Park, Doohyun Jang, Ryoungwoo Chung, Myung Jin An, Hyun Joon Bak, Seongwon Choi, Euijoon Hwang, Dosik 2024-01-19T09:01:17Z 2024-01-19T09:01:17Z 2023-10-05 2023-08 2045-2322 https://pubs.kist.re.kr/handle/201004/113378 The Coronavirus Disease 2019 (COVID-19) is transitioning into the endemic phase. Nonetheless, it is crucial to remain mindful that pandemics related to infectious respiratory diseases (IRDs) can emerge unpredictably. Therefore, we aimed to develop and validate a severity assessment model for IRDs, including COVID-19, influenza, and novel influenza, using CT images on a multi-centre data set. Of the 805 COVID-19 patients collected from a single centre, 649 were used for training and 156 were used for internal validation (D1). Additionally, three external validation sets were obtained from 7 cohorts: 1138 patients with COVID-19 (D2), and 233 patients with influenza and novel influenza (D3). A hybrid model, referred to as Hybrid-DDM, was constructed by combining two deep learning models and a machine learning model. Across datasets D1, D2, and D3, the Hybrid-DDM exhibited significantly improved performance compared to the baseline model. The areas under the receiver operating curves (AUCs) were 0.830 versus 0.767 (p=0.036) in D1, 0.801 versus 0.753 (p<0.001) in D2, and 0.774 versus 0.668 (p<0.001) in D3. This study indicates that the Hybrid-DDM model, trained using COVID-19 patient data, is effective and can also be applicable to patients with other types of viral pneumonia. English Nature Publishing Group Development and validation of a hybrid deep learning-machine learning approach for severity assessment of COVID-19 and other pneumonias Article 10.1038/s41598-023-40506-w 1 Scientific Reports, v.13, no.1 Scientific Reports 13 1 Y scie scopus 001064718300053 2-s2.0-85168295838 Multidisciplinary Sciences Science & Technology - Other Topics Article BACTERIAL