使用CBCT图像对下沟与第三关节的自动分类
Neil Abraham Barnes1, Winniecia Dkhar1, Sharath S1
1Department of Medical Imaging Technology, Manipal College of Health Professions, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India.
F1000Research
|February 10, 2025
概括
人工智能 (AI) 模型被开发用于自动化下沟通道与第三关节的分类,使用圆束计算断层扫描 (CBCT) 图像. 这些人工智能模型实现了高精度,提高了牙科放射学的效率.
科学领域:
- 牙科放射学 牙科放射学
- 医学成像医学成像
- 人工智能的人工智能是人工智能.
背景情况:
- 圆束计算断层扫描 (CBCT) 由于其尺寸和低辐射,在牙科放射学中具有优势.
- 管道跟踪对于评估下膜神经和第三关节关系至关重要,通常是一个耗时的手工过程.
- 自动化下沟与第三牙关系的分类对于提高效率至关重要.
研究的目的:
- 开发和评估人工智能 (AI) 模型,用于自动化下沟通道与第三关节的分类.
- 评估卷积神经网络 (CNN) 模型的性能,特别是 AlexNet 和 ResNet50,用于此分类任务.
- 确定人工智能是否可以提供与手工方法相比准确和高效的分类.
主要方法:
- 一项回顾性研究使用了434张CBCT图像,使用3D Slicer软件进行注释和分类到语言,口腔和劣等类别.
- 两个CNN模型,AlexNet和ResNet50,在262张图像上进行了训练,并在172张图像上进行了测试.
- 模型性能使用灵敏度,精度,F1得分和混矩阵进行评估.
主要成果:
- 亚历克斯Net和ResNet50模型都在将下沟与第三关节相比分类时表现出高准确度.
- 在不同级别的车型中,F1分数从0.64到0.92不等.
- 这些模型的整体准确率为81% (AlexNet) 和83% (ResNet50).
结论:
- 人工智能模型可以使用CBCT图像准确地分类下沟通道和第三牙之间的关系.
- 开发的AI模型为手动道跟踪提供了更准确,更有效的替代方案.
- 这一进步对牙科和手术成像精度有重大影响.
相关概念视频
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The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
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