人类牙裂纹图像分析与多种深度学习方法
Zheng Li1, Zhongqiang Li1, Ya Zhang1
1Division of Electrical and Computer Engineering, College of Engineering, Louisiana State University, Baton Rouge, LA, 70803, USA.
Annals of biomedical engineering
|September 6, 2024
概括
诊断牙裂是一个挑战. 一种新的近红外光成像技术与深度学习模型相结合,显著提高了检测牙裂的准确性,帮助牙科专业人员.
科学领域:
- 牙科成像 牙科成像 牙科成像
- 人工智能的人工智能
- 生物医学工程 生物医学工程
背景情况:
- 牙裂很常见,难以检测,往往需要及时治疗以防止牙脱落.
- 像X射线这样的传统成像方法在可视化微妙的牙裂方面存在局限性.
- 印ocyanine绿色 (ICG) 辅助近红外光 (NIRF) 成像提供了更好的可视化由于深光透和ICG光.
研究的目的:
- 评估ICG辅助的NIRF牙科成像技术用于检测牙裂的有效性.
- 应用机器学习和深度学习模型来自动分析牙裂纹图像.
- 评估这种综合方法在协助牙医诊断裂方面的潜力.
主要方法:
- 使用NIR成像视频获得了593张破裂和601张非破裂牙图像的数据集.
- 机器学习模型,包括预训练的残余网络和squeezenet1_1,用于图像分类.
- 对象检测 (单拍多盒探测器 - SSD) 和超分辨率 (SR-Generative Adversarial Network) 模型用于裂纹识别和图像增强.
主要成果:
- 分类模型实现了高准确度:残留网络为88.2%,squeezenet1_1为94.25%,用于区分裂纹与非裂纹的牙.
- 固态硬盘模型成功地识别了裂,无论输入图像大小如何.
- 在SR-Generative Adversarial网络中,破解图像的分辨率得到了改进.
结论:
- 深度学习模型显著提高了牙裂识别的效率和准确性.
- 开发的NIR牙科成像系统与深度学习相结合,显示出有很大的潜力,可以帮助牙医诊断牙裂.
- 这项技术可能会导致更早,更可靠地检测牙骨折.
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