Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Appendicitis-II: Diagnostic Studies and Management01:29

Appendicitis-II: Diagnostic Studies and Management

66
Diagnosing and managing appendicitis requires a structured and comprehensive approach that spans from initial assessment to postoperative care. Here is an overview of the process:
Diagnosing Appendicitis
It requires a multifaceted approach, starting with a detailed physical examination to pinpoint the location and nature of the pain and identify any associated symptoms. Laboratory tests play a crucial role. A complete Blood Count (CBC) typically reveals leukocytosis (an increased number of...
66
Appendicitis-I: Introduction01:22

Appendicitis-I: Introduction

91
The appendix, a small, narrow, blind tube extending from the inferior part of the cecum, is widely regarded as a vestigial organ, having lost much of its original function through evolution. Despite its diminished role, the appendix can become inflamed, a condition known as appendicitis.
Etiology: Appendicitis can arise from various causes, primarily rooted in the obstruction of the appendix lumen. Factors contributing to this obstruction include fecal accumulation, lymphoid hyperplasia and, in...
91

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Comparative clinical outcomes of polymyxin-based versus non-polymyxin regimens as definitive therapy in Carbapenem-resistant Klebsiella pneumoniae bacteraemia.

PloS one·2026
Same author

NoisyFlow: differentially private optimal transport using neural networks for secure biomedical data sharing across multiple institutions.

Bioinformatics (Oxford, England)·2026
Same author

Perceptions and barriers to dietary fiber intake among middle-aged south Indian women: A qualitative study.

PLOS global public health·2026
Same author

Development of a deep learning based framework for classification of Indian venomous snakes integrated with explainable artificial intelligence for primary and emergency care providers.

PLoS neglected tropical diseases·2026
Same author

Triglyceride-glucose-based indices in relation to vitamin D concentrations among adults with metabolic syndrome.

PloS one·2026
Same author

Cluster randomized trial of reablement strategies targeting sarcopenia (ReStart-S) in long-term care settings.

The journals of gerontology. Series A, Biological sciences and medical sciences·2026

Related Experiment Video

Updated: Jun 10, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.2K

An interpretable and transparent machine learning framework for appendicitis detection in pediatric patients.

Krishnaraj Chadaga1, Varada Khanna2, Srikanth Prabhu3

  • 1Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, Karnataka, India.

Scientific Reports
|October 18, 2024
PubMed
Summary

This study introduces an AI model for diagnosing appendicitis in children, achieving 94% accuracy. Key indicators for diagnosis include appendix diameter and white blood cell count.

More Related Videos

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.7K
Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

42.5K

Related Experiment Videos

Last Updated: Jun 10, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.2K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.7K
Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

42.5K

Area of Science:

  • Medical Informatics
  • Pediatric Surgery
  • Artificial Intelligence in Medicine

Background:

  • Appendicitis is a common pediatric condition requiring prompt diagnosis to prevent severe complications like peritonitis and sepsis.
  • Current diagnostic methods involve laboratory tests and imaging, with a need for improved accuracy and speed.
  • Artificial Intelligence (AI) and machine learning offer promising tools for enhancing medical diagnostics.

Purpose of the Study:

  • To develop and evaluate supervised learning models for accurate appendicitis diagnosis in pediatric patients.
  • To compare the performance of various hyperparameter tuning techniques for optimizing AI diagnostic models.
  • To identify key clinical and imaging markers predictive of appendicitis using explainable AI.

Main Methods:

  • Utilized six heterogeneous search techniques (Bayesian Optimization, Hybrid Bat Algorithm, etc.) for hyperparameter tuning.
  • Trained and evaluated nine classification models for appendicitis detection in children.
  • Applied five explainable AI techniques to interpret model predictions and identify significant diagnostic features.

Main Results:

  • The Hybrid Bat Algorithm achieved the highest accuracy (94%) with the customized APPSTACK model.
  • Explainable AI identified critical diagnostic markers including length of stay, ultrasonographic vermiform appendix detection, white blood cell count, and appendix diameter.
  • The developed AI system demonstrates potential for early and validated appendicitis diagnosis.

Conclusions:

  • AI, particularly the Hybrid Bat Algorithm, shows high efficacy in diagnosing pediatric appendicitis.
  • Key features like appendix diameter and WBC count are crucial for AI-driven appendicitis detection.
  • The proposed AI system can aid clinicians in rapid and reliable diagnosis, complementing existing methods.