ArticleScientific reports2020
Convolutional-neural-network-based diagnosis of appendicitis via CT scans in patients with acute abdominal pain presenting in the emergency department.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it, 64 citations in OpenAlex.
- Artificial Intelligence and Acute Appendicitis: A Systematic Review of Diagnostic and Prognostic Models.World journal of emergency surgery : WJES · 2023Pooled it
- Habitat Radiomics and Machine Learning for Preoperative Three-class Classification of Pediatric Acute Appendicitis: A Dual-center Study with External Validation.Journal of imaging informatics in medicine · 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
- Artificial intelligence in acute appendicitis: A comprehensive review of machine learning and deep learning applications.World journal of gastroenterology · 2025Review
- Progress in the application of machine learning in CT diagnosis of acute appendicitis.Abdominal radiology (New York) · 2025Review
- Machine Learning and Feature Selection in Pediatric Appendicitis.Tomography (Ann Arbor, Mich.) · 2025Article
- Diagnostic Accuracy of a Machine Learning-Derived Appendicitis Score in Children: A Multicenter Validation Study.Children (Basel, Switzerland) · 2025Article
- A Systematic Integration of Artificial Intelligence Models in Appendicitis Management: A Comprehensive Review.Diagnostics (Basel, Switzerland) · 2025Review
- Development and validation of automated three-dimensional convolutional neural network model for acute appendicitis diagnosis.Scientific reports · 2025Article
- Accurate diagnosis of acute appendicitis in the emergency department: an artificial intelligence-based approach.Internal and emergency medicine · 2024Article
- MaskAppendix: Backbone-Enriched Mask R-CNN Based on Grad-CAM for Automatic Appendix Segmentation.Diagnostics (Basel, Switzerland) · 2024Article
- Fully Automated Detection of the Appendix Using U-Net Deep Learning Architecture in CT Scans.Journal of clinical medicine · 2024Article
- Elevating healthcare through artificial intelligence: analyzing the abdominal emergencies data set (TR_ABDOMEN_RAD_EMERGENCY) at TEKNOFEST-2022.European radiology · 2024Article
- Combination of clinical information and radiomics models for the differentiation of acute simple appendicitis and non simple appendicitis on CT images.Scientific reports · 2024Article
- Computer image analysis with artificial intelligence: a practical introduction to convolutional neural networks for medical professionals.Postgraduate medical journal · 2023Review
- Enhancing Disease Classification in Abdominal CT Scans through RGB Superposition Methods and 2D Convolutional Neural Networks: A Study of Appendicitis and Diverticulitis.Computational and mathematical methods in medicine · 2023Article
- An Extra Set of Intelligent Eyes: Application of Artificial Intelligence in Imaging of Abdominopelvic Pathologies in Emergency Radiology.Diagnostics (Basel, Switzerland) · 2022Review
Corrections and comments
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Authors and funding
6 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Acute appendicitis is one of the most common causes of abdominal emergencies. We investigated the feasibility of a neural-network-based diagnosis algorithm of appendicitis by using computed tomography (CT) for patients with acute abdominal pain visiting the emergency room (ER). A neural-network-based diagnostic algorithm of appendicitis was developed and validated using CT data from three institutions who visited the ER with abdominal pain and underwent abdominopelvic CT. For input data, 3D isotropic cubes including the appendix were manually extracted and labeled as appendicitis or a normal appendix. A 3D convolutional neural network (CNN) was trained to binary classification on the input. For model development and testing, 8-fold cross validation was conducted for internal validation and an ensemble model was used for external validation. Diagnostic performance was excellent in both the internal and external validation with an accuracy larger than 90%. The CNN-based diagnosis algorithm may be feasible in diagnosing acute appendicitis using the CT data of patients visiting the ER with acute abdominal pain.
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