Related Experiment Video
Updated: Oct 21, 2025
Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations
Published on: January 8, 2013
Automatic machine learning based on native T1 mapping can identify myocardial fibrosis in patients with hypertrophic
Wan-Lin Peng1, Tian-Jing Zhang2, Ke Shi1
1Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
Insights
Automatic machine learning using native T1 mapping can predict late gadolinium enhancement (LGE) status in hypertrophic cardiomyopathy (HCM) patients. This approach may help detect myocardial fibrosis without contrast agents, offering a promising tool for HCM management.
Area of Science:
- Cardiovascular Magnetic Resonance Imaging
- Artificial Intelligence in Medicine
- Cardiac Pathology
Background:
- Hypertrophic cardiomyopathy (HCM) is a genetic heart muscle disease.
- Late gadolinium enhancement (LGE) on MRI indicates myocardial fibrosis in HCM.
- Native T1 mapping is a non-contrast MRI technique that reflects tissue characteristics.
Purpose of the Study:
- To assess the feasibility of using automatic machine learning (autoML) with native T1 mapping to predict LGE status in HCM.
- To evaluate the diagnostic performance of autoML models in differentiating LGE-positive and LGE-negative HCM patients, and HCM patients from healthy controls.
Main Methods:
- Ninety-one HCM patients and 44 controls underwent cardiovascular MRI with native T1 mapping.
- An autoML pipeline (TPOT) was used for three binary classifications: LGE+ vs LGE-, LGE- vs Control, and HCM vs Control.
- Model performance was evaluated using sensitivity, specificity, accuracy, and AUC.
Main Results:
- AutoML models achieved diagnostic accuracies of 0.80 (by slice) and 0.79 (by case) for predicting LGE status in HCM patients.
- The models also discriminated between LGE-negative HCM patients and controls (accuracy: 0.77-0.78) and between all HCM patients and controls (accuracy: 0.88).
Conclusions:
- Native T1 map analysis with autoML correlates with LGE status in HCM.
- The TPOT algorithm shows potential for predicting myocardial fibrosis (LGE) in HCM without contrast agents.
- AutoML can detect native T1 map alterations even in LGE-negative HCM patients.
Objectives:
To investigate the feasibility of automatic machine learning (autoML) based on native T1 mapping to predict late gadolinium enhancement (LGE) status in hypertrophic cardiomyopathy (HCM).
Methods:
Ninety-one HCM patients and 44 healthy controls who underwent cardiovascular MRI were enrolled. The native T1 maps of HCM patients were classified as LGE ( +) or LGE (-) based on location-matched LGE images. An autoML pipeline was implemented using the tree-based pipeline optimization tool (TPOT) for 3 binary classifications: LGE ( +) and LGE (-), LGE (-) and control, and HCM and control. TPOT modeling was repeated 10 times to obtain the optimal model for each classification. The diagnostic performance of the best models by slice and by case was evaluated using sensitivity, specificity, accuracy, and microaveraged area under the curve (AUC).
Results:
Ten prediction models were generated by TPOT for each of the 3 binary classifications. The diagnostic accuracy obtained with the best pipeline in detecting LGE status in the testing cohort of HCM patients was 0.80 by slice and 0.79 by case. In addition, the TPOT model also showed discriminability between LGE (-) patients and control (accuracy: 0.77 by slice; 0.78 by case) and for all HCM patients and controls (accuracy: 0.88 for both).
Conclusions:
Native T1 map analysis based on autoML correlates with LGE ( +) or (-) status. The TPOT machine learning algorithm could be a promising method for predicting myocardial fibrosis, as reflected by the presence of LGE in HCM patients without the need for late contrast-enhanced MRI sequences.
Key Points:
• The tree-based pipeline optimization tool (TPOT) is a machine learning algorithm that could help predict late gadolinium enhancement (LGE) status in patients with hypertrophic cardiomyopathy. • The TPOT could serve as an adjuvant method to detect LGE by using information from native T1 maps, thus avoiding the need for contrast agent. • The TPOT also detects native T1 map alterations in LGE-negative patients with hypertrophic cardiomyopathy.
