Performance Analysis of AI-Assisted Chest Radiography for COVID-19 Pneumonia Diagnosis in Resource-Limited Settings
Year: 2024
Type: article
Abstract: Introduction: Chest radiography (CXR) is commonly used for diagnosinglung and cardiothoracic disorders, including coronavirus disease(COVID-19) pneumonia. However, its diagnostic accuracy during theearly COVID-19 stages was limited. Artificial intelligence (AI) can enhanceCXR analysis and diagnostic accuracy.Objective: To evaluate AI in X-ray diagnostics for COVID-19 patientsin Kyrgyzstan.Methods: Three radiologists reviewed CXR reports of pneumonia patientsand healthy individuals. An AI system with the MedVit deep learningmodel identified COVID-19 pneumonia, and its reports were comparedto radiologists’ interpretations to evaluate diagnostic accuracy.Results: AI’s performance in detecting pneumonia matched that ofradiologists, with 88.31% sensitivity and 96.67% specificity. HighYouden index values indicated quality. AI can enhance X-ray accuracy,especially in resource-limited settings, though challenges like dataquality, standardization, and ethics must be addressed for widespreadadoption.Conclusion: Collaboration between radiologists and AI can enhanceradiological reports for patients with COVID-19 pneumonia, particularlyin rural areas with staff shortages. How to cite this article:Emilov B, Sorokin A, Chubakov T, BaitelievaA, Salibaev O, Chubakov, Zhumabekova A.Performance Analysis of AI-Assisted ChestRadiography for COVID-19 PneumoniaDiagnosis in Resource-Limited Settings. JCommun Dis. 2024;56(4):146-152. DOI: https://doi.org/10.24321/0019.5138.202485
Source: JOURNAL OF COMMUNICABLE DISEASES
Author: Altynai Zhumabekova
Language: English
Topic: COVID-19 diagnosis using AI
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W6907729967
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