Deep learning-based automated scanline localization in suprahyoid muscle ultrasound for quantitative dysphagia assessment

Ultrasound-based assessment of suprahyoid muscle (SHM) motion has emerged as a promising approach for evaluating dysphagia. However, quantitative analysis remains limited by operator-dependent scanline placement and variability in manual interpretation. This study proposes an integrated framework combining mathematical modeling, automated SHM segmentation, scanline determination, and deep learning–based analysis for objective dysphagia assessment. A total of 371 ultrasound images acquired from patients with normal, mild, and severe dysphagia were analyzed. Clinically relevant SHM-derived variables, including thickness, displacement, SHM Difference (Δ = D − T), and total duration, were incorporated into a mathematical interpretation framework. U-Net was employed for SHM segmentation and automatic scanline determination, and agreement with expert-selected scanlines was evaluated using median-based accuracy, intraclass correlation coefficient (ICC), mean pairwise absolute difference (MPAD), and population standard deviation (SDpop). In addition, five deep learning architectures (ResNet-101, Fast R-CNN, YOLO11, VGG-19, and U-Net) were compared for dysphagia severity classification. The proposed framework demonstrated high agreement with expert assessments. U-Net achieved an average intersection-over-union (IoU) of 0.9599 across the three major SHM components, indicating near-expert segmentation performance. Automated scanline determination achieved an overall agreement of 72.3% with expert consensus, while manual scanline placement exhibited relatively low interobserver reliability (ICC = 0.386). Among the evaluated classification models, Fast R-CNN achieved the highest performance with an F1-score of 0.993 and an inference time of 3.44 ms. Grad-CAM analysis further confirmed that model attention was concentrated on clinically relevant SHM regions. The proposed framework provides an objective and reproducible approach for ultrasound-based dysphagia assessment by integrating mathematical modeling with AI-driven segmentation and classification. Automated SHM localization and scanline determination reduce operator dependency while maintaining expert-level accuracy. These findings support the potential of AI-assisted ultrasound as a clinically applicable tool for standardized dysphagia evaluation and real-time decision support. A computational framework for automated dysphagia severity assessment. Ultrasound images of swallowing are processed by AI to segment suprahyoid muscles and automatically determine scanlines. Quantified motion features are modeled mathematically and used to classify dysphagia severity with reduced expert variability.