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下一代表型用于诊断和表型-基因型相关性在卡布基综合征
Quentin Hennocq1,2,3,4,5,6, Marjolaine Willems7, Jeanne Amiel8,9,10
1Imagine Institute, INSERM UMR1163, 75015, Paris, France. quentin.hennocq@aphp.fr.
Scientific reports
|January 28, 2024
概括
人工智能 (AI) 和下一代表型 (NGP) 正在彻底改变形状学. 这项研究开发了一种新的NGP模型,从面部照片中准确预测卡布基综合征 (KS),优于现有的AI解决方案.
科学领域:
- 医学遗传学 医学遗传学
- 人工智能的人工智能
- 计算生物学 计算生物学
背景情况:
- 异形状症的诊断依赖于专家的临床评估.
- 下一代表型 (NGP) 和人工智能 (AI) 为分析面部照片提供了新的工具.
- 卡布基综合征 (KS) 是一种罕见的遗传疾病,具有明显的面部特征,在诊断和亚型区分方面存在挑战.
研究的目的:
- 开发和验证一种新的NGP模型,用于从2D面部照片中预测卡布基综合征 (KS).
- 使用NGP模型,区分KS类型1 (KMT2D相关) 和KS类型2 (KDM6A相关).
- 将开发的NGP模型的性能与现有的AI解决方案 (如DeepGestalt) 进行比较.
主要方法:
- 利用了从1998年至2023年期间634名患者的1448张正面和侧面面部照片的数据集 (107个KS,527个对照).
- 在自动预处理后提取的几何和纹理面部特征.
- 采用XGboost,一个监督的机器学习分类器,结合年龄,性别和种族,并在一个独立的集合上进行验证.
主要成果:
- 在NGP模型中,在验证组中,NGP模型在将KS与对照区分的准确度达到95.8%.
- 该模型将KS类型1与KS类型2区分开来,曲线下的面积 (AUC) 为0.805.
- 开发的KS检测模型表现出高性能,AUC为0.993,准确率为95.8%.
结论:
- 基于人工智能的自动NGP模型可以从面部照片中准确检测卡布基综合征.
- 该模型在区分KS亚型 (KS1和KS2) 中表现出强的表现.
- 这种新的方法超过了当前商业人工智能解决方案和KS诊断专家临床医生的性能.
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