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Automatic Segmentation of Periodontal Tissue Ultrasound Images with Artificial Intelligence: A Novel Method for Improving Dataset Quality
Stefano Pagliari; Shinji Kuroda; Roberto Sacco; Dimitrios Michelogiannakis; Salvatore Sauro
This study introduces a novel AI-based pipeline designed not primarily to perform periodontal ultrasound segmentation, but rather to improve dataset quality by automatically identifying inadequate or low-quality annotations. Periodontal ultrasonography is an emerging imaging modality capable of visualizing gingival and periodontal soft tissues without ionizing radiation. However, manual segmentation of structures such as the gingival margin, alveolar crest, sulcus, and cemento-enamel junction is highly operator-dependent and prone to inconsistency. These inconsistencies significantly reduce the reliability of datasets used for machine learning model development. The authors developed a deep learning segmentation system using a U‑Net–based architecture trained to detect four periodontal structures. A dataset of 704 ultrasound images was annotated by trained clinicians and divided into 80% training and 20% testing. Importantly, two training strategies were evaluated: (1) a model trained on all annotations, including low-quality ones, and (2) a model trained only on high-quality, curated annotations. The objective was to investigate whether AI could help identify erroneous annotations by exhibiting poor segmentation performance on those cases. The model trained on curated annotations performed substantially better, with Dice scores improving across all four structures. Visual inspection of segmentation failures revealed that the model consistently struggled on images with noisy or incorrect human labels—allowing the system to act as an automated “annotation auditor.” This demonstrates a new conceptual approach: using AI not only for segmentation, but also as a quality control mechanism to enhance dataset reliability before downstream model training. By establishing a quantifiable and automated method for detecting weak annotations, the study provides an important contribution to the emerging field of periodontal ultrasound AI. It highlights the need for rigorous dataset curation and suggests that machine learning can play a central role in improving annotation consistency and accelerating clinical translation of periodontal ultrasound imaging.
Periodontal Tissue ; ultrasound; ltrasonography; segmentation; dataset quality; deep learning; Mask R-CNN; U-Net; deep learning
Improving annotation quality is critical for advancing periodontal ultrasound AI models and ensuring consistency in soft tissue evaluation.