ArticlePediatric surgery international2023
A simple machine learning approach for preoperative diagnosis of esophageal burns after caustic substance ingestion in children.
Article in Pediatric surgery international, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed, 1 citations in OpenAlex.
- Inflammatory biomarkers and symptom presence in pediatric corrosive substance ingestion: a retrospective study.BMC pediatrics · 2026Article
- AI in pediatric surgery: a narrative review.Translational pediatrics · 2026Review
- From data to decisions: Predicting inpatient burn mortality with advanced classification models.PloS one · 2026Article
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Authors and funding
14 authors at 7 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThe unresolved debate about the management of corrosive ingestion is a major problem both for the patients and healthcare systems. This study aims to demonstrate the presence and the severity of the esophageal burn after caustic substance ingestion can be predicted with complete blood count parameters.
methodsA multicenter, national, retrospective cohort study was performed on all caustic substance cases between 2000 and 2018. The classification learner toolbox of MATLAB version R2021a was used for the classification problem. Machine learning algorithms were used to forecast caustic burn.
resultsAmong 1839 patients, 142 patients (7.7%) had burns. The type of the caustic and the PDW (platelet distribution width) values were the most important predictors. In the acid group, the AUC (area under curve) value was 84% while it was 70% in the alkaline group. The external validation had 85.17% accuracy in the acidic group and 91.66% in the alkaline group.
conclusionsArtificial intelligence systems have a high potential to be used in the prediction of caustic burns in pediatric age groups.
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