Deep learning-based automated scanline localization in suprahyoid muscle ultrasound for quantitative dysphagia assessment.
Authors
Affiliations (7)
Affiliations (7)
- School of Artificial Intelligence Convergence, Hallym University, Chuncheon, Republic of Korea.
- Institute of New Frontier Research Center, Hallym University College of Medicine, Chuncheon, Republic of Korea.
- Department of Neurology, Hallym Chuncheon Sacred Heart Hospital, Hallym University College of Medicine, Chuncheon, Republic of Korea. [email protected].
- Institute of New Frontier Research Center, Hallym University College of Medicine, Chuncheon, Republic of Korea. [email protected].
- Department of Neurology, Hallym Chuncheon Sacred Heart Hospital, Hallym University College of Medicine, Chuncheon, Republic of Korea.
- Department of Anesthesiology and Pain, Hallym Chuncheon Sacred Heart Hospital, Hallym University College of Medicine, Chuncheon, Republic of Korea.
- School of Artificial Intelligence Convergence, Hallym University, Chuncheon, Republic of Korea. [email protected].
Abstract
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.