Journal of pediatric gastroenterology and nutrition · 2026
The needle study: Machine learning as a new method for case-finding in celiac disease.
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Trovato Chiara Maria, Montuori Monica, Costanzo Maria Ludovica, Russo Giusy, Ruggiero Cosimo, Volpe Danila, Ferretti Francesca, Diamanti Antonella, Ciliberto Carlo, Oliva Salvatore
Аннотация
OBJECTIVES: Despite a well-defined diagnostic work-up, uncertainties persist regarding celiac disease (CeD) detection strategies in the general population. Machine learning (ML) algorithms offer promise in aiding medical decision-making on clinical data. This study aimed to utilize ML prediction models to identify uncommon features indicating the necessity for CeD screening in children.
METHODS: The discovery cohort comprised children with CeD exhibiting nonspecific clinical features, drawn from a single referral center, along with gender- and age-matched controls. Data collected included demographic details, symptoms, laboratory tests, and family history of CeD or other autoimmune diseases, excluding anti-transglutaminase immunoglobulin A (IgA) values. Various supervised ML models, utilizing input features to label CeD presence, were applied, with 73 features considered initially.
RESULTS: We collected data from 325 patients with CeD and 490 controls. A comprehensive evaluation using 10-fold cross-validation showed that a Ridge Classifier model achieved the best overall performance, with a mean area under the receiver operating characteristic curve of 0.763 (±0.070), F1-score of 0.662 (±0.067), sensitivity of 0.652 (±0.110), and specificity of 0.689 (±0.081). The least absolute shrinkage and selection operator (LASSO) model identified a stable set of 40 predictive features, with noteworthy features including muscle pain, gastroesophageal reflux disease-like symptoms, fatigue, and specific family histories of autoimmune disease.
CONCLUSIONS: ML models, particularly the LASSO model, exhibited accuracy in identifying CeD in children with nonspecific clinical features. The identification of 40 uncommon features predictive of CeD diagnosis could support a more effective case-finding strategy, potentially enhancing CeD detection.
METHODS: The discovery cohort comprised children with CeD exhibiting nonspecific clinical features, drawn from a single referral center, along with gender- and age-matched controls. Data collected included demographic details, symptoms, laboratory tests, and family history of CeD or other autoimmune diseases, excluding anti-transglutaminase immunoglobulin A (IgA) values. Various supervised ML models, utilizing input features to label CeD presence, were applied, with 73 features considered initially.
RESULTS: We collected data from 325 patients with CeD and 490 controls. A comprehensive evaluation using 10-fold cross-validation showed that a Ridge Classifier model achieved the best overall performance, with a mean area under the receiver operating characteristic curve of 0.763 (±0.070), F1-score of 0.662 (±0.067), sensitivity of 0.652 (±0.110), and specificity of 0.689 (±0.081). The least absolute shrinkage and selection operator (LASSO) model identified a stable set of 40 predictive features, with noteworthy features including muscle pain, gastroesophageal reflux disease-like symptoms, fatigue, and specific family histories of autoimmune disease.
CONCLUSIONS: ML models, particularly the LASSO model, exhibited accuracy in identifying CeD in children with nonspecific clinical features. The identification of 40 uncommon features predictive of CeD diagnosis could support a more effective case-finding strategy, potentially enhancing CeD detection.