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

Authors
 Hwan Kim  ;  Young Joong Yang  ;  Kyunghwa Han  ;  Pan Ki Kim  ;  Byoung Wook Choi  ;  Jin Young Kim  ;  Young Joo Suh 
Citation
 QUANTITATIVE IMAGING IN MEDICINE AND SURGERY, Vol.13(10) : 6750-6760, 2023-10 
Journal Title
QUANTITATIVE IMAGING IN MEDICINE AND SURGERY
ISSN
 2223-4292 
Issue Date
2023-10
Keywords
Magnetic resonance imaging (MRI) ; T2 map ; deep learning (DL) ; heart ; myocarditis
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.
Files in This Item:
T202305799.pdf Download
DOI
10.21037/qims-23-375
Appears in Collections:
1. College of Medicine (의과대학) > Research Institute (부설연구소) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers
Yonsei Authors
Suh, Young Joo(서영주) ORCID logo https://orcid.org/0000-0002-2078-5832
Choi, Byoung Wook(최병욱) ORCID logo https://orcid.org/0000-0002-8873-5444
Han, Kyung Hwa(한경화)
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/196543
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