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Related Concept Videos

Appendicitis-I: Introduction01:22

Appendicitis-I: Introduction

259
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...
259
Appendicitis-II: Diagnostic Studies and Management01:29

Appendicitis-II: Diagnostic Studies and Management

100
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...
100

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Updated: Jul 23, 2025

Murine Appendectomy Model of Chronic Colitis Associated Colorectal Cancer by Precise Localization of Caecal Patch
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Artificial intelligence for predicting acute appendicitis: a systematic review.

Antoinette Lam1, Emily Squires2, Sheryn Tan1

  • 1University of Adelaide, Adelaide, South Australia, Australia.

ANZ Journal of Surgery
|July 17, 2023
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) shows promise in diagnosing and predicting outcomes for pediatric appendicitis. However, further research is needed to implement AI models effectively in clinical settings for improved patient care.

Keywords:
artificial neural networkmachine learningpredictive analyticssurgery

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Area of Science:

  • Pediatric surgery
  • Medical informatics
  • Artificial intelligence in medicine

Background:

  • Pediatric appendicitis diagnosis and outcome prediction present significant challenges.
  • Existing diagnostic and prognostic scores have limitations.
  • Artificial intelligence (AI) offers potential solutions for improving diagnostic accuracy and prognostic capabilities.

Approach:

  • A systematic review evaluated current evidence on AI for pediatric appendicitis diagnosis and prognostication.
  • Searches were conducted across PubMed, EMBASE, and Cochrane Library databases, adhering to PRISMA guidelines.
  • The review was prospectively registered on PROSPERO.

Key Points:

  • Ten studies were included, focusing on AI model derivation and validation, with no implementation evaluations.
  • Input parameters commonly included demographic, clinical, laboratory, and imaging data.
  • AI algorithms like random forests and neural networks demonstrated high performance in diagnosing appendicitis and its subtypes, though clinical usability measures were often lacking.

Conclusions:

  • AI techniques are increasingly explored for developing prediction models for appendicitis diagnosis and classification.
  • Further implementation studies are crucial to demonstrate AI's benefits in system and patient-centered outcomes.
  • Advancing AI models to clinical usability requires robust validation and outcome-focused research.