通过面部识别软件和深度学习来评估肌痛神经衰弱的面部弱点
Annabel M Ruiter1, Ziqi Wang2, Zhao Yin2
1Department of Neurology, Leiden University Medical Center, Leiden, the Netherlands.
Annals of clinical and translational neurology
|June 9, 2023
概括
用人工智能可以自动检测肌痛神经衰弱 (MG) 的面部软弱. 一个深度学习模型准确地诊断MG,并通过面部表情评估疾病的严重程度.
科学领域:
- 神经学 神经学
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 骨髓灰质炎 (MG) 是一种自身免疫性疾病,导致疲劳性肌肉衰弱,通常影响眼睛和肌肉.
- 面部肌肉衰弱是MG患者经常出现但往往没有量化的症状.
研究的目的:
- 通过使用基于计算机的方法来研究MG中面部软弱的自动定量.
- 评估面部表情识别软件和MG诊断和疾病严重程度分类的深度学习 (DL) 模型的实用性.
主要方法:
- 一项涉及70名MG患者和69名健康对照 (HC) 的视频记录的横截面研究.
- 使用面部表情识别软件量化面部弱点.
- 培训和验证DL模型,用于使用视频数据的交叉验证对MG诊断和疾病严重程度进行分类.
主要成果:
- 与HC相比,MG患者表现出明显减少的愤怒,恐惧和幸福的表达.
- 在训练和验证组中,DL模型在诊断MG (AUC 0.75-0.82) 和分类疾病严重程度 (AUC 0.75-0.88) 中实现了高精度.
- 经验证的模型性能显示,诊断的敏感度为100%,严重程度为94%,具有高特异性.
结论:
- 面部识别软件可以检测到MG中面部软弱的特征模式.
- 这项研究提供了一个DL模型的概念证明,该模型能够区分MG和健康个体,并根据面部运动分析对疾病严重程度进行分级.
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