Assessing the Performance and Reliability of Deep Learning Auto-Segmentation in Videofluoroscopic Swallowing Studies: A Systematic Review and Meta-Analysis.

OBJECTIVE: To systematically evaluate the accuracy and reliability of deep learning-based auto-segmentation methods in videofluoroscopic swallowing studies (VFSS) through meta-analysis. DATA SOURCES: A comprehensive literature search was conducted across PubMed, IEEE Xplore, Embase, Web of Science, and Cochrane Library databases for studies published in English between 2013 and 2024. STUDY SELECTION: Studies were included if they applied deep learning techniques to the auto-segmentation of anatomical structures in VFSS, specifically the bolus, cervical spine, hyoid bone, or thyroid cartilage-vocal fold complex (TVC) and reported quantitative performance metrics such as the Dice similarity coefficient. DATA EXTRACTION: Two independent reviewers extracted data on study characteristics, segmentation targets, deep learning model types, and performance metrics. Methodological quality was assessed using the CLAIM and QUADAS-2 tools. DATA SYNTHESIS: Ten studies met inclusion criteria. A random-effects meta-analysis yielded an overall pooled Dice score of 0.83 (95% CI: 0.76-0.88, I² = 77%). Subgroup analyses showed similar performance for bolus segmentation (pooled Dice score = 0.84; 95% CI: 0.70-0.92, I² = 74%) and cervical spine segmentation (pooled Dice score = 0.83; 95% CI: 0.69-0.91, I² = 87%). Despite high accuracy, substantial heterogeneity was observed. CONCLUSIONS: Deep learning-based auto-segmentation in VFSS demonstrates promising accuracy across different anatomical targets. However, methodological variability among studies underscores the need for standardized protocols, multi-center datasets, and comparative evaluations of model architectures to enhance generalizability and clinical utility. PROSPERO registration: CRD42024578117.