tetano
Editor, Senior Moderator
Ann Med
. 2025 Dec;57(1):2598914.
doi: 10.1080/07853890.2025.2598914. Epub 2025 Dec 12. AI-driven prediction of severe respiratory sequelae in COVID-19 patients
Yao Li[SUP] 1 [/SUP], Lihua Liao[SUP] 1 [/SUP], Xianyue Hu[SUP] 1 [/SUP], Lufang Huang[SUP] 1 [/SUP], Lin Zhang[SUP] 1 [/SUP]
Affiliations
Background: In coronavirus disease 2019 (COVID-19) patients, respiratory sequelae are common complications that significantly impact health outcomes. Hence, early identification of patients at risk is essential for improving prognosis and care.
Materials and methods: We enrolled 516 COVID-19 patients and applied K-means algorithms to cluster them into subtypes based on clinical characteristics and risk profiles. ResNet-50 was employed to analyze and extract features from chest X-rays, accurately identifying COVID-19-related lesions. The extracted imaging data were integrated with clinical data to develop a predictive model aimed at stratifying post-COVID-19 patients by risk and identifying those likely to develop severe respiratory sequelae.
Results: We identified two distinct COVID-19 subtypes, one of which was associated with severe respiratory sequelae. The convolutional neural networks (CNNs) accurately detected COVID-19-related lesions on chest X-rays. The predictive model showed excellent subtype discriminative ability, achieving an area under the curve (AUC) of 0.949 and 0.958 in the training and validation cohorts, respectively.
Conclusions: Our AI-driven predictive model demonstrates strong potential for the early identification of respiratory sequelae in COVID-19 patients. By applying the K-means algorithm to cluster patients based on clinical characteristics, in combination with feature extraction from chest X-rays using the ResNet-50 deep learning model, we accurately stratified patients by their risk of severe respiratory outcomes. However, to evaluate its performance in clinical settings, further validation using larger, independent datasets is essential to confirm the model's reliability and generalizability across diverse populations.
Keywords: COVID-19; convolutional neural networks; precision medicine; respiratory sequelae; unsupervised machine learning.
. 2025 Dec;57(1):2598914.
doi: 10.1080/07853890.2025.2598914. Epub 2025 Dec 12. AI-driven prediction of severe respiratory sequelae in COVID-19 patients
Yao Li[SUP] 1 [/SUP], Lihua Liao[SUP] 1 [/SUP], Xianyue Hu[SUP] 1 [/SUP], Lufang Huang[SUP] 1 [/SUP], Lin Zhang[SUP] 1 [/SUP]
Affiliations
- PMID: 41383156
- DOI: 10.1080/07853890.2025.2598914
Background: In coronavirus disease 2019 (COVID-19) patients, respiratory sequelae are common complications that significantly impact health outcomes. Hence, early identification of patients at risk is essential for improving prognosis and care.
Materials and methods: We enrolled 516 COVID-19 patients and applied K-means algorithms to cluster them into subtypes based on clinical characteristics and risk profiles. ResNet-50 was employed to analyze and extract features from chest X-rays, accurately identifying COVID-19-related lesions. The extracted imaging data were integrated with clinical data to develop a predictive model aimed at stratifying post-COVID-19 patients by risk and identifying those likely to develop severe respiratory sequelae.
Results: We identified two distinct COVID-19 subtypes, one of which was associated with severe respiratory sequelae. The convolutional neural networks (CNNs) accurately detected COVID-19-related lesions on chest X-rays. The predictive model showed excellent subtype discriminative ability, achieving an area under the curve (AUC) of 0.949 and 0.958 in the training and validation cohorts, respectively.
Conclusions: Our AI-driven predictive model demonstrates strong potential for the early identification of respiratory sequelae in COVID-19 patients. By applying the K-means algorithm to cluster patients based on clinical characteristics, in combination with feature extraction from chest X-rays using the ResNet-50 deep learning model, we accurately stratified patients by their risk of severe respiratory outcomes. However, to evaluate its performance in clinical settings, further validation using larger, independent datasets is essential to confirm the model's reliability and generalizability across diverse populations.
Keywords: COVID-19; convolutional neural networks; precision medicine; respiratory sequelae; unsupervised machine learning.