深度学习模型在冠状束计算机断层扫描图像中检测辅助骨的准确性
Shishir Shetty1, Wael Talaat2, Natheer Al-Rawi2
1Department of Oral and Craniofacial Health Sciences, College of Dental medicine, University of Sharjah, Sharjah, United Arab Emirates. shishirshettyomr@gmail.com.
Scientific reports
|March 11, 2025
概括
这项研究表明,深度学习模型,特别是ResNet101v2,可以准确地检测束计算机断层扫描 (CBCT) 图像中的辅助骨 (AO). 这一进步有助于通过先进的放射学分析识别鼻病理.
科学领域:
- 医疗成像医学成像
- 放射学中的人工智能
- 耳鼻喉科 耳鼻喉科 耳鼻喉科
背景情况:
- 附属骨 (AO) 是上鼻的显著解剖变异,经常与鼻病理有关.
- 放射性成像对于AO检测至关重要,但深度学习在面成像中的这个特定目的的应用仍然未被探索.
- 现有的研究还没有调查卷积神经网络 (CNN) 在从X光片中检测AO的有效性.
研究的目的:
- 评估深度学习模型在冠状束计算机断层扫描 (CBCT) 图像中检测辅助骨 (AO) 的准确性.
- 解决关于CNN在放射性成像中AO检测的有效性方面的知识差距.
主要方法:
- 一个数据集由454张冠状CBCT图像 (227张带有AO,227张没有AO) 来自856张大视野CBCT扫描.
- 图像进行了预处理和增强,共创建了1260张图像,用于模型训练和验证.
- 使用了三种预先训练的CNN模型 (VGG16,MobileNetV2,ResNet101v2),ResNet101v2被选择用于使用L1调整进行微调.
主要成果:
- 该ResNet101v2模型实现了0.81的测试准确度和0.51的损失.
- 性能指标包括精度 (0.82),回忆 (0.81),F1得分 (0.81) 和AUC (0.87),表明强大的检测能力.
- ResNet101v2在从二维冠状CBCT图像中识别辅助骨方面表现出很好的准确性.
结论:
- 深度学习,特别是ResNet101v2模型,显示出在冠状CBCT图像中准确检测辅助骨的重大前景.
- 这项研究强调了人工智能的潜力,提高了对大脸部放射图像的解读,以了解解剖变异.
- 未来的研究应该探索深度学习模型在3D CBCT重建中检测AO的有效性.
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