Automatic Assessment of Periodontium Complex in Intraoral Ultrasound Videos

Intraoral ultrasound (IUS) is emerging as a valuable imaging modality in dentistry, offering noninvasive, radiation-free, real-time visualization of periodontal structures. Unlike traditional imaging methods, IUS enables dynamic assessments during clinical procedures, supporting diagnostic and treatment-planning capabilities. The accurate evaluation of parameters such as alveolar bone level (ABL), gingival thickness (GT), and alveolar bone thickness (ABT) is critical for diagnosing periodontal diseases. However, current assessment techniques are typically manual, time-consuming, and based on static images, leading to inter-operator variability and limiting real-time application. To address these gaps, this study aimed to develop OralSAM, an end-to-end machine learning network for automated segmentation and quantitative assessment of periodontal structures in IUS videos. The network segments gingiva, enamel, alveolar bone, and cementum, followed by a morphological analysis pipeline to extract clinically relevant measurements. A total of 158 IUS videos from 30 orthodontic patients were included, and the dataset was split into training, validation, and testing subsets following a 6:2:2 ratio. The segmentation performance of OralSAM, evaluated against expert-annotated ground truth, demonstrated high segmentation accuracy across key periodontal structures. Morphological measurements derived from the machine learning network also exhibited strong inter-rater reliability, as confirmed by Bland-Altman analysis, which demonstrated narrow limits of agreement (LOAs) for ABL (mean bias = -0.063 mm, LOA = -0.771 to 0.646 mm), GT (mean bias = -0.063 mm, LOA = -0.24 to 0.115 mm), and ABT (mean bias = -0.002 mm, LOA = -0.104 to 0.1 mm). The intraclass correlation coefficients were 0.893 (95% confidence interval [CI], 0.864 to 0.915) for ABL, 0.918 (95% CI, 0.768 to 0.960) for GT, and 0.848 (95% CI, 0.806 to 0.880) for ABT. These findings highlight OralSAM's capability to accurately delineate periodontal structures and provide consistent assessments. The proposed framework shows strong potential for integration into routine chairside workflows, enabling early detection, real-time monitoring, and personalized management of periodontal disease.