Chen Chen1, Fabao Gao1, and Xiaoyue Zhou2
1Department of Radiology, West China Hospital, Chengdu, China, 2MR Collaboration, Siemens Healthineers Ltd., Shanghai, China
1Department of Radiology, West China Hospital, Chengdu, China, 2MR Collaboration, Siemens Healthineers Ltd., Shanghai, China
Machine learning demonstrated excellent diagnostic
performance for prediction of PFTs, especially for MB vs non-MB and PA vs
non-PA. MRI sequences, algorithms, region of interest, and feature extraction were the main
factors affecting the diagnostic performance of machine learning.
Table 1 Baseline characteristic of included
studies
Fig. 3 Forest plot of single studies for
the pooled DOR and a represents EP vs non-EP, b MB vs non-MB, c PA vs non-PA.