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Validation of a deep learning-based software for automated analysis of T2 mapping in cardiac magnetic resonance imaging

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Author(s)
Hwan KimYoung Joong YangKyunghwa HanPan Ki KimByoung Wook ChoiJin Young KimYoung Joo Suh
Keimyung Author(s)
Kim, Jin Young
Department
Dept. of Radiology (영상의학)
Journal Title
Quant Imaging Med Surg
Issued Date
2023
Volume
13
Issue
10
Keyword
Magnetic resonance imaging (MRI)T2 mapdeep learning (DL)heartmyocarditis
Abstract
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:
The average Dice similarity coefficient for segmentation of myocardium on T2 maps was 0.844. The automated T2 values were non-inferior to the manual reference T2 values on a per-segment analysis (45.35 vs. 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.
Keimyung Author(s)(Kor)
김진영
Publisher
School of Medicine (의과대학)
Type
Article
ISSN
2223-4306
Source
https://qims.amegroups.org/article/view/116588/html
DOI
10.21037/qims-23-375
URI
https://kumel.medlib.dsmc.or.kr/handle/2015.oak/45293
Appears in Collections:
1. School of Medicine (의과대학) > Dept. of Radiology (영상의학)
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