{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T20:07:24Z","timestamp":1789502844857,"version":"build-2803163510"},"reference-count":0,"publisher":"Narra Sains Indonesia","issue":"2","license":[{"start":{"date-parts":[[2025,5,5]],"date-time":"2025-05-05T00:00:00Z","timestamp":1746403200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Narra J"],"abstract":"<jats:p>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\u2014including retrospective data collection, inter-hospital variability, and limited external validation\u2014the 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.<\/jats:p>","DOI":"10.52225\/narra.v5i2.1606","type":"journal-article","created":{"date-parts":[[2025,5,6]],"date-time":"2025-05-06T00:39:46Z","timestamp":1746491986000},"page":"e1606","source":"Crossref","is-referenced-by-count":2,"title":["Designing the CORI score for COVID-19 diagnosis in parallel with deep learning-based imaging models"],"prefix":"10.52225","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3838-8357","authenticated-orcid":false,"given":"Telly","family":"Kamelia","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7005-0810","authenticated-orcid":false,"given":"Benny","family":"Zulkarnaien","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wita","family":"Septiyanti","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rahmi","family":"Afifi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adila","family":"Krisnadhi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cleopas M.","family":"Rumende","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ari","family":"Wibisono","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gladhi","family":"Guarddin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dina","family":"Chahyati","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9046-6303","authenticated-orcid":false,"given":"Reyhan E.","family":"Yunus","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dhita P.","family":"Pratama","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4373-9799","authenticated-orcid":false,"given":"Irda N.","family":"Rahmawati","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dewi","family":"Nareswari","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5912-7001","authenticated-orcid":false,"given":"Maharani","family":"Falerisya","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-4620-1540","authenticated-orcid":false,"given":"Raissa","family":"Salsabila","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bagus DI.","family":"Baruna","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anggraini","family":"Iriani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Finny","family":"Nandipinto","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ceva","family":"Wicaksono","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ivan R.","family":"Sini","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"29794","published-online":{"date-parts":[[2025,5,5]]},"container-title":["Narra J"],"original-title":[],"link":[{"URL":"https:\/\/narraj.org\/main\/article\/download\/1606\/865","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/narraj.org\/main\/article\/download\/1606\/865","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,6]],"date-time":"2025-05-06T08:28:26Z","timestamp":1746520106000},"score":1,"resource":{"primary":{"URL":"https:\/\/narraj.org\/main\/article\/view\/1606"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,5]]},"references-count":0,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2025,4,21]]}},"URL":"https:\/\/doi.org\/10.52225\/narra.v5i2.1606","relation":{},"ISSN":["2807-2618"],"issn-type":[{"value":"2807-2618","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,5]]}}}