Name
Title | ||
|---|---|---|
Severity Assessment and Progression Prediction of COVID-19 Patients Based on the LesionEncoder Framework and Chest CT |
Abstract | ||
|---|---|---|
Automatic severity assessment and progression prediction can facilitate admission, triage, and referral of COVID-19 patients. This study aims to explore the potential use of lung lesion features in the management of COVID-19, based on the assumption that lesion features may carry important diagnostic and prognostic information for quantifying infection severity and forecasting disease progression. A novel LesionEncoder framework is proposed to detect lesions in chest CT scans and to encode lesion features for automatic severity assessment and progression prediction. The LesionEncoder framework consists of a U-Net module for detecting lesions and extracting features from individual CT slices, and a recurrent neural network (RNN) module for learning the relationship between feature vectors and collectively classifying the sequence of feature vectors. Chest CT scans of two cohorts of COVID-19 patients from two hospitals in China were used for training and testing the proposed framework. When applied to assessing severity, this framework outperformed baseline methods achieving a sensitivity of 0.818, specificity of 0.952, accuracy of 0.940, and AUC of 0.903. It also outperformed the other tested methods in disease progression prediction with a sensitivity of 0.667, specificity of 0.838, accuracy of 0.829, and AUC of 0.736. The LesionEncoder framework demonstrates a strong potential for clinical application in current COVID-19 management, particularly in automatic severity assessment of COVID-19 patients. This framework also has a potential for other lesion-focused medical image analyses. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.3390/info12110471 | INFORMATION |
Keywords | DocType | Volume |
chest CT, COVID-19, severity assessment, progression prediction, U-Net, RNN | Journal | 12 |
Issue | Citations | PageRank |
11 | 0 | 0.34 |
References | Authors | |
0 | 14 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Youzhen Feng | 1 | 0 | 0.34 |
| Sidong Liu | 2 | 207 | 19.24 |
| Zhongyuan Cheng | 3 | 0 | 0.34 |
| Juan C. Quiroz | 4 | 0 | 1.69 |
| Data Rezazadegan | 5 | 0 | 0.34 |
| Pingkang Chen | 6 | 0 | 0.34 |
| Qiting Lin | 7 | 0 | 0.34 |
| Qian Long | 8 | 57 | 10.30 |
| Xiaofang Liu | 9 | 0 | 0.34 |
| Shlomo Berkovsky | 10 | 1027 | 86.12 |
| Enrico Coiera | 11 | 162 | 22.79 |
| Lei Song | 12 | 0 | 0.34 |
| Xiaoming Qiu | 13 | 4 | 1.13 |
| Xiangran Cai | 14 | 0 | 0.34 |