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http://conacyt.repositorioinstitucional.mx/jspui/handle/1000/5328| Artificial Intelligence Distinguishes COVID-19 from Community Acquired Pneumonia on Chest CT | |
| Li Lin. Qin Lixin. Xu Zeguo. Yin Youbing. Wang Xin. Kong Bin. Bai Junjie. Lu Yi. Fang Zhenghan. Song Qi. Cao Kunlin. Liu Daliang. Wang Guisheng. Xu Qizhong. Fang Xisheng. Zhang Shiqin. Xia Juan. Xia Jun. | |
| Acceso Abierto | |
| Atribución-NoComercial-SinDerivadas | |
| 10.1148/radiol.2020200905 | |
| Background Coronavirus disease has widely spread all over the world since the beginning of 2020. It is desirable to develop automatic and accurate detection of COVID-19 using chest CT. Purpose To develop a fully automatic framework to detect COVID-19 using chest CT and evaluate its performances. Materials and Methods In this retrospective and multi-center study, a deep learning model, COVID-19 detection neural network (COVNet), was developed to extract visual features from volumetric chest CT exams for the detection of COVID-19. Community acquired pneumonia (CAP) and other non-pneumonia CT exams were included to test the robustness of the model. The datasets were collected from 6 hospitals between August 2016 and February 2020. Diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC), sensitivity and specificity. Results The collected dataset consisted of 4356 chest CT exams from 3,322 patients. The average age is 49±15 years and there were slightly more male patients than female (1838 vs 1484; p-value=0.29). The per-exam sensitivity and specificity for detecting COVID-19 in the independent test set was 114 of 127 (90% [95% CI: 83%, 94%]) and 294 of 307 (96% [95% CI: 93%, 98%]), respectively, with an AUC of 0.96 (p-value | |
| Radiology | |
| 2020 | |
| Artículo | |
| https://pubs.rsna.org/doi/pdf/10.1148/radiol.2020200905 | |
| Inglés | |
| VIRUS RESPIRATORIOS | |
| Aparece en las colecciones: | Artículos científicos |
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