基于深度学习的预测,骨类II患者需要使用面部配置图像进行骨干手术:人工智能研究
Soodeh Tahmasbi1,2, Hadi Shokoofi3, Kazem Dalaie1,2
1Department of Orthodontics, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Journal of imaging informatics in medicine
|August 14, 2025
概括
这项研究开发了一种AI模型,用于分类侧面面部形状,用于整形手术查. 深度学习模型从个人资料图像中准确识别手术和非手术病例,帮助诊断.
科学领域:
- 人工智能在医学中的应用
- 医学成像分析 医学成像分析
- 矯正牙醫和臉外科手術
背景情况:
- 骨II类缺陷通常需要正体手术,影响面部美学和功能.
- 目前使用脑电量放射的诊断方法耗时且容易变化.
- 人工智能辅助的侧面软组织样本的查正在出现,用于识别下回退.
研究的目的:
- 开发和评估一种深度学习模型,用于将侧面面部照片分类为外科 (S) 和非外科 (NS) 类别.
- 评估模型的准确性,并确定影响分类的关键面部区域.
- 探索人工智能在整形外科的初步查方面的潜力.
主要方法:
- 收集了524张骨II类患者个人资料图像的数据集,并通过专家标记.
- 图像进行了预处理,包括裁剪,移除背景 (U2-Net),抽出轮和调整大小.
- 一个定制的卷积神经网络 (ContourNet) 使用五倍交叉验证被开发和优化.
主要成果:
- ContourNet模型的整体准确率达到了90%.
- 精度为NS的92%和S的88%;回忆为NS的88%和S的92%.
- Saliency 地图表明该模型专注于临床相关的区域,如下和下巴.
结论:
- 深度学习框架显示出作为一种高效,非侵入性工具的潜力,用于初步的整形外科查.
- 该模型准确地分类了手术和非手术侧面面部形状.
- 在临床实施之前,建议对不同种群进行进一步的验证.
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