<![CDATA[The coronavirus disease 2019 (COVID-19) pandemic has triggered a global health crisis and placed unprecedented strain on healthcare systems, particularly in resource-limited settings where access to RT-PCR testing is often restricted. Alternative diagnostic strategies are therefore critical. Chest X-rays, when integrated with artificial intelligence (AI), offers a promising approach for COVID-19 detection. The aim of this study was to develop an AI-assisted diagnostic model that combines chest X-ray images and clinical data to generate a COVID-19 Risk Index (CORI) Score and to implement a deep learning model based on ResNet architecture. Between April 2020 and July 2021, a multicenter cohort study was conducted across three hospitals in Jakarta, Indonesia, involving 367 participants categorized into three groups: 100 COVID-19 positive, 100 with non-COVID-19 pneumonia, and 100 healthy individuals. Clinical parameters (e.g., fever, cough, oxygen saturation) and laboratory findings (e.g., D-dimer and C-reactive protein levels) were collected alongside chest X-ray images. Both the CORI Score and the ResNet model were trained using this integrated dataset. During internal validation, the ResNet model achieved 91% accuracy, 94% sensitivity, and 92% specificity. In external validation, it correctly identified 82 of 100 COVID-19 cases. The combined use of imaging, clinical, and laboratory data yielded an area under the ROC curve of 0.98 and a sensitivity exceeding 95%. The CORI Score demonstrated strong diagnostic performance, with 96.6% accuracy, 98% sensitivity, 95.4% specificity, a 99.5% negative predictive value, and a 91.1% positive predictive value. Despite limitations—including retrospective data collection, inter-hospital variability, and limited external validation—the ResNet-based AI model and the CORI Score show substantial promise as diagnostic tools for COVID-19, with performance comparable to that of experienced thoracic radiologists in Indonesia.]]>
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