使用视觉变压器对顶开放的分类:与专家决定进行比较的方法
Merve Daldal1, Sümeyye Coşgun Baybars2, Merve Parlak Baydoğan3
1Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Fırat University, Elazığ, Turkey. mdaldal@firat.edu.tr.
Journal of imaging informatics in medicine
|December 10, 2025
概括
这项研究开发了一种使用视觉变压器 (ViT) 模型的人工智能 (AI) 方法,用于在牙科全景放射图中分类顶端根开放. 人工智能实现了88%的准确性,为改善临床决策支持提供一致的结果.
科学领域:
- 牙科 牙科是指牙科的专业.
- 放射学 放射学是一门学科.
- 人工智能的人工智能
背景情况:
- 根形态评估对于牙科诊断和治疗计划至关重要.
- 上开放,表明根部发育不完全,在内牙和正牙治疗中提出了挑战,特别是在年轻患者中.
- 全景放射提供广泛的解剖覆盖与低辐射剂量,使它们适合牙科评估.
研究的目的:
- 开发和评估一种基于人工智能 (AI) 的方法,用于分类全景放射图中的顶端根开放度.
- 评估视觉变压器 (ViT) 模型在识别不同程度的顶点开放度方面的性能.
主要方法:
- 来自512张全景放射图的902个单根永久牙的数据集进行了策划.
- 牙被手工切割,并分为闭顶,解剖开放和病理开放的组.
- 在图像预处理后使用视觉变压器 (ViT Base Patch32) 模型进行分类.
主要成果:
- 这款ViT型号的整体准确度,精度,回忆率和F1得分达到了88%.
- 人工智能模型与牙科学生的手动评估相比,显示了更一致的分类结果.
- 该模型在分类角根开放方面特别优于经验较少的牙科专业人员.
结论:
- 视觉变压器 (ViT) 模型在全景射线图上检测顶端根开放度时显示出高准确度.
- 这种基于人工智能的方法显示出作为牙科临床决策支持系统的可靠工具的重大前景.
- 人工智能方法可以帮助牙医进行诊断和治疗计划,特别是在涉及不完全根部发育的情况下.
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