Key result
Deep learning on CMR cine images predicts mPAP severity with 0.82 AUC, outperforming traditional models.
Why the study?
Accurate estimation of mPAP and PVRi in pulmonary arterial hypertension typically requires invasive cardiac catheterization, which carries risks, especially in children.
Can deep learning models accurately predict invasive pulmonary hemodynamics (mPAP and PVRi) from non-invasive CMR cine images in pediatric PAH patients?
Observational (n=33)
No
Can deep learning models accurately predict invasive pulmonary hemodynamics (mPAP and PVRi) from non-invasive CMR cine images in pediatric PAH patients?
Effect estimate: AUC 0.82 (95% CI 0.48-1.00)
Absolute Event Rate: 0.82% vs 0.6%
Deep learning models applied to CMR cine images show potential for non-invasive prediction of pulmonary hemodynamics in pediatric PAH, potentially reducing the need for invasive right heart catheterization.
May aid non-invasive mPAP estimation in pediatric PAH; leaves open validation before reducing catheterization.
PURPOSE: Pulmonary arterial hypertension (PAH) significantly affects the pulmonary vasculature, requiring accurate estimation of mean pulmonary arterial pressure (mPAP) and pulmonary vascular resistance index (PVRi). Although cardiac catheterization is the gold standard for these measurements, it poses risks, especially in children. This pilot study explored how machine learning (ML) can predict pulmonary hemodynamics from non-invasive cardiac magnetic resonance (CMR) cine images in pediatric PAH patients. METHODS: A retrospective analysis of 40 CMR studies from children with PAH using a four-fold stratified group cross-validation was conducted. The endpoints were severity profiles of mPAP and PVRi, categorised as 'low', 'high', and 'extreme'. Deep learning (DL) and traditional ML models were optimized through hyperparameter tuning. Receiver operating characteristic curves and area under the curve (AUC) were used as the primary evaluation metrics. RESULTS: =0.73). True positive rates (TPR) for predicting low, high, and extreme mPAP were 5/10, 11/16, and 11/14, respectively. TPR for predicting low, high, and extreme PVRi were 5/13, 14/15, and 7/12, respectively. Optimal DL models only used spatial patterns from consecutive CMR cine frames to maximize prediction performance. CONCLUSION: This exploratory pilot study demonstrates the potential of DL leveraging CMR imaging for non-invasive prediction of mPAP and PVRi in pediatric PAH. While preliminary, these findings may lay the groundwork for future advancements in CMR imaging in pediatric PAH, offering a pathway to safer disease monitoring and reduced reliance on invasive cardiac catheterization.
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Chu et al. (2025) conducted an observational in Pediatric pulmonary arterial hypertension (PAH) (n=33). Deep learning models utilizing cardiac magnetic resonance (CMR) cine imaging vs. Traditional machine learning models (Logistic Regression, Random Forest) was evaluated on Prediction of mean pulmonary arterial pressure (mPAP) severity profile on test folds (AUC) (AUC 0.82, 95% CI 0.48-1.00). Deep learning models utilizing cardiac magnetic resonance cine imaging predicted mean pulmonary arterial pressure severity profiles with an AUC of 0.82 on test folds, outperforming traditional machine learning models.
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