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Related Concept Videos

Appendicitis-II: Diagnostic Studies and Management01:29

Appendicitis-II: Diagnostic Studies and Management

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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...
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Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
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Related Experiment Video

Updated: Oct 5, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
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Performance optimization of support vector machine with oppositional grasshopper optimization for acute appendicitis

Jianfu Xia1, Zhifei Wang2, Daqing Yang1

  • 1Department of General Surgery, The Second Affiliated Hospital of Shanghai University (Wenzhou Central Hospital), Wenzhou, Zhejiang, 325000, China.

Computers in Biology and Medicine
|February 1, 2022
PubMed
Summary
This summary is machine-generated.

This study developed an intelligent diagnostic model to differentiate complicated appendicitis from uncomplicated appendicitis. The new model uses routine markers for accurate, noninvasive, and cost-effective clinical decision support.

Keywords:
Appendicitis diagnosisFeature selectionGrasshopper optimization algorithmOpposition-based learningSupport vector machine

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Area of Science:

  • Medical Diagnostics
  • Computational Medicine
  • Surgical Pathology

Background:

  • Preoperative differentiation between complicated and uncomplicated appendicitis presents a clinical challenge.
  • Accurate diagnosis is crucial for appropriate treatment and patient outcomes.
  • Existing diagnostic methods may lack accuracy, speed, or cost-effectiveness.

Purpose of the Study:

  • To develop a novel, intelligent diagnostic rule for distinguishing complicated appendicitis (CAP) from uncomplicated appendicitis (UAP).
  • To create a noninvasive, fast, and cost-effective tool to aid clinical decision-making.

Main Methods:

  • Retrospective review of demographic, clinical, and laboratory data from 298 acute appendicitis patients.
  • Identification of key discriminating variables (CRP, heart rate, body temperature, neutrophils) using random forest analysis.
  • Construction of a diagnostic model using a grasshopper optimization algorithm-based support vector machine.

Main Results:

  • The optimal intelligent model achieved an average accuracy of 83.56%.
  • The model demonstrated a sensitivity of 81.71% and specificity of 85.33%.
  • A Matthews correlation coefficient of 0.6732 indicated good discriminatory power.

Conclusions:

  • The proposed intelligent diagnosis model, based on routinely available markers, is highly reliable.
  • This model shows potential as a valuable tool to assist clinicians in diagnosing appendicitis accurately.
  • Further validation may support its integration into routine clinical practice for appendicitis management.