基于深度学习的AI模型用于预测8至13岁儿童年轻永久牙中的牙斑
Banu Çiçek Tez1, Yasin Güzel2,3, Bahar Başak Kızıltan Eliaçık4
1Department of Pediatric Dentistry, Faculty of Dentistry, Ankara Medipol University, Ankara 06050, Türkiye.
Children (Basel, Switzerland)
|May 1, 2025
概括
一个新的深度学习模型准确地检测和细分儿童的牙斑,表现优于牙医. 这种人工智能的进步为儿童牙科护理和口腔健康管理提供了更快,更可靠的工具.
科学领域:
- 人工智能在牙科中的应用
- 对于医学成像的深度学习
- 口腔健康诊断 口腔健康诊断
背景情况:
- 牙斑导致牙损伤和牙周炎等严重的口腔健康问题.
- 手动斑块评估具有挑战性,特别是在儿科患者中,由于时间限制和错误的可能性.
- 开发先进的斑块检测方法对于有效的口腔健康管理至关重要.
研究的目的:
- 开发和评估一种深度学习模型,用于检测和细分年轻永久牙中的牙斑.
- 评估开发的AI模型的诊断精度.
- 将AI模型的性能与经验丰富的儿科牙医进行比较.
主要方法:
- 在31名儿科患者 (8-13岁) 的506张牙科图像数据集上训练和评估了6个最先进的模型.
- 由于其卓越的性能,选择了U-Net变压器模型.
- 使用35张测试图像,将U-Net Transformer模型与3名儿科牙医进行了比较.
主要成果:
- 在测试组中,U-Net变压器模型获得了0.7845的交叉与联盟 (IoU) 评分.
- 统计分析 (p值<0.05) 表明深度学习模型的性能在临床上优于经验丰富的儿科牙医.
- 人工智能模型在牙斑块检测和细分方面表现出更高的准确性和可靠性.
结论:
- 人工智能驱动的技术显示出在儿童牙科中改善牙斑块检测和细分的巨大潜力.
- 开发的深度学习模型为手动评估提供了一个有希望,准确和可靠的替代方案.
- 这项技术可以提高儿科口腔健康的诊断能力和患者护理.
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