tetano
Editor, Senior Moderator
J Thorac Dis
. 2020 Oct;12(10):5336-5346.
doi: 10.21037/jtd-20-1584.
CT imaging features of different clinical types of COVID-19 calculated by AI system: a Chinese multicenter study
Xiaofei Hu[SUP] 1 [/SUP], Wenbing Zeng[SUP] 2 3 [/SUP], Yuhan Zhang[SUP] 1 [/SUP], Zhiming Zhen[SUP] 1 [/SUP], Yalan Zheng[SUP] 1 [/SUP], Lin Cheng[SUP] 1 [/SUP], Xianqi Wang[SUP] 1 [/SUP], Haoran Luo[SUP] 1 [/SUP], Shu Zhang[SUP] 4 [/SUP], Zifeng Wu[SUP] 4 [/SUP], Zeyu Sun[SUP] 4 [/SUP], Xiuli Li[SUP] 4 [/SUP], Yang Cao[SUP] 5 [/SUP], Ming Xu[SUP] 6 [/SUP], Jian Wang[SUP] 1 [/SUP], Wei Chen[SUP] 1 [/SUP]
Affiliations
Abstract
Background: The study is designed to explore the chest CT features of different clinical types of coronavirus disease 2019 (COVID-19) pneumonia based on a Chinese multicenter dataset using an artificial intelligence (AI) system.
Methods: A total of 164 patients confirmed COVID-19 were retrospectively enrolled from 6 hospitals. All patients were divided into the mild type (136 cases) and the severe type (28 cases) according to their clinical manifestations. The total CT severity score and quantitative CT features were calculated by AI pneumonia detection and evaluation system with correction by radiologists. The clinical and CT imaging features of different types were analyzed.
Results: It was observed that patients in the severe type group were older than the mild type group. Round lesions, Fan-shaped lesions, crazy-paving pattern, fibrosis, "white lung", pleural thickening, pleural indentation, mediastinal lymphadenectasis were more common in the CT images of severe patients than in the mild ones. A higher total lung severity score and scores of each lobe were observed in the severe group, with higher scores in bilateral lower lobes of both groups. Further analysis showed that the volume and number of pneumonia lesions and consolidation lesions in overall lung were higher in the severe group, and showed a wider distribution in the lower lobes of bilateral lung in both groups.
Conclusions: Chest CT of patients with severe COVID-19 pneumonia showed more consolidative and progressive lesions. With the assistance of AI, CT could evaluate the clinical severity of COVID-19 pneumonia more precisely and help the early diagnosis and surveillance of the patients.
Keywords: Coronavirus infections; artificial intelligence (AI); pneumonia, viral; severity of illness index; tomography, X-ray computed.
. 2020 Oct;12(10):5336-5346.
doi: 10.21037/jtd-20-1584.
CT imaging features of different clinical types of COVID-19 calculated by AI system: a Chinese multicenter study
Xiaofei Hu[SUP] 1 [/SUP], Wenbing Zeng[SUP] 2 3 [/SUP], Yuhan Zhang[SUP] 1 [/SUP], Zhiming Zhen[SUP] 1 [/SUP], Yalan Zheng[SUP] 1 [/SUP], Lin Cheng[SUP] 1 [/SUP], Xianqi Wang[SUP] 1 [/SUP], Haoran Luo[SUP] 1 [/SUP], Shu Zhang[SUP] 4 [/SUP], Zifeng Wu[SUP] 4 [/SUP], Zeyu Sun[SUP] 4 [/SUP], Xiuli Li[SUP] 4 [/SUP], Yang Cao[SUP] 5 [/SUP], Ming Xu[SUP] 6 [/SUP], Jian Wang[SUP] 1 [/SUP], Wei Chen[SUP] 1 [/SUP]
Affiliations
- PMID: 33209367
- PMCID: PMC7656439
- DOI: 10.21037/jtd-20-1584
Abstract
Background: The study is designed to explore the chest CT features of different clinical types of coronavirus disease 2019 (COVID-19) pneumonia based on a Chinese multicenter dataset using an artificial intelligence (AI) system.
Methods: A total of 164 patients confirmed COVID-19 were retrospectively enrolled from 6 hospitals. All patients were divided into the mild type (136 cases) and the severe type (28 cases) according to their clinical manifestations. The total CT severity score and quantitative CT features were calculated by AI pneumonia detection and evaluation system with correction by radiologists. The clinical and CT imaging features of different types were analyzed.
Results: It was observed that patients in the severe type group were older than the mild type group. Round lesions, Fan-shaped lesions, crazy-paving pattern, fibrosis, "white lung", pleural thickening, pleural indentation, mediastinal lymphadenectasis were more common in the CT images of severe patients than in the mild ones. A higher total lung severity score and scores of each lobe were observed in the severe group, with higher scores in bilateral lower lobes of both groups. Further analysis showed that the volume and number of pneumonia lesions and consolidation lesions in overall lung were higher in the severe group, and showed a wider distribution in the lower lobes of bilateral lung in both groups.
Conclusions: Chest CT of patients with severe COVID-19 pneumonia showed more consolidative and progressive lesions. With the assistance of AI, CT could evaluate the clinical severity of COVID-19 pneumonia more precisely and help the early diagnosis and surveillance of the patients.
Keywords: Coronavirus infections; artificial intelligence (AI); pneumonia, viral; severity of illness index; tomography, X-ray computed.