ArticleDiagnostics (Basel, Switzerland)2023
Prediction of Perforated and Nonperforated Acute Appendicitis Using Machine Learning-Based Explainable Artificial Intelligence.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 2 of them syntheses that pooled it.
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Who cites it
30 citing papers in PubMed, 2 syntheses or guidelines pooled it, 42 citations in OpenAlex.
- Artificial intelligence for acute appendicitis diagnosis: A systematic review of current evidence, challenges, and future directions.Medicine · 2026Pooled it
- Artificial Intelligence and Acute Appendicitis: A Systematic Review of Diagnostic and Prognostic Models.World journal of emergency surgery : WJES · 2023Pooled it
- Development and validation of an explainable machine learning model for risk stratification in patients with clinically suspected acute pulmonary embolism: retrospective study.Clinics (Sao Paulo, Brazil) · 2026Article
- Diagnosis and classification of thalassemia disease using machine learning: Comparative analysis of traditional models and a novel hybrid approach.Technology and health care : official journal of the European Society for Engineering and Medicine · 2026Article
- A novel CT-based scoring system for appendicoliths and its prognostic value in acute appendicitis: a retrospective cohort study.Emergency radiology · 2026Article
- Building and validating machine learning models to predict appendiceal perforation during conservative treatment of fecalith-associated appendicitis: a 20-algorithm multicenter retrospective analysis.Scientific reports · 2026Article
- Usefulness of Laboratory-Based Machine Learning for Detection and Severity Classification of Acute Appendicitis in a Resource-Limited Healthcare Setting.Diagnostics (Basel, Switzerland) · 2026Article
- Can Artificial Intelligence Revolutionise Surgical Decision-Making for Appendectomy? A Narrative Review.Surgical innovation · 2026Review
- Article
- Artificial intelligence in emergency surgery: a scoping review within the artificial intelligence in emergency and trauma surgery (ARIES) project.World journal of emergency surgery : WJES · 2026Article
- A hybrid machine learning approach to improve the diagnostic accuracy of acute appendicitis.Ulusal travma ve acil cerrahi dergisi = Turkish journal of trauma & emergency surgery : TJTES · 2026Article
- Revolutionizing non-traumatic acute care: a review of the role of artificial intelligence and machine learning in triaging and diagnosis.Acute and critical care · 2026Article
- Machine learning-based prediction of acute and complicated appendicitis using readily available data in low-resource settings.PloS one · 2026Article
- Pro-adrenomedullin: a novel diagnostic biomarker of acute appendicitis.Ulusal travma ve acil cerrahi dergisi = Turkish journal of trauma & emergency surgery : TJTES · 2026Article
- Artificial intelligence in acute appendicitis: A comprehensive review of machine learning and deep learning applications.World journal of gastroenterology · 2025Review
- Machine Learning and Feature Selection in Pediatric Appendicitis.Tomography (Ann Arbor, Mich.) · 2025Article
- Role of immature granulocyte and blood biomarkers in predicting perforated acute appendicitis using machine learning model.World journal of clinical cases · 2025Article
- Evaluating Ensemble-Based Machine Learning Models for Diagnosing Pediatric Acute Appendicitis: Insights from a Retrospective Observational Study.Journal of clinical medicine · 2025Article
- A Systematic Integration of Artificial Intelligence Models in Appendicitis Management: A Comprehensive Review.Diagnostics (Basel, Switzerland) · 2025Review
- Diagnostic value of the MZXBTCH scoring system for acute complex appendicitis.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe primary aim of this study was to create a machine learning (ML) model that can predict perforated and nonperforated acute appendicitis (AAp) with high accuracy and to demonstrate the clinical interpretability of the model with explainable artificial intelligence (XAI).
methodA total of 1797 patients who underwent appendectomy with a preliminary diagnosis of AAp between May 2009 and March 2022 were included in the study. Considering the histopathological examination, the patients were divided into two groups as AAp (
resultsThe CatBoost model could distinguish AAp patients from non-AAp individuals with an accuracy of 88.2% (85.6-90.8%), while distinguishing perforated AAp patients from nonperforated AAp individuals with an accuracy of 92% (89.6-94.5%). According to the results of the SHAP method applied to the CatBoost model, it was observed that high total bilirubin, WBC, Netrophil, WLR, NLR, CRP, and WNR values, and low PNR, PDW, and MCV values increased the prediction of AAp biochemically. On the other hand, high CRP, Age, Total Bilirubin, PLT, RDW, WBC, MCV, WLR, NLR, and Neutrophil values, and low Lymphocyte, PDW, MPV, and PNR values were observed to increase the prediction of perforated AAp.
conclusionFor the first time in the literature, a new approach combining ML and XAI methods was tried to predict AAp and perforated AAp, and both clinical conditions were predicted with high accuracy. This new approach proved successful in showing how well which demographic and biochemical parameters could explain the current clinical situation in predicting AAp and perforated AAp.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.