Pyramid Convolution Transformer-Driven YOLOv10 for Multimodal Clinical and Echocardiographic Heart Disease Prediction

Early and accurate prediction of cardiovascular diseases (CVDs) remains challenging because existing diagnostic models often have limited generalizability, insufficient multimodal feature representation, and delayed detection. To address these limitations, a Pyramid Convolution Vision Transformer (PC-ViT)–Enhanced You Only Look Once version 10 (YOLOv10) framework is proposed to integrate clinical parameters with echocardiographic features for efficient multimodal heart disease prediction.The framework integrates pyramid convolutions to capture multi-scale cardiac structural patterns and Vision Transformer (ViT) layers to learn longrange dependencies across multimodal datasets. Additionally, YOLOv10 enables rapid and precise detection of defects in echocardiograms. The primary objective is to enhance prediction accuracy, minimize misclassifications, and offer explainable insights for clinical decision-making.Experimental evaluation demonstrates superior performance over existing hybrid CNN- and ViT-based models, yielding higher diagnostic accuracy, sensitivity, and F1 scores alongside fewer false alarms. The model's robust multimodal learning capability highlights PCViT-YOLOv10 as an efficient tool for early heart disease detection and timely intervention.