11 Papers
21 Citations
Bo Liang is an academic researcher from Huazhong University of Science and Technology. The author has contributed to research in topics: Medicine & Retrospective cohort study. The author has an hindex of 6, co-authored 9 publications.
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Papers
Time Course of Lung Changes at Chest CT during Recovery from Coronavirus Disease 2019 (COVID-19).
Feng Pan,Tianhe Ye,Peng Sun,Shan Gui,Bo Liang,Lingli Li,Dandan Zheng,Jiazheng Wang,Richard L. Hesketh,Lian Yang,Chuansheng Zheng +10 more
TL;DR: In patients recovering from coronavirus disease 2019 (without severe respiratory distress during the disease course), lung abnormalities on chest CT scans showed greatest severity approximately 10 days after initial onset of symptoms.
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The pulmonary sequalae in discharged patients with COVID-19: a short-term observational study.
TL;DR: Lung lesions in COVID-19 pneumonia patients can be absorbed completely during short-term follow-up with no sequelae, and two weeks after discharge might be the optimal time point for early radiological estimation.
Chest CT Patterns from Diagnosis to 1 Year of Follow-up in COVID-19.
Feng Pan,Lian Yang,Bo Liang,Tianhe Ye,Lingli Li,Lin Li,Dehan Liu,Jiazheng Wang,Richard L. Hesketh,Chuansheng Zheng +9 more
TL;DR: Lee et al. as discussed by the authors assessed the chest CT manifestations of COVID-19 up to 1 year after symptom onset and found that the residual linear opacities in 25% of participants and multifocal reticular/cystic lesions in 28% of the participants had not resolved after one year.
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A novel deep learning-based quantification of serial chest computed tomography in Coronavirus Disease 2019 (COVID-19).
Feng Pan,Lin Li,Bo Liu,Tianhe Ye,Lingli Li,Dehan Liu,Zezhen Ding,Guangfeng Chen,Bo Liang,Lian Yang,Chuansheng Zheng +10 more
TL;DR: Wang et al. as discussed by the authors explored and compared a novel deep learning-based quantification with the conventional semi-quantitative computed tomography (CT) scoring for the serial chest CT scans of COVID-19.
Glycemic status affects the severity of coronavirus disease 2019 in patients with diabetes mellitus: an observational study of CT radiological manifestations using an artificial intelligence algorithm.
TL;DR: In this article, the authors explored the impact of diabetes mellitus and glycemic control on chest CT manifestations, acquired using an artificial intelligence (AI)-based quantitative evaluation system, and COVID-19 disease severity and investigated the association between CT lesions and clinical outcome.