SynthesisWorld journal of emergency surgery : WJES2023
Artificial Intelligence and Acute Appendicitis: A Systematic Review of Diagnostic and Prognostic Models.
Synthesis in World journal of emergency surgery : WJES, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 5 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
43 citing papers in PubMed, 5 syntheses or guidelines pooled it, 60 citations in OpenAlex.
- Use of Artificial Intelligence in the Interpretation of Electroretinography (ERG) Studies.International journal of molecular sciences · 2026Pooled it
- Artificial intelligence for acute appendicitis diagnosis: A systematic review of current evidence, challenges, and future directions.Medicine · 2026Pooled it
- Outcomes following out-of-hours acute appendectomy: a systematic review and meta-analysis.Langenbeck's archives of surgery · 2026Pooled it
- A meta-analysis of the diagnostic test accuracy of artificial intelligence predicting emergency department dispositions.BMC medical informatics and decision making · 2025Pooled it
- Ultrasound and computed tomography in differentiating between simple and complicated appendicitis in pediatric patients.Pediatric surgery international · 2024Pooled it
- TSNet: a multi-modal deep learning framework for subtyping appendicitis: integrating ultrasound images, handcrafted features, and clinical data.Abdominal radiology (New York) · 2026Article
- AI-based disease severity grading predicts complications in laparoscopic appendectomy.Surgical endoscopy · 2026Article
- Predictors of prolonged hospitalization and inflammation severity in acute appendicitis: a retrospective cohort study.Ulusal travma ve acil cerrahi dergisi = Turkish journal of trauma & emergency surgery : TJTES · 2026Article
- Explainable Machine Learning for Predicting Complicated Appendicitis and Expected Hospital Length of Stay in Children: An Exploratory Single-Center Study.Medicina (Kaunas, Lithuania) · 2026Article
- Neural network approach for predicting outcomes of external cephalic version for breech presentation: a retrospective cohort study.Journal of Zhejiang University. Science. B · 2026Article
- Research on the construction of an artificial intelligence model for the early diagnosis of pediatric acute appendicitis: a single-center retrospective study based on complete blood count.BMC pediatrics · 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
- Artificial intelligence and robotic surgery in emergency gastrointestinal procedures: a review of current evidence and future directions.Journal of robotic surgery · 2026Review
- 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
- 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
- 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
- A Hybrid Model with Quantum Feature Map Based on CNN and Vision Transformer for Clinical Support in Diagnosis of Acute Appendicitis.Biomedicines · 2026Article
- Dharma: A novel, clinically grounded machine learning framework for pediatric appendicitis-Diagnosis, severity assessment and evidence-based clinical decision support.PLOS digital health · 2026Article
- Minimally Invasive Surgical Techniques in the Treatment of Appendicitis: A Narrative Review.Cureus · 2025Review
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTo assess the efficacy of artificial intelligence (AI) models in diagnosing and prognosticating acute appendicitis (AA) in adult patients compared to traditional methods. AA is a common cause of emergency department visits and abdominal surgeries. It is typically diagnosed through clinical assessments, laboratory tests, and imaging studies. However, traditional diagnostic methods can be time-consuming and inaccurate. Machine learning models have shown promise in improving diagnostic accuracy and predicting outcomes. MAIN BODY: A systematic review following the PRISMA guidelines was conducted, searching PubMed, Embase, Scopus, and Web of Science databases. Studies were evaluated for risk of bias using the Prediction Model Risk of Bias Assessment Tool. Data points extracted included model type, input features, validation strategies, and key performance metrics.
resultsIn total, 29 studies were analyzed, out of which 21 focused on diagnosis, seven on prognosis, and one on both. Artificial neural networks (ANNs) were the most commonly employed algorithm for diagnosis. Both ANN and logistic regression were also widely used for categorizing types of AA. ANNs showed high performance in most cases, with accuracy rates often exceeding 80% and AUC values peaking at 0.985. The models also demonstrated promising results in predicting postoperative outcomes such as sepsis risk and ICU admission. Risk of bias was identified in a majority of studies, with selection bias and lack of internal validation being the most common issues.
conclusionAI algorithms demonstrate significant promise in diagnosing and prognosticating AA, often surpassing traditional methods and clinical scores such as the Alvarado scoring system in terms of speed and accuracy.
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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.