Different Appearance of Chest CT Images of T2DM and NDM Patients with COVID-19 Pneumonia Based on an Artificial Intelligent Quantitative Method
COVID-19 is a kind of pneumonia with new coronavirus infection, and the risk of death in COVID-19 patients with diabetes is four times higher than that in healthy people. It is unclear whether there is a difference in chest CT images between type 2 diabetes mellitus (T2DM) and non-diabetes mellitus...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | International Journal of Endocrinology |
| Online Access: | http://dx.doi.org/10.1155/2021/6616069 |
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| author | Shan Lu Zhiheng Xing Shiyu Zhao Xianglu Meng Juhong Yang Wenlong Ding Jigang Wang Chencui Huang Jingxu Xu Baocheng Chang Jun Shen |
| author_facet | Shan Lu Zhiheng Xing Shiyu Zhao Xianglu Meng Juhong Yang Wenlong Ding Jigang Wang Chencui Huang Jingxu Xu Baocheng Chang Jun Shen |
| author_sort | Shan Lu |
| collection | DOAJ |
| description | COVID-19 is a kind of pneumonia with new coronavirus infection, and the risk of death in COVID-19 patients with diabetes is four times higher than that in healthy people. It is unclear whether there is a difference in chest CT images between type 2 diabetes mellitus (T2DM) and non-diabetes mellitus (NDM) COVID-19 patients. The aim of this study was to investigate the differences in chest CT images between T2DM and NDM patients with COVID-19 based on a quantitative method of artificial intelligence. A total of 62 patients with COVID-19 pneumonia were retrospectively enrolled and divided into group A (T2DM COVID-19 pneumonia group, n = 15) and group B (NDM COVID-19 pneumonia group, n = 47). The clinical and laboratory examination information of the two groups was collected. Quantitative features (volume of consolidation shadows and ground glass shadows, proportion of consolidation shadow (or ground glass shadow) to lobe volume, total volume, total proportion, and number) of chest spiral CT images were extracted using Dr. Wise @Pneumonia software. The results showed that among the 26 CT image features, the total volume and proportion of bilateral pulmonary consolidation shadow in group A were larger than those in group B (P=0.031 and 0.019, respectively); there was no significant difference in the total volume and proportion of bilateral pulmonary ground glass density shadow between the two groups (P>0.05). In group A, the blood glucose level was correlated with the volume of consolidation shadow and the proportion of consolidation shadow to right middle lobe volume, and higher than those patients in group B. In conclusion, the inflammatory exudation in the lung of COVID-19 patients with diabetes is more serious than that of patients without diabetes based on the quantitative method of artificial intelligence. Moreover, the blood glucose level is positively correlated with pulmonary inflammatory exudation in COVID-19 patients. |
| format | Article |
| id | doaj-art-d835113440574600a28f6e59f4d9fba3 |
| institution | OA Journals |
| issn | 1687-8337 1687-8345 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Endocrinology |
| spelling | doaj-art-d835113440574600a28f6e59f4d9fba32025-08-20T02:21:11ZengWileyInternational Journal of Endocrinology1687-83371687-83452021-01-01202110.1155/2021/66160696616069Different Appearance of Chest CT Images of T2DM and NDM Patients with COVID-19 Pneumonia Based on an Artificial Intelligent Quantitative MethodShan Lu0Zhiheng Xing1Shiyu Zhao2Xianglu Meng3Juhong Yang4Wenlong Ding5Jigang Wang6Chencui Huang7Jingxu Xu8Baocheng Chang9Jun Shen10NHC Key Laboratory of Hormones and Development (Tianjin Medical University), Tianjin Key Laboratory of Metabolic Diseases, Tianjin Medical University Chu Hsien-I Memorial Hospital and Tianjin Institute of Endocrinology, Tianjin, ChinaHaihe Hospital, Tianjin University, Tianjin Institute of Respiratory Diseases, Tianjin, ChinaNHC Key Laboratory of Hormones and Development (Tianjin Medical University), Tianjin Key Laboratory of Metabolic Diseases, Tianjin Medical University Chu Hsien-I Memorial Hospital and Tianjin Institute of Endocrinology, Tianjin, ChinaNHC Key Laboratory of Hormones and Development (Tianjin Medical University), Tianjin Key Laboratory of Metabolic Diseases, Tianjin Medical University Chu Hsien-I Memorial Hospital and Tianjin Institute of Endocrinology, Tianjin, ChinaNHC Key Laboratory of Hormones and Development (Tianjin Medical University), Tianjin Key Laboratory of Metabolic Diseases, Tianjin Medical University Chu Hsien-I Memorial Hospital and Tianjin Institute of Endocrinology, Tianjin, ChinaHaihe Hospital, Tianjin University, Tianjin Institute of Respiratory Diseases, Tianjin, ChinaHaihe Hospital, Tianjin University, Tianjin Institute of Respiratory Diseases, Tianjin, ChinaDepartment of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co. Ltd, Beijing, ChinaDepartment of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co. Ltd, Beijing, ChinaNHC Key Laboratory of Hormones and Development (Tianjin Medical University), Tianjin Key Laboratory of Metabolic Diseases, Tianjin Medical University Chu Hsien-I Memorial Hospital and Tianjin Institute of Endocrinology, Tianjin, ChinaHaihe Hospital, Tianjin University, Tianjin Institute of Respiratory Diseases, Tianjin, ChinaCOVID-19 is a kind of pneumonia with new coronavirus infection, and the risk of death in COVID-19 patients with diabetes is four times higher than that in healthy people. It is unclear whether there is a difference in chest CT images between type 2 diabetes mellitus (T2DM) and non-diabetes mellitus (NDM) COVID-19 patients. The aim of this study was to investigate the differences in chest CT images between T2DM and NDM patients with COVID-19 based on a quantitative method of artificial intelligence. A total of 62 patients with COVID-19 pneumonia were retrospectively enrolled and divided into group A (T2DM COVID-19 pneumonia group, n = 15) and group B (NDM COVID-19 pneumonia group, n = 47). The clinical and laboratory examination information of the two groups was collected. Quantitative features (volume of consolidation shadows and ground glass shadows, proportion of consolidation shadow (or ground glass shadow) to lobe volume, total volume, total proportion, and number) of chest spiral CT images were extracted using Dr. Wise @Pneumonia software. The results showed that among the 26 CT image features, the total volume and proportion of bilateral pulmonary consolidation shadow in group A were larger than those in group B (P=0.031 and 0.019, respectively); there was no significant difference in the total volume and proportion of bilateral pulmonary ground glass density shadow between the two groups (P>0.05). In group A, the blood glucose level was correlated with the volume of consolidation shadow and the proportion of consolidation shadow to right middle lobe volume, and higher than those patients in group B. In conclusion, the inflammatory exudation in the lung of COVID-19 patients with diabetes is more serious than that of patients without diabetes based on the quantitative method of artificial intelligence. Moreover, the blood glucose level is positively correlated with pulmonary inflammatory exudation in COVID-19 patients.http://dx.doi.org/10.1155/2021/6616069 |
| spellingShingle | Shan Lu Zhiheng Xing Shiyu Zhao Xianglu Meng Juhong Yang Wenlong Ding Jigang Wang Chencui Huang Jingxu Xu Baocheng Chang Jun Shen Different Appearance of Chest CT Images of T2DM and NDM Patients with COVID-19 Pneumonia Based on an Artificial Intelligent Quantitative Method International Journal of Endocrinology |
| title | Different Appearance of Chest CT Images of T2DM and NDM Patients with COVID-19 Pneumonia Based on an Artificial Intelligent Quantitative Method |
| title_full | Different Appearance of Chest CT Images of T2DM and NDM Patients with COVID-19 Pneumonia Based on an Artificial Intelligent Quantitative Method |
| title_fullStr | Different Appearance of Chest CT Images of T2DM and NDM Patients with COVID-19 Pneumonia Based on an Artificial Intelligent Quantitative Method |
| title_full_unstemmed | Different Appearance of Chest CT Images of T2DM and NDM Patients with COVID-19 Pneumonia Based on an Artificial Intelligent Quantitative Method |
| title_short | Different Appearance of Chest CT Images of T2DM and NDM Patients with COVID-19 Pneumonia Based on an Artificial Intelligent Quantitative Method |
| title_sort | different appearance of chest ct images of t2dm and ndm patients with covid 19 pneumonia based on an artificial intelligent quantitative method |
| url | http://dx.doi.org/10.1155/2021/6616069 |
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