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Cine-cardiac magnetic resonance to distinguish between ischemic and non-ischemic cardiomyopathies: a machine learning
Riccardo Cau1, Francesco Pisu1, Alessandra Pintus1
1Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari - Polo di Monserrato s.s. 554 Monserrato, 09045, Cagliari, Italy.
Insights
A machine learning model effectively differentiates ischemic cardiomyopathy (ICM) from non-ischemic cardiomyopathy (NICM) using non-contrast cardiovascular magnetic resonance (CMR) imaging. This approach offers competitive diagnostic accuracy, potentially improving access to CMR examinations.
Area of Science:
- Cardiology
- Medical Imaging
- Machine Learning
Background:
- Cardiovascular magnetic resonance (CMR) is crucial for diagnosing cardiomyopathies.
- Differentiating ischemic cardiomyopathy (ICM) from non-ischemic cardiomyopathy (NICM) can be challenging.
- There is a need for faster and more cost-effective CMR protocols.
Purpose of the Study:
- To develop and evaluate a machine learning (ML) model for distinguishing ICM from NICM using non-contrast CMR.
- To compare the performance of an explainable gradient boosting model against traditional ML models.
- To identify key CMR parameters important for differentiating between ICM and NICM.
Main Methods:
- Retrospective analysis of 107 patient CMR scans (49 ICM, 58 NICM).
- Inclusion of atrial and ventricular strain parameters in the analysis.
- Training and validation of ML models using repeated cross-validation.
Main Results:
- The proposed ML model achieved an area under the curve of 0.82 and a Brier score of 0.19.
- Sensitivity at the Youden's index was 0.72, demonstrating competitive diagnostic accuracy and calibration.
- Both atrial and ventricular strain parameters were identified as important predictors for ICM.
Conclusions:
- Machine learning models utilizing multi-chamber myocardial strain and function on non-contrast CMR can discriminate between ICM and NICM with competitive accuracy.
- This ML approach may facilitate wider access to CMR examinations through lower costs and faster acquisition.
- Non-contrast CMR parameters analyzed by ML models offer an effective method for distinguishing between ischemic and non-ischemic cardiomyopathies.
Objective:
This work aimed to derive a machine learning (ML) model for the differentiation between ischemic cardiomyopathy (ICM) and non-ischemic cardiomyopathy (NICM) on non-contrast cardiovascular magnetic resonance (CMR).
Methods:
This retrospective study evaluated CMR scans of 107 consecutive patients (49 ICM, 58 NICM), including atrial and ventricular strain parameters. We used these data to compare an explainable tree-based gradient boosting additive model with four traditional ML models for the differentiation of ICM and NICM. The models were trained and internally validated with repeated cross-validation according to discrimination and calibration. Furthermore, we examined important variables for distinguishing between ICM and NICM.
Results:
A total of 107 patients and 38 variables were available for the analysis. Of those, 49 were ICM (34 males, mean age 60 ± 9 years) and 58 patients were NICM (38 males, mean age 56 ± 19 years). After 10 repetitions of the tenfold cross-validation, the proposed model achieved the highest area under curve (0.82, 95% CI [0.47-1.00]) and lowest Brier score (0.19, 95% CI [0.13-0.27]), showing competitive diagnostic accuracy and calibration. At the Youden's index, sensitivity was 0.72 (95% CI [0.68-0.76]), the highest of all. Analysis of predictions revealed that both atrial and ventricular strain CMR parameters were important for the identification of ICM patients.
Conclusion:
The current study demonstrated that using a ML model, multi chamber myocardial strain, and function on non-contrast CMR parameters enables the discrimination between ICM and NICM with competitive diagnostic accuracy.
Clinical Relevance Statement:
A machine learning model based on non-contrast cardiovascular magnetic resonance parameters may discriminate between ischemic and non-ischemic cardiomyopathy enabling wider access to cardiovascular magnetic resonance examinations with lower costs and faster imaging acquisition.
Key Points:
• The exponential growth in cardiovascular magnetic resonance examinations may require faster and more cost-effective protocols. • Artificial intelligence models can be utilized to distinguish between ischemic and non-ischemic etiologies. • Machine learning using non-contrast CMR parameters can effectively distinguish between ischemic and non-ischemic cardiomyopathies.
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