Name
Abstract | ||
|---|---|---|
•Holistic information in COVID-19 patients with imaging and non-imaging data can help predict patient outcome in terms of the need for ICU admission.•Validation of model over multiple sites is important to establish its generalizablity.•Both volume and radiomic features of pulmonary opacities are key to quantifying the extent of lung involvement. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.media.2020.101844 | Medical Image Analysis |
Keywords | DocType | Volume |
COVID-19,Chest CT,Outcome prediction,Artificial intelligence | Journal | 67 |
ISSN | Citations | PageRank |
1361-8415 | 3 | 0.38 |
References | Authors | |
1 | 16 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hanqing Chao | 1 | 8 | 3.16 |
| Xi Fang | 2 | 4 | 1.78 |
| Jiajin Zhang | 3 | 3 | 0.38 |
| Fatemeh Homayounieh | 4 | 4 | 2.13 |
| Chiara D. Arru | 5 | 3 | 0.38 |
| Subba R. Digumarthy | 6 | 5 | 1.45 |
| Rosa Babaei | 7 | 3 | 0.38 |
| Hadi K. Mobin | 8 | 3 | 0.38 |
| Iman Mohseni | 9 | 3 | 0.38 |
| Luca Saba | 10 | 202 | 24.44 |
| Alessandro Carriero | 11 | 3 | 0.72 |
| Zeno Falaschi | 12 | 3 | 0.38 |
| Alessio Pasche | 13 | 3 | 0.38 |
| Ge Wang | 14 | 1000 | 142.51 |
| Mannudeep Kalra | 15 | 144 | 14.28 |
| Pingkun Yan | 16 | 1306 | 83.14 |