Deep learning-based software for automated T2 map analysis yielded non-inferior measurements and detected elevated T2 values with 83.6-92.8% sensitivity and 82.5-92.0% specificity.
Observational (n=83)
Yes
Does a deep learning-based software provide non-inferior automated T2 measurements on 3.0-T cardiac MRI compared to manual analysis in healthy subjects and patients with myocarditis?
A deep learning-based software provides accurate and non-inferior automated T2 mapping measurements on cardiac MRI compared to manual analysis by experienced radiologists.
Background: The reliability and diagnostic performance of deep learning (DL)-based automated T2 measurements on T2 map of 3.0-T cardiac magnetic resonance imaging (MRI) using multi-institutional datasets have not been investigated. We aimed to evaluate the performance of a DL-based software for measuring automated T2 values from 3.0-T cardiac MRI obtained at two centers. Methods: Eighty-three subjects were retrospectively enrolled from two centers (42 healthy subjects and 41 patients with myocarditis) to validate a commercial DL-based software that was trained to segment the left ventricular myocardium and measure T2 values on T2 mapping sequences. Manual reference T2 values by two experienced radiologists and those calculated by the DL-based software were obtained. The segmentation performance of the DL-based software and the non-inferiority of automated T2 values were assessed compared with the manual reference standard per segment level. The software's performance in detecting elevated T2 values was assessed by calculating the sensitivity, specificity, and accuracy per segment. Results: 44.32 ms). The DL-based software exhibited good performance (sensitivity: 83.6-92.8%; specificity: 82.5-92.0%; accuracy: 82.7-92.2%) in detecting elevated T2 values. Conclusions: The DL-based software for automated T2 map analysis yields non-inferior measurements at the per-segment level and good performance for detecting myocardial segments with elevated T2 values compared with manual analysis.
Kim et al. (Mon,) conducted a observational in Myocarditis (n=83). Deep learning-based software for automated T2 mapping analysis vs. Manual reference T2 values by experienced radiologists was evaluated on Detection of elevated T2 values per segment. Deep learning-based software for automated T2 map analysis yielded non-inferior measurements and detected elevated T2 values with 83.6-92.8% sensitivity and 82.5-92.0% specificity.