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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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The needle study: Machine learning as a new method for case-finding in celiac disease.

Chiara Maria Trovato¹, Monica Montuori², Maria Ludovica Costanzo³

¹Gastroenterology and Nutrition Unit, Bambino Gesù Children Hospital, IRCCS, Rome, Italy.

Journal of Pediatric Gastroenterology and Nutrition

|April 27, 2026

Summary

Machine learning models can identify children with celiac disease (CeD) using uncommon symptoms. This approach aids in developing better screening strategies for early CeD detection.

Keywords:

artificial intelligence children diagnosis uncommon feature

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Last Updated: Apr 28, 2026

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Published on: August 16, 2020

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Area of Science:

Pediatric Gastroenterology
Medical Informatics
Computational Biology

Background:

Celiac disease (CeD) diagnosis can be challenging in children with nonspecific symptoms.
Machine learning (ML) offers potential for improving diagnostic accuracy using clinical data.

Purpose of the Study:

To develop and evaluate ML prediction models for identifying children requiring CeD screening.
To identify uncommon clinical features indicative of CeD in pediatric patients.

Main Methods:

Utilized a discovery cohort of children with CeD and matched controls.
Collected demographic, symptom, laboratory, and family history data, excluding specific antibody levels.
Applied various supervised ML models, including Ridge Classifier and LASSO, with 10-fold cross-validation.

Main Results:

A Ridge Classifier model achieved an area under the ROC curve of 0.763.
The LASSO model identified 40 predictive features, including muscle pain, reflux-like symptoms, and fatigue.
Performance metrics included F1-score of 0.662, sensitivity of 0.652, and specificity of 0.689.

Conclusions:

ML models accurately identified CeD in children with nonspecific clinical features.
The identified 40 features can enhance case-finding strategies for improved CeD detection.