Identification of ischemic stroke risk in patients with left ventricular non-compaction using echocardiography, deep learning, and radiomics.

External validation of machine learning models predicting global longitudinal strain from conventional echocardiography in patients with cancer.

AI-PPG for pre-screening of reduced left ventricular ejection fraction: development and prospective external validation

A Nomogram Integrating Clinical and Cardiac Imaging for Predicting Short-Term Left Ventricular Ejection Fraction Decline in Patients with Duchenne Muscular Dystrophy

Purpose This study aimed to develop and validate a nomogram for predicting short-term LVEF decline in patients with DMD. Methods This was a single-center retrospective cohort study enrolling male patients diagnosed with DMD at Sun Yat-sen Memorial Hospital, Sun Yat-sen University, between 2015 and 2025. Data collected included patient age, cardiac troponin I (cTnI) levels, history of steroid therapy, baseline echocardiographic LVEF, and CMR data (including LGE and native T1 values). The primary outcome was the decline in LVEF (ΔLVEF ≤ −10%) during follow-up within 12 months. Least absolute shrinkage and selection operator (LASSO) logistic regression analysis was employed to identify independent risk factors and construct a nomogram-based predictive model. Model performance was assessed by the area under the receiver operating characteristic (ROC) curve and internally validated using bootstrap resampling (1000 repetitions). Results A total of 102 patients were included, of whom 38 (37.3%) exhibited a decline in LVEF. Multivariable analysis identified older age (OR 1.16, 95% CI 1.04–1.30, p = 0.009), abnormal cTnI (cTnI ≥ 0.04 ng/mL) (OR 7.27, 95% CI 1.46–36.28, p = 0.016), longer steroid duration (OR 1.72, 95% CI 1.09–2.71, p = 0.020, likely reflecting disease severity), the presence of LGE (OR 5.45, 95% CI 1.27–23.43, p = 0.023), and higher native T1 values (OR 1.02, 95% CI 1.01–1.04, p = 0.001) as independent risk factors. A higher baseline LVEF was protective (OR 0.81, 95% CI 0.72–0.91, p<0.001). The predictive model demonstrated excellent discrimination, with an AUC of 0.943 (95% CI 0.901–0.985). Internal validation yielded an optimism-corrected C-index of 0.922. Conclusion This study successfully established a comprehensive prediction model incorporating clinical and imaging variables, which can accurately identify DMD patients at risk for short-term LVEF decline. The model demonstrated high discriminative ability (AUC 0.943) in this retrospective, single-center cohort; however, these results are preliminary and require external validation.

An interpretable machine learning approach to predict left ventricular aneurysm formation following primary PCI in STEMI: A retrospective study with independent external validation.

BACKGROUND Left ventricular aneurysm (LVA) remains a clinically important structural complication after primary percutaneous coronary intervention (pPCI) in patients with ST-segment elevation myocardial infarction (STEMI). This study aimed to develop and externally validate an interpretable model for predicting LVA after pPCI. METHODS We retrospectively included 1507 patients from the development center and 535 patients from an external validation center. Least absolute shrinkage and selection operator (LASSO) regression and the Boruta algorithm were used for feature selection. Eight routinely available predictors were retained: albumin (ALB), N-terminal pro-B-type natriuretic peptide (NT-proBNP), C-reactive protein (CRP), Killip class ≥2, left ventricular ejection fraction (LVEF), Gensini score, lactate dehydrogenase (LDH), and infarct-related artery involving the left anterior descending artery (IRA-LAD). These variables were incorporated into eight machine learning algorithms. Model performance was evaluated using discrimination, calibration, decision curve analysis (DCA), and classification metrics. SHapley Additive exPlanations (SHAP) was used for model interpretation, and a web-based calculator was developed. RESULTS Logistic regression showed the most favorable balance between performance and interpretability. The model achieved an area under the receiver operating characteristic curve (AUC) of 0.948 (95% confidence interval [CI], 0.897-0.997) in internal validation and 0.950 (95% CI, 0.908-0.992) in external validation. Calibration and DCA showed acceptable agreement and clinical net benefit. SHAP analysis identified LVEF, NT-proBNP, Killip class ≥2, and CRP as major predictors. CONCLUSIONS An interpretable model using routine clinical variables was developed and externally validated for predicting LVA after pPCI in STEMI patients. This model may support individualized risk assessment and follow-up planning.

Machine learning–based prediction of in-hospital deep vein thrombosis in patients with acute ischemic stroke: a multicenter study

Background Deep vein thrombosis (DVT) is a common complication of acute ischemic stroke (AIS) and may worsen clinical outcomes, yet reliable tools for early risk stratification remain limited. We aimed to develop and internally evaluate machine learning models for predicting in-hospital DVT in patients with AIS. Methods We conducted a secondary analysis of a publicly available multicenter retrospective dataset including 21,459 patients with AIS. The primary outcome was imaging-confirmed in-hospital DVT. Participants were stratified according to DVT status and randomly divided into a training set (70%) and a held-out test set (30%). Feature selection was performed using least absolute shrinkage and selection operator regression, the Boruta algorithm, variance inflation factor assessment, and clinical judgment. Eight machine learning algorithms were trained using a 19-variable full predictor set and an 8-variable simplified predictor set. Hyperparameters were optimized using repeated 5-fold cross-validation with 2 repeats. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), area under the precision-recall curve (AUPRC), Brier score, calibration, decision curve analysis, and additional classification metrics. Sensitivity analyses excluded D-dimer and used within-fold synthetic minority oversampling. Results Among 21,459 patients, 1,324 (6.17%) developed in-hospital DVT. In the full predictor-set analysis, RANGER achieved the highest AUC in the held-out test set (0.976). Among models using the simplified predictor set, XGBoost achieved the highest AUC (0.917) and sensitivity (0.852), whereas SVM demonstrated the most favorable overall performance profile, with the highest AUPRC (0.605), lowest Brier score (0.038), highest positive predictive value (0.440), and highest F1-score (0.549). D-dimer was the most influential predictor. Model performance was attenuated after exclusion of D-dimer, and the SMOTE analysis showed slightly lower discrimination and greater calibration discrepancies. All 8 simplified predictor-set models were incorporated into an online prediction platform. Conclusion Machine learning models demonstrated favorable performance for predicting in-hospital DVT after AIS. Among models using the simplified 8-variable predictor set, SVM showed the most favorable overall performance, whereas XGBoost prioritized sensitivity. Independent external validation and prospective clinical-impact assessment are required before routine clinical implementation.

Nomogram model for predicting multivessel disease in coronary artery disease using cardiac function parameters and clinical features

Background Multivessel disease (MVD) represents a severe phenotype of coronary artery disease and is associated with poor prognosis. Early, non-invasive identification of MVD remains a clinical challenge. This study aimed to develop and validate a nomogram integrating cardiac function parameters and clinical characteristics for individualized prediction of MVD risk. Methods Clinical data were retrospectively collected from 353 patients with angiographically confirmed coronary artery disease at Guangzhou Red Cross Hospital between January 2023 and December 2024. Patients were randomly assigned to a training set (70%) and an internal validation set (30%). Least absolute shrinkage and selection operator (LASSO) regression was used to screen potential risk factors, followed by multivariate logistic regression to construct the predictive model and generate the nomogram. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA). Results Seven predictors were ultimately included in the nomogram: age, gender, prior stent implantation, total cholesterol, left ventricular ejection fraction (LVEF), high-density lipoprotein, and albumin. The model exhibited good discrimination, with an AUC of 0.84 (95% CI [0.79–0.90]) in the training set and 0.79 (95% CI [0.68–0.89]) in the validation set. Accuracy was 0.81 in both datasets. Calibration curves and decision curve analysis demonstrated good predictive accuracy and clinical utility of the nomogram. Furthermore, the nomogram scores successfully stratified patients into high-risk (≥191) and low-risk (<191) groups, with significantly different score distributions between the MVD and non-MVD groups (P < 0.001). The developed nomogram provides an accurate and individualized tool for non-invasive prediction of MVD risk and may assist clinicians in identifying high-risk patients who could benefit from intensified intervention.