.80). Mixed-effects meta-regression
(generalized linear mixed model, likelihood ratio test P=0.019)
confirmed the superior performance of AI models. Sensitivity and
specificity estimates for AI models were 86.1% (95% CI: 68.5-94.7)
and 93.3% (95% CI: 71.4-98.7), respectively, compared to ASGE
guideline performance of 78.4% (95% CI: 67.5-86.4) and 57.5% (95%
CI: 37.3-75.6). CDL, choledocholithiasis; SROC, summary receiver
operating characteristic; AI, artificial intelligence; AUC, area
under the curve; CI, confidence interval; ASGE, American Society
for Gastrointestinal Endoscopy.

Figure 5

The Quality Assessment of Diagnostic
Accuracy Studies 2 tool was used to evaluate risk of bias and
applicability concerns for the 11 included studies (>7,000
patients). The round-shaped plot (top) summarizes the judgment of
each study across four bias domains, patient selection, index test,
reference standard, and flow/timing, and three applicability
domains, using grey (low risk), and white (some concerns). No areas
of high risk were noted. The summary plot (bottom) depicts the
overall proportion of studies rated at low risk or with some
concerns within each domain. Overall, most studies demonstrated low
risk in the index test and reference standard domains, while
patient selection and flow/timing presented the most frequent
concerns, primarily due to retrospective designs, incomplete
clinical data, or unclear temporal alignment of index and reference
tests. Applicability concerns were minimal for most studies,
reflecting consistent patient populations and appropriate test
applications. These results indicate an overall moderate to high
quality of included studies, supporting the robustness of the
meta-analysis findings.

Figure 6

Proposed diagnostic pathway
incorporating AI-assisted risk stratification for suspected CDL
into ASGE guidelines. Conceptual diagram illustrating the
integration of AI-based predictive tools into clinical workflows
for patients with suspected CDL, particularly among
intermediate-risk and high-risk groups lacking definitive imaging
or clinical confirmation. AI, artificial intelligence; ASGE,
American Society for Gastrointestinal Endoscopy; CDL,
choledocholithiasis; TB, total bilirubin.
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Copy and paste a formatted citation
Spandidos Publications style
Doukas PG, Doukas SG and Broder A: The role of AI models in enhancing choledocholithiasis diagnosis: A systematic review and meta‑analysis. Exp Ther Med 30: 221, 2025.
APA
Doukas, P.G., Doukas, S.G., & Broder, A. (2025). The role of AI models in enhancing choledocholithiasis diagnosis: A systematic review and meta‑analysis. Experimental and Therapeutic Medicine, 30, 221. https://doi.org/10.3892/etm.2025.12971
MLA
Doukas, P. G., Doukas, S. G., Broder, A."The role of AI models in enhancing choledocholithiasis diagnosis: A systematic review and meta‑analysis". Experimental and Therapeutic Medicine 30.6 (2025): 221.
Chicago
Doukas, P. G., Doukas, S. G., Broder, A."The role of AI models in enhancing choledocholithiasis diagnosis: A systematic review and meta‑analysis". Experimental and Therapeutic Medicine 30, no. 6 (2025): 221. https://doi.org/10.3892/etm.2025.12971
Copy and paste a formatted citation
x
Spandidos Publications style
Doukas PG, Doukas SG and Broder A: The role of AI models in enhancing choledocholithiasis diagnosis: A systematic review and meta‑analysis. Exp Ther Med 30: 221, 2025.
APA
Doukas, P.G., Doukas, S.G., & Broder, A. (2025). The role of AI models in enhancing choledocholithiasis diagnosis: A systematic review and meta‑analysis. Experimental and Therapeutic Medicine, 30, 221. https://doi.org/10.3892/etm.2025.12971
MLA
Doukas, P. G., Doukas, S. G., Broder, A."The role of AI models in enhancing choledocholithiasis diagnosis: A systematic review and meta‑analysis". Experimental and Therapeutic Medicine 30.6 (2025): 221.
Chicago
Doukas, P. G., Doukas, S. G., Broder, A."The role of AI models in enhancing choledocholithiasis diagnosis: A systematic review and meta‑analysis". Experimental and Therapeutic Medicine 30, no. 6 (2025): 221. https://doi.org/10.3892/etm.2025.12971
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