Designing the CORI score for COVID-19 diagnosis in parallel with deep learning-based imaging models

Authors

  • Telly Kamelia Division of Respirology and Critical Care, Department of Internal Medicine, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia; Division of Respirology and Critical Care, Department of Internal Medicine, Cipto Mangunkusumo National General Hospital, Jakarta, Indonesia https://orcid.org/0000-0003-3838-8357
  • Benny Zulkarnaien Department of Radiology, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia; Department of Radiology, Cipto Mangunkusumo National General Hospital, Jakarta, Indonesia https://orcid.org/0000-0001-7005-0810
  • Wita Septiyanti Department of Radiology, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia; Department of Radiology, Cipto Mangunkusumo National General Hospital, Jakarta, Indonesia
  • Rahmi Afifi Department of Radiology, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia; Department of Radiology, Cipto Mangunkusumo National General Hospital, Jakarta, Indonesia
  • Adila Krisnadhi Faculty of Computer Science, Universitas Indonesia, Jakarta, Indonesia
  • Cleopas M. Rumende Division of Respirology and Critical Care, Department of Internal Medicine, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia; Division of Respirology and Critical Care, Department of Internal Medicine, Cipto Mangunkusumo National General Hospital, Jakarta, Indonesia
  • Ari Wibisono Faculty of Computer Science, Universitas Indonesia, Jakarta, Indonesia
  • Gladhi Guarddin Faculty of Computer Science, Universitas Indonesia, Jakarta, Indonesia
  • Dina Chahyati Faculty of Computer Science, Universitas Indonesia, Jakarta, Indonesia
  • Reyhan E. Yunus Department of Radiology, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia; Department of Radiology, Cipto Mangunkusumo National General Hospital, Jakarta, Indonesia https://orcid.org/0000-0001-9046-6303
  • Dhita P. Pratama Faculty of Computer Science, Universitas Indonesia, Jakarta, Indonesia
  • Irda N. Rahmawati Department of Internal Medicine, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia; Department of Internal Medicine, Cipto Mangunkusumo National General Hospital, Jakarta, Indonesia https://orcid.org/0000-0003-4373-9799
  • Dewi Nareswari Department of Internal Medicine, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia; Department of Internal Medicine, Cipto Mangunkusumo National General Hospital, Jakarta, Indonesia
  • Maharani Falerisya Department of Internal Medicine, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia; Department of Internal Medicine, Cipto Mangunkusumo National General Hospital, Jakarta, Indonesia https://orcid.org/0000-0002-5912-7001
  • Raissa Salsabila Department of Internal Medicine, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia; Department of Internal Medicine, Cipto Mangunkusumo National General Hospital, Jakarta, Indonesia https://orcid.org/0009-0001-4620-1540
  • Bagus DI. Baruna Department of Radiology, Bunda Jakarta General Hospital, Jakarta, Indonesia
  • Anggraini Iriani Department of Clinical Pathology, Bunda Jakarta General Hospital, Jakarta, Indonesia
  • Finny Nandipinto Department of Radiology, Bunda Margonda General Hospital, Jakarta, Indonesia
  • Ceva Wicaksono Division of Respirology and Critical Care, Department of Internal Medicine, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia; Division of Respirology and Critical Care, Department of Internal Medicine, Cipto Mangunkusumo National General Hospital, Jakarta, Indonesia
  • Ivan R. Sini IRSI Research and Training Centre, Jakarta, Indonesia

DOI:

https://doi.org/10.52225/narra.v5i2.1606

Keywords:

COVID-19, diagnostic, scoring system, artificial intelligence, X-ray

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.

Downloads

Download data is not yet available.

Downloads

Issue

Section

Original Article

Citations