Performance Analysis of AI-Assisted Chest Radiography for COVID-19 Pneumonia Diagnosis in Resource-Limited Settings

Berik  Emilov; Aleksander  Sorokin; Tilek  Chubakov; Altynai  Baitelieva; Oskon  Salibaev; Tulegen  Chubakov; Altynai  Zhumabekova

Berik Emilov Department of Internal Medicine, Educational-Scientific Medical Center, IK Akhunbaev Kyrgyz State Medical Academy, Bishkek, Kyrgyzstan.
Aleksander Sorokin Department of Physics, Medical Informatics and Biology, Kyrgyz-Russian Slavic University named after BN Yeltsin, Bishkek, Kyrgyzstan
Tilek Chubakov Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, United States.
Altynai Baitelieva Department of Scientific, Innovative and Clinical Work, IK Akhunbaev Kyrgyz State Medical Academy, Bishkek, Kyrgyzstan.
Oskon Salibaev
Tulegen Chubakov Department of Phthisiopulmonology, Kyrgyz State Medical Institute of Post-Graduate Training and Continuous Education named after SB Daniyarov, Bishkek, Kyrgyzstan.
Altynai Zhumabekova Honorary International Faculty, AJ Research Centre, AJ Institute of Medical Sciences and Research Centre, Mangaluru, Karnataka, India.

Keywords: Chest Radiography, COVID-19, Pneumonia, Artificial Intelligence, Radiologists

Abstract

Introduction: Chest radiography (CXR) is commonly used for diagnosing
lung and cardiothoracic disorders, including coronavirus disease
(COVID-19) pneumonia. However, its diagnostic accuracy during the
early COVID-19 stages was limited. Artificial intelligence (AI) can enhance
CXR analysis and diagnostic accuracy.
Objective: To evaluate AI in X-ray diagnostics for COVID-19 patients
in Kyrgyzstan.
Methods: Three radiologists reviewed CXR reports of pneumonia patients
and healthy individuals. An AI system with the MedVit deep learning
model identified COVID-19 pneumonia, and its reports were compared
to radiologists’ interpretations to evaluate diagnostic accuracy.
Results: AI’s performance in detecting pneumonia matched that of
radiologists, with 88.31% sensitivity and 96.67% specificity. High
Youden index values indicated quality. AI can enhance X-ray accuracy,
especially in resource-limited settings, though challenges like data
quality, standardization, and ethics must be addressed for widespread
adoption.
Conclusion: Collaboration between radiologists and AI can enhance
radiological reports for patients with COVID-19 pneumonia, particularly
in rural areas with staff shortages.

How to cite this article:
Emilov B, Sorokin A, Chubakov T, Baitelieva
A, Salibaev O, Chubakov, Zhumabekova A.
Performance Analysis of AI-Assisted Chest
Radiography for COVID-19 Pneumonia
Diagnosis in Resource-Limited Settings. J
Commun Dis. 2024;56(4):146-152.

DOI: https://doi.org/10.24321/0019.5138.202485

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