通过使用CBCT成像技术,通过人工智能对关节退行性疾病进行自动诊断和分类
Wei-Yu Mao1, Yuan-Yuan Fang1, Zhong-Zhen Wang2
1Department of Oral & Maxillofacial Radiology, Peking University School & Hospital of Stomatology, Beijing 100081, PR China; National Center for Stomatology & National Clinical Research Center for Oral Diseases, Beijing 100081, PR China; National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, Beijing 100081, PR China; Beijing Key Laboratory of Digital Stomatology, Beijing 100081, PR China.
Journal of dentistry
|January 27, 2025
概括
人工智能 (AI) 准确地诊断了关节 (TMJ) 退行性关节疾病 (DJD) 以及其标志在形束计算断层扫描 (CBCT) 图像上. 这种深度学习模型有助于早期检测和分类TMJ DJD,改善患者的治疗结果.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能的人工智能
- 牙科 牙科是指牙科的专业.
背景情况:
- 关节 (TMJ) 退行性关节疾病 (DJD) 由于疼痛和功能障碍,显著影响生活质量.
- 关节DJD的早期诊断至关重要,但由于微妙的放射性迹象而具有挑战性.
- 圆束计算断层扫描 (CBCT) 是TMJ评估的关键成像方式.
研究的目的:
- 在CBCT图像上开发和评估人工智能模型,用于TMJ DJD的自动诊断和分类.
- 评估人工智能模型识别DJD特定放射性征兆的能力,包括侵蚀,骨质细胞,硬化和子冠状腺囊.
- 确定人工智能模型在检测和分类TMJ DJD及其组件中的准确性和性能指标.
主要方法:
- 基于YOLOv10算法的深度学习模型在7357张注释的TMJ CBCT图像上进行了训练.
- 该数据集包括来自1018名患有关节疾病的患者的图像.
- 该模型经过验证和测试,以检测DJD及其放射性标志的能力.
主要成果:
- 人工智能模型在检测TMJ DJD方面取得了高性能,准确度,精度和F1得分超过0.95.
- 该模型在识别个人DJD标志方面表现出很高的准确性:侵蚀 (0.91),骨质细胞 (0.96),硬化症 (0.91),和子冠状腺囊 (0.96).
- 在单个图像中检测多个标志的性能各不相同,准确度从一个标志的94%,到四个标志的63%.
结论:
- 基于YOLOv10的深度学习模型有效地检测CBCT图像上的TMJ DJD及其特征性放射标志.
- 人工智能模型为TMJ DJD的准确和快速诊断和分类提供了一个有前途的工具.
- 人工智能辅助诊断可以显著帮助临床医生识别早期的关节DJD,促进及时干预.
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