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

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.
(© 2025 The Author(s).)

All the authors declare that there are no conflicts of interest. FundingThis research was funded by Direktorat Inovasi & Science Tecno Park (Techno Park Innovation and Science Directory), Universitas Indonesia.Underlying dataDerived data supporting the findings of this study are available from the corresponding author on request.Declaration of artificial intelligence useThis study used artificial intelligence (AI) tools and methodologies in the following capacities. Data analysis and modeling: multiple deep learning architectures were utilized for image classification and prediction tasks, including COVID-Net, Dense-Net, ResNet, Inception-ResNet, DarkCovidNet, CoroNet. Among these, ResNet demonstrated the highest accuracy and was selected for further model integration. These models were implemented using Python and TenserFlow, ensuring robust deep learning capabilities for processing chest X-ray images. Data preprocessing: AI-assisted techniques, such as image augmentation, feature scaling, and word embedding (Word2Vec for text processing), were applied to enhance dataset quality and optimize model training. Visualization: AI tools, including Matplotlib, TensorFlow visualization libraries, and cloud-based web deployment (http://kalamakara-ai.ui.ac.id/), were used for generating model performance graphs, ROC curves, and user-interface displays. We confirm that all AI-assisted processes were critically reviewed by the authors to ensure the integrity and reliability of the results. The final decisions and interpretations presented in this article were solely made by the authors.