人工智能用于在有限的数据可用性的情况下在周围放射学上诊断dens evaginatus
Eunhye Choi1, KangMi Pang2, Eunjae Jeong3,4
1School of Dentistry, Dental Research Institute, Seoul National University, Seoul, Republic of Korea.
Scientific reports
|August 14, 2023
概括
一个人工智能 (AI) 模型准确地在周围放射 (PA) 图像上诊断出密度发泄 (DE). 这种人工智能工具与内牙医相比表现优越,有助于早期诊断和改善牙预后.
科学领域:
- 牙科 牙科是指牙科的专业.
- 人工智能的人工智能
- 放射学 放射学是一门学科.
背景情况:
- 密度发发 (Dens evaginatus,简称DE) 是一种发育异常,可能导致脉和周围脉的病变.
- 精确和及时的诊断DE在周周放射 (PA) 对于有效的治疗计划至关重要.
研究的目的:
- 开发和评估基于深度学习的人工智能 (AI) 模型,用于在周围放射 (PA) 上诊断密度发泄 (DE).
- 将人工智能模型的诊断性能与内牙科医生的评估进行比较.
主要方法:
- 使用了402张PA图像 (138张DE,264张正常) 的数据集.
- 采用了预训练的ResNet模型,在完整的 (F模型) 和切割的 (C模型) 图像上进行了测试.
- 使用曲线下的面积 (AUC) 和科恩的卡帕来评估性能.
主要成果:
- 与内牙医相比,人工智能模型实现了更高的AUC (最佳模型为0.878).
- 在F和C模型之间没有观察到人工智能性能的显著统计差异 (p=0.753).
- 人工智能模型显示了实质性的协议 (Cohen的kappa为F模型的0.774,C模型的0.684),超过了专家的公平协议.
结论:
- 开发的AI模型显示PA图像上的dens evaginatus的高诊断准确性.
- 人工智能显示出增强诊断能力和支持临床医生诊断ED的潜力.
- 改善诊断准确度可以导致更好的长期牙预后.
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