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基于人工智能的面部表型支持PACS1-,PACS2-和WDR37相关综合征的共享分子轴

Julia Del Rincón¹, Marta Gil-Salvador¹, Cristina Lucia-Campos¹

¹Unit of Clinical Genetics and Functional Genomics, Department of Pharmacology and Physiology, School of Medicine, University of Zaragoza, CIBERER-GCV2 and IIS-Aragon-GIIS062, 50009 Zaragoza, Spain.

International journal of molecular sciences

|August 28, 2025

概括

人工智能面部分析揭示了Schuurs-Hoeijmakers综合征 (SHMS) 和相关疾病的共同面部特征,这表明了共同的分子机制. 这种方法有助于理解极为罕见的遗传疾病.

科学领域:

遗传学
人工智能
医疗诊断

背景情况:

许多罕见遗传疾病的分子基础尚不清楚, 阻碍了诊断和治疗.
鉴定基因与疾病之间的功能关系具有挑战性,特别是在患者数据有限的超罕见综合征中.
疾病模块是功能相关基因的集群,为了解复杂的遗传条件提供了框架.

研究的目的:

研究基于人工智能的面部表型识别在临床相关的超罕见遗传疾病中识别共享的分子机制的实用性.
探索Schuurs-Hoeijmakers综合征 (SHMS) 与PACS2相关疾病 (DEE66) 和WDR37相关疾病 (NOCGUS) 之间的表型和功能关联.

主要方法:

使用DeepGestalt和GestaltMatcher人工智能算法分析了SHMS患者的面部照片.
人工智能算法用于识别SHMS,DEE66和NOCGUS之间的表型融合和共享面部形态.
进行了二进制比较和文献审查,以确认重叠的面特征和潜在的功能关联.

主要成果:

人工智能算法始终认为SHMS是一个独立的实体.
算法经常将DEE66和NOCGUS与SHMS相匹配,这表明他们有共同的面部姿态.
通过二进制比较证实了重叠的面特征,并得到了文献审查的支持,这表明了潜在的功能联系.

结论:

在PACS1,PACS2和WDR37相关的综合征中存在一致的面部相似性.
人工智能驱动的面部表型是发现极为罕见遗传疾病中临床相关关系的宝贵工具.
在有限的病例数据的情况下,这种方法可以帮助识别共享的分子机制和疾病模块.

关键词:

基于人工智能的面部分析脸2基因一个PACS1 在 PACS2 SHMS的使用情况舒尔斯-霍伊迈克斯综合征 WDR37 其他形状变异这样罕见的遗传疾病

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