CARDINAL: Küçük Örneklem Ekokardiyografi için Fizyoloji Güdümlü Öğrenme

Özet (abstract)

Echocardiographic assessment of diastolic dysfunction from small clinical cohorts is susceptible to overfitting, unstable feature attribution, and poorly calibrated confidence estimates, limiting the clinical translation of machine learning models. This study aimed to develop and evaluate CARDINAL, a physiology-guided framework combining a dataset-specific structural prior, directional graph regularization, prototype-aligned representation learning, and Monte Carlo dropout uncertainty estimation, and to assess whether it improves discrimination, calibration, and uncertainty-guided referral relative to established tabular learning models under a leakage-controlled protocol. We present CARDINAL, a physiology-guided framework that integrates four components: (i) a dataset-specific structural prior that encodes known physiological relations among clinical variables; (ii) directional graph refinement under sparsity and acyclicity penalties, serving as a regularizer and interpretability scaffold rather than as an identified causal model; (iii) prototype-aligned representation learning to enforce intra-class compactness under limited data; and (iv) Monte Carlo dropout-based uncertainty estimation ( \(T=30\) , dropout rate 0.20) for calibrated confidence and selective referral of borderline cases. The study cohort comprised 118 cases classified by a composite reference index as normal ( \(n=37\) ), mild ( \(n=42\) ), or severe ( \(n=39\) ). Evaluation followed a leakage-controlled protocol with a fixed stratified 70/15/15 split producing an 82-case training pool, an 18-case validation set, and an 18-case locked test set evaluated once after all modeling choices were finalized. On the locked test set, CARDINAL achieved a binary abnormal-versus-normal ROC-AUC of 0.969, AUPRC of 0.990, accuracy of 0.944, sensitivity of 0.923, specificity of 1.000, Brier score of 0.066, ECE of 0.095, and NLL of 0.223. Patient-level DeLong and McNemar tests were non-significant on this single split ( \(n=18\) ), reflecting limited statistical power rather than equivalence. Across 20 matched repeated runs, CARDINAL showed consistent stability with Holm-adjusted Wilcoxon \(p = 3.05\times10^{-5}\) versus TabPFN-tuned. Three-class confusion analysis indicated reasonable grade-level discrimination, with misclassifications occurring predominantly between adjacent grades. Ablation analysis suggested that each framework component—directional graph regularization, prototype alignment, and uncertainty calibration—contributed to overall performance. Three public datasets (UCI Heart Disease, Heart Failure Clinical Records, and MIMIC-IV) served as tabular robustness benchmarks; CARDINAL showed consistently modest numerical advantages across all four datasets. These results suggest that physiological structure, prototype learning, and uncertainty quantification can be integrated within a leakage-controlled evaluation framework. Feature importance analysis indicated that \({E/e}^\prime\) and LAVI—label-adjacent variables in the reference index—were the predominant contributors to model predictions, consistent with established cardiology guidelines. A complementary analysis excluding these label-adjacent variables preserved CARDINAL’s relative advantage (ROC-AUC 0.873 versus 0.846 for TabPFN-tuned). Uncertainty estimates may assist in identifying borderline cases for selective human review. These findings motivate prospective multi-center validation to evaluate clinical utility, transportability, and the impact of uncertainty-guided review on patient care.

Yazarların özeti; kaynağından alınmıştır. BMC Medical Informatics and Decision Making, 2026 · DOI ↗