Generalisability of an adult-trained AI model to paediatric coeliac disease diagnosis: a multicentre external validation study

@article{jaeckle2026generalisability,
  title = {{Generalisability of an adult-trained AI model to paediatric coeliac disease diagnosis: a multicentre external validation study}},
  author = {Florian Jaeckle and Peter M. Gillett and Kathryn J. Kirkwood and Shonali Natu and James Y.H. Chan and Adrian C. Bateman and Mark J. Arends and Elizabeth J. Soilleux},
  journal = {eClinicalMedicine},
  year = {2026},
  month = oct,
  doi = {10.1016/j.eclinm.2026.104246},
  url = {https://linkinghub.elsevier.com/retrieve/pii/S2589537026004992},
  note = {CC BY 4.0},
}

TY  - JOUR
TI  - Generalisability of an adult-trained AI model to paediatric coeliac disease diagnosis: a multicentre external validation study
AU  - Florian Jaeckle
AU  - Peter M. Gillett
AU  - Kathryn J. Kirkwood
AU  - Shonali Natu
AU  - James Y.H. Chan
AU  - Adrian C. Bateman
AU  - Mark J. Arends
AU  - Elizabeth J. Soilleux
PY  - 2026
DA  - 2026/10/12
JO  - eClinicalMedicine
DO  - 10.1016/j.eclinm.2026.104246
UR  - https://linkinghub.elsevier.com/retrieve/pii/S2589537026004992
AB  - Background  Coeliac disease diagnosis on duodenal biopsies is limited by interobserver variability. We have previously demonstrated pathologist-level performance with our artificial intelligence (AI) model for the histopathological diagnosis of adult coeliac disease, but not in paediatric practice. As paediatric coeliac disease screening programmes expand internationally, accurate and scalable diagnostic tools are needed. We investigated whether an AI model trained exclusively on adult whole-slide images (WSIs) can generalise to paediatric coeliac disease diagnosis across independent centres.Methods  A training and validation dataset of 9958 WSIs from 8421 adult patients (961 coeliac disease; collected Jan 1, 2014, to March 31, 2025) from five centres was used to develop an ensemble of multiple-instance learning models using features from a foundation model. Testing was performed on 708 consecutive paediatric patients (86 with coeliac disease) from two centres (Edinburgh and Southampton) not included in training. Model calibration was assessed, and probability outputs were grouped into clinically interpretable categories.Findings  In adult cross-validation, the AI model had an area under the receiver operating characteristic curve (AUC) of 99.1% (95% CI 98.9-99.4), sensitivity of 89.3% (87.2-91.1), specificity of 98.8% (98.5-99.0), and negative predictive value (NPV) of 98.6% (98.3-98.9). On testing (paediatric) datasets, performance remained high (AUC 98.8%, 95% CI 98.2-99.4; sensitivity 80.2%, 70.6-87.3; specificity 98.4%, 97.1-99.1; NPV 97.3%, 95.7-98.3). Restricting analysis to predictions outside the intermediate-probability range (predicted coeliac disease probability <10% or ≥65%; 604 [85.3%] of 708 cases) improved sensitivity to 100% (95% CI 93.7-100.0) and specificity to 98.7% (97.4-99.4). No misclassifications were observed among high-confidence predictions (<2% or ≥85%; 467 [66.0%] of 708 cases). The expected calibration error was 0.03. Performance improved significantly when biopsies from both duodenal sites (bulb [D1] and descending [D2/3]) were considered.Interpretation  Our AI model, trained on adult biopsies, generalises to paediatric coeliac disease diagnosis across centres and scanner platforms. Well calibrated probability outputs provide clinically interpretable measures of diagnostic confidence and could support safe identification of coeliac disease-negative biopsies within defined thresholds. These findings demonstrate the feasibility of applying adult-derived AI models in paediatric populations and reinforce the importance of multi-site (D1 & D2) biopsy sampling.Funding  The National Institute for Health and Care Research.
SN  - 2589-5370
ER  -