关键词: DeepGestalt Face2Gene computer-aided facial phenotyping tool dysmorphology

Mesh : Child Humans Retrospective Studies Image Processing, Computer-Assisted / methods Syndrome Algorithms Italy

来  源:   DOI:10.1002/ajmg.a.63459

Abstract:
Neurodevelopmental disorders exhibit recurrent facial features that can suggest the genetic diagnosis at a glance, but recognizing subtle dysmorphisms is a specialized skill that requires very long training. Face2Gene (FDNA Inc) is an innovative computer-aided phenotyping tool that analyses patient\'s portraits and suggests 30 candidate syndromes with similar morphology in a prioritized list. We hypothesized that the software could support even expert physicians in the diagnostic workup of genetic conditions. In this study, we assessed the performance of Face2Gene in an Italian dysmorphological pediatrics clinic. We uploaded two-dimensional face pictures of 145 children affected by genetic conditions with typical phenotypic traits. All diagnoses were previously confirmed by cytogenetic or molecular tests. Overall, the software\'s differential included the correct syndrome in most cases (98%). We evaluated the efficiency of the algorithm even considering the rareness of the genetic conditions. All \"common\" diagnoses were correctly identified, most of them with high diagnostic accuracy (93% in top-3 matches). Finally, the performance for the most common pediatric syndromes was calculated. Face2Gene performed well even for ultra-rare genetic conditions (75% within top-3 matches and 83% within top-10 matches). Expert geneticists maybe do not need computer support to recognize common syndromes, but our results prove that the tool can be useful not only for general pediatricians but also in dysmorphological clinics for ultra-rare genetic conditions.
摘要:
神经发育障碍表现出复发性面部特征,可以一目了然地提示遗传诊断,但是识别微妙的畸形是一项需要很长时间训练的专业技能。Face2Gene(FDNAInc)是一种创新的计算机辅助表型工具,可分析患者的肖像,并在优先列表中建议30种形态相似的候选综合征。我们假设该软件甚至可以在遗传条件的诊断工作中支持专家医生。在这项研究中,我们评估了Face2Gene在一家意大利畸形儿科诊所的表现.我们上传了145名受遗传条件影响的儿童的二维人脸图片,这些儿童具有典型的表型特征。所有诊断先前都通过细胞遗传学或分子测试确认。总的来说,在大多数情况下,软件的鉴别包括正确的综合征(98%)。即使考虑到遗传条件的稀缺性,我们也评估了算法的效率。所有“常见”诊断都被正确识别,他们中的大多数具有很高的诊断准确性(前3名比赛中为93%)。最后,计算了最常见儿科综合征的表现.Face2Gene即使在极罕见的遗传条件下也表现良好(前3名匹配中有75%,前10名匹配中有83%)。专家遗传学家可能不需要计算机支持来识别常见综合征,但我们的研究结果证明,该工具不仅对普通儿科医生有用,而且在超罕见遗传病的畸形临床中也有用.
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