背景：轻微的身体异常（MPA）是与胎儿发育中断有关的先天性形态学异常。MPA在22q11.2缺失综合征（22q11DS）和精神病谱系障碍（PS）中很常见，并且可能代表早期胚胎发育的破坏，这可能有助于识别这些疾病中与精神病相关的重叠机制。
方法：这里，从22q11DS（n=150）收集2D数码照片，PS（n=55），通常发育(TD；n=93)个体。使用两种计算机视觉技术对照片进行了分析：（1）DeepGestalt算法（Face2Gene（F2G））技术，以识别遗传介导的面部疾病的存在，和(2)Emotrics-一种定位和测量面部特征的半自动机器学习技术。
结果：F2G可靠地确定了22q11DS患者；PS患者的面部与多种遗传条件相匹配，包括FragileX和22q11DS。所有F2G得分的PCA衍生因子载荷表明与22q11DS和PS相关的独特且重叠的面部模式。与TD相比，22q11DS中眼睛和鼻子的局部面部测量值更小，而PS显示中间测量值。
结论：颅面畸形学22q11DS和PS在亚精神病症状受损或痛苦之前重叠和明显的程度可能使我们能够更可靠地识别处于危险中的年轻人，并且处于早期发展阶段。
BACKGROUND: Minor physical anomalies (MPAs) are congenital morphological abnormalities linked to disruptions of fetal development. MPAs are common in 22q11.2 deletion syndrome (22q11DS) and psychosis spectrum disorders (PS) and likely represent a disruption of early embryologic development that may help identify overlapping mechanisms linked to psychosis in these disorders.
METHODS: Here, 2D digital photographs were collected from 22q11DS (n = 150), PS (n = 55), and typically developing (TD; n = 93) individuals. Photographs were analyzed using two computer-vision techniques: (1) DeepGestalt algorithm (Face2Gene (F2G)) technology to identify the presence of genetically mediated facial disorders, and (2) Emotrics-a semi-automated machine learning technique that localizes and measures facial features.
RESULTS: F2G reliably identified patients with 22q11DS; faces of PS patients were matched to several genetic conditions including FragileX and 22q11DS. PCA-derived factor loadings of all F2G scores indicated unique and overlapping facial patterns that were related to both 22q11DS and PS. Regional facial measurements of the eyes and nose were smaller in 22q11DS as compared to TD, while PS showed intermediate measurements.
CONCLUSIONS: The extent to which craniofacial dysmorphology 22q11DS and PS overlapping and evident before the impairment or distress of sub-psychotic symptoms may allow us to identify at-risk youths more reliably and at an earlier stage of development.
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