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All Journal Narra J
Zulkarnaien, Benny
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Biochemistry, Genetics & Molecular Biology Health Professions Immunology & microbiology Medicine & Pharmacology Public Health

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Designing the CORI score for COVID-19 diagnosis in parallel with deep learning-based imaging models Kamelia, Telly; Zulkarnaien, Benny; Septiyanti, Wita; Afifi, Rahmi; Krisnadhi, Adila; Rumende, Cleopas M.; Wibisono, Ari; Guarddin, Gladhi; Chahyati, Dina; Yunus, Reyhan E.; Pratama, Dhita P.; Rahmawati, Irda N.; Nareswari, Dewi; Falerisya, Maharani; Salsabila, Raissa; Baruna, Bagus DI.; Iriani, Anggraini; Nandipinto, Finny; Wicaksono, Ceva; Sini, Ivan R.
Narra J Vol. 5 No. 2 (2025): August 2025
Publisher : Narra Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52225/narra.v5i2.1606

Abstract

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.
Co-Authors Afifi, Rahmi Anggraini Iriani Ari Wibisono Baruna, Bagus DI. Chahyati, Dina Falerisya, Maharani Guarddin, Gladhi Krisnadhi, Adila Nandipinto, Finny Nareswari, Dewi Pratama, Dhita P. Rahmawati, Irda N. Rumende, Cleopas M. Salsabila, Raissa Septiyanti, Wita Sini, Ivan R. Telly Kamelia Wicaksono, Ceva Yunus, Reyhan E.