辅助人工智能的放射学分析用于检测牙槽骨丧失的严重程度和模式
AI-assisted radiographic analysis in detecting alveolar bone-loss severity and patterns
AI总结：
本研究提出了一种新型的基于人工智能的深度学习框架，利用牙内窥镜根尖放射图像自动检测和量化牙槽骨丧失及其模式，通过结合YOLOv8进行牙齿检测和Keypoint R-CNN模型识别解剖标志物，实现了对牙槽骨丧失严重程度的精确计算，并通过几何分析确定水平与角状骨丧失模式，实验结果在1000张专家标注的放射图像上达到了高准确率。
摘要：
牙周炎是一种慢性炎症性疾病，导致牙槽骨丧失，显著影响口腔健康和生活质量。准确评估骨丧失的严重程度和模式对于诊断和治疗计划至关重要。在本研究中，我们提出了一种新型的基于人工智能的深度学习框架，利用牙内窥镜根尖放射图像自动检测和量化牙槽骨丧失及其模式。我们的方法结合YOLOv8进行牙齿检测，与Keypoint R-CNN模型识别解剖标志物，从而实现对骨丧失严重程度的精确计算。此外，YOLOv8x-seg模型用于分割骨水平和牙齿掩码，通过几何分析确定骨丧失模式（水平 vs. 角状）。在1000张大规模、专家标注的放射图像上进行评估，我们的方法在检测骨丧失严重程度（类内相关系数高达0.80）和骨丧失模式分类（准确率87%）方面取得了高准确率。这种自动化系统提供了一种快速、客观且可重复的牙周评估工具，减少了对主观手动评估的依赖。通过将人工智能整合到牙科放射学分析中，我们的框架有潜力提高牙周炎的早期诊断和个性化治疗计划，最终改善患者护理和临床结果。
英文摘要：
Periodontitis, a chronic inflammatory disease causing alveolar bone loss, significantly affects oral health and quality of life. Accurate assessment of bone loss severity and pattern is critical for diagnosis and treatment planning. In this study, we propose a novel AI-based deep learning framework to automatically detect and quantify alveolar bone loss and its patterns using intraoral periapical (IOPA) radiographs. Our method combines YOLOv8 for tooth detection with Keypoint R-CNN models to identify anatomical landmarks, enabling precise calculation of bone loss severity. Additionally, YOLOv8x-seg models segment bone levels and tooth masks to determine bone loss patterns (horizontal vs. angular) via geometric analysis. Evaluated on a large, expertly annotated dataset of 1000 radiographs, our approach achieved high accuracy in detecting bone loss severity (intra-class correlation coefficient up to 0.80) and bone loss pattern classification (accuracy 87%). This automated system offers a rapid, objective, and reproducible tool for periodontal assessment, reducing reliance on subjective manual evaluation. By integrating AI into dental radiographic analysis, our framework has the potential to improve early diagnosis and personalized treatment planning for periodontitis, ultimately enhancing patient care and clinical outcomes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
