tetano
Editor, Senior Moderator
Sci Rep
. 2023 Aug 17;13(1):13420.
doi: 10.1038/s41598-023-40506-w. Development and validation of a hybrid deep learning-machine learning approach for severity assessment of COVID-19 and other pneumonias
Doohyun Park[SUP] 1 [/SUP], Ryoungwoo Jang[SUP] 2 [/SUP], Myung Jin Chung[SUP] 3 4 [/SUP], Hyun Joon An[SUP] 2 [/SUP], Seongwon Bak[SUP] 2 [/SUP], Euijoon Choi[SUP] 5 [/SUP], Dosik Hwang[SUP] 6 7 8 9 [/SUP]
Affiliations
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.
. 2023 Aug 17;13(1):13420.
doi: 10.1038/s41598-023-40506-w. Development and validation of a hybrid deep learning-machine learning approach for severity assessment of COVID-19 and other pneumonias
Doohyun Park[SUP] 1 [/SUP], Ryoungwoo Jang[SUP] 2 [/SUP], Myung Jin Chung[SUP] 3 4 [/SUP], Hyun Joon An[SUP] 2 [/SUP], Seongwon Bak[SUP] 2 [/SUP], Euijoon Choi[SUP] 5 [/SUP], Dosik Hwang[SUP] 6 7 8 9 [/SUP]
Affiliations
- PMID: 37591967
- DOI: 10.1038/s41598-023-40506-w
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.