一个混合MMBERT框架来分类牙周骨损失:整合视觉和文本信息
Jennifer Jeyaruby1, Pradeep Kumar Yadalam1
1Department of Periodontics, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, India.
Journal of oral biology and craniofacial research
|January 26, 2026
概括
这项研究引入了一种混合mmBERT框架,用于使用IOPA放射图进行牙周炎诊断. 该模型实现了100%的准确性,改善了牙周骨损失的早期检测.
科学领域:
- 牙科 牙科是指牙科的专业.
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 牙周炎是一种慢性炎症性疾病,导致骨质流失和牙不稳定.
- 牙周炎的早期诊断对于有效治疗至关重要.
- 像mmBERT这样的现有多式联机深度学习模型面临着图像可变性和有限数据的挑战.
研究的目的:
- 提出混合mmBERT框架,以提高牙周炎检测的诊断准确性.
- 为了提高预测牙周骨损失使用口内周周 (IOPA) 放射.
主要方法:
- 使用了150张IOPA图像和专家注释的数据集.
- 临床笔记使用ClinicalBERT进行处理,图像由ResNet50.0进行处理.
- 一个多式BERT (mmBERT) 模型通过交叉模式变压器融合了图像和文本嵌入.
主要成果:
- 混合mmBERT框架显示了预测和基本真相面具之间的强烈重叠.
- 该模型实现了高的交叉对联和边界精度.
- 伯特模型达到了99.11%的训练和100%的测试准确度,表明了强度.
结论:
- 该研究提出了一种使用mmBERT架构的牙周骨损失的新型分类方法.
- 该框架在牙周炎的放射性解释方面显示出有前途的表现.
- 需要进一步的研究来探索其临床应用.
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