Machine learning-based prediction of acute and complicated appendicitis using readily available data in low-resource settings

Machine learning models based on CBC and CRP show preliminary potential for predicting appendicitis, but given the surgical-only cohort and modest performance, further validation is needed before clinical use, particularly in low-resource settings.

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Publication Details

Journal
PLoS ONE
Published
2026-02-03
DOI
https://doi.org/10.1371/journal.pone.0339299
Primary Topic
Appendicitis Diagnosis and Management
Type
article
Field-Weighted Citation Impact
0.00
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article

Machine learning-based prediction of acute and complicated appendicitis using readily available data in low-resource settings

Mehdi Amirfazli, Mohammad Hossein Mahmoudi, Arash Dehghan, Arya Derakhshesh et al.
PLoS ONE
Appendicitis Diagnosis and Management
article

Machine learning-based prediction of acute and complicated appendicitis using readily available data in low-resource settings

Mehdi Amirfazli, Mohammad Hossein Mahmoudi, Arash Dehghan, Arya Derakhshesh, Kimya Bahamin, Mohammad Ali Takhshid, Omid Kohandel Gargari
article en

Abstract

Machine learning models based on CBC and CRP show preliminary potential for predicting appendicitis, but given the surgical-only cohort and modest performance, further validation is needed before clinical use, particularly in low-resource settings.

PLoS ONEVol. 21(2)
Hamedan University of Medical Sciences (IR), Sharif University of Technology (IR), Shiraz University of Medical Sciences (IR), Jahrom University of Medical Sciences (IR), Alborz University of Medical Sciences (IR), Illinois State University (US)
Openalex Percentile: Top 69%
Appendicitis Diagnosis and Management
DOIOpen Access PDFOpenAlex
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