Multi-task learning for automated classification of hypertensive heart disease and hypertrophic cardiomyopathy using native T1 mapping
10.3760/cma.j.cn121382-20240222-00406
- VernacularTitle:基于多任务学习的native T1 mapping图像对高血压心脏病和肥厚型心肌病的自动分类
- Author:
Honglin ZHU
1
;
Yufan QIAN
;
Xiao CHANG
;
Yan ZHOU
;
Jian MA
;
Rong SUN
;
Shengdong NIE
;
Lianming WU
Author Information
1. 上海理工大学健康科学与工程学院，上海 200093
- Keywords:
Hypertrophic cardiomyopathy;
Hypertensive heart disease;
Multi-task learning;
Native T1 mapping;
Ten-fold crossover;
Confusion matrix;
Area under the curve
- From:
International Journal of Biomedical Engineering
2024;47(4):342-348
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To automatically classify hypertensive heart disease (HHD) and hypertrophic cardiomyopathy (HCM) based on mul-titask learning algorithm using native T1 mapping images.Methods:A total of 203 patients admitted to Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University from January 2017 to December 2021 were enrolled, including 53 patients with HHD, 121 patients with HCM, and 29 patients with normal control (NC). Native T1 mapping images of all enrolled patients were acquired using MRI and processed by a multi-task learning algorithm. The classification performance of each model was validated using ten-fold crossover, confusion matrix, and receiver operator characteristic (ROC) curves. The Resnet 50 model based on the original images was established as a control.Results:The ten-fold crossover validation results showed that the MTL-1 024, MTL-64, and MTL-all models showed better performance in terms of area under the curve (AUC), accuracy, sensitivity, and specificity compared to the Resnet 50 model. In the classification task, the MTL-64 model showed the best performance in terms of AUC (0.942 1), while the MTL-all model reached the highest value in terms of accuracy (0.852 2). In the segmentation task, the MTL-64 model achieved the best results with the Dice coefficient (0.879 7). The confusion matrix plot showed that the MTL model outperforms the Resnet 50 model based on the original image in terms of overall performance. The ROC graphs of all MTL models were significantly higher than the original image input Resnet 50 model.Conclusions:Multi-task learning-based native T1 mapping images are effective for automatic classification of HHD and HCM.
