Diagnostically relevant facial gestalt information from ordinary photos.
Diagnostically relevant facial gestalt information from ordinary photos.
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DOI：
10.7554/elife.02020
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发表时间：
2014-06-24
期刊：
影响因子：
7.7
通讯作者：
Nellåker C
中科院分区：
文献类型：
--
作者：
Ferry Q;Steinberg J;Webber C;FitzPatrick DR;Ponting CP;Zisserman A;Nellåker C
临床遗传学家在诊断遗传性疾病时，颅面特征具有很高的信息量。作为超罕见发育疾病高通量诊断的第一步，我们介绍了一种自动方法，该方法实现了计算机视觉的最新发展。该算法从普通的非临床照片中提取表型信息，并使用机器学习，在多维“临床面部表型空间”中对人类面部畸形进行建模。该空间将患者定位在已知综合征的背景下，从而促进诊断假设的生成。因此，该方法将通过大大缩小（27.6倍）疑似发育障碍患者潜在诊断的搜索空间来帮助临床医生。此外，即使不存在已知的综合征诊断，该临床面部表型空间也允许通过表型对患者进行聚类，从而有助于疾病识别。我们证明，这种方法提供了一种新的方法，通过功能性遗传途径比较从临床测序数据推断致病性遗传变异。DOI：http://dx.doi.org/10.7554/eLife.02020.001罕见的遗传性疾病影响着大约8%的人，其中许多人的症状大大降低了他们的生活质量。基因诊断可以为医生提供无法通过评估临床症状获得的信息，这使他们能够为患者选择更合适的治疗方法。然而，目前只有少数患者接受基因诊断。面部和头骨的改变存在于30-40%的遗传疾病中，这些改变可以帮助医生识别某些疾病，如唐氏综合征或脆性X染色体。Ferry等人扩展了这种方法，训练了一个基于计算机的模型来识别与不同遗传疾病相关的面部异常模式。该模型将从患者面部照片中提取的数据与91种疾病的面部特征数据进行比较，然后提供该个体最可能的诊断列表。该模型使用36个点来描述空间，包括7个用于下巴，6个用于嘴，7个用于鼻子，8个用于眼睛和8个用于眉毛。Ferry等人的这种方法有三个优点。首先，它为临床医生提供了可以帮助他们诊断罕见遗传疾病的信息。其次，它可以缩小患有相同超罕见疾病的患者的可能疾病范围，即使该疾病目前尚不清楚。第三，它可以识别患者群体，这些患者可以对其基因组进行测序，以识别与特定疾病相关的遗传变异。Ferry等人的工作阐述了自动化方法分析面部和头骨形状的基本原理。下一个挑战是将照片与遗传数据整合起来，用于临床环境。DOI：http://dx.doi.org/10.7554/eLife.02020.002网站
Craniofacial characteristics are highly informative for clinical geneticists when diagnosing genetic diseases. As a first step towards the high-throughput diagnosis of ultra-rare developmental diseases we introduce an automatic approach that implements recent developments in computer vision. This algorithm extracts phenotypic information from ordinary non-clinical photographs and, using machine learning, models human facial dysmorphisms in a multidimensional 'Clinical Face Phenotype Space'. The space locates patients in the context of known syndromes and thereby facilitates the generation of diagnostic hypotheses. Consequently, the approach will aid clinicians by greatly narrowing (by 27.6-fold) the search space of potential diagnoses for patients with suspected developmental disorders. Furthermore, this Clinical Face Phenotype Space allows the clustering of patients by phenotype even when no known syndrome diagnosis exists, thereby aiding disease identification. We demonstrate that this approach provides a novel method for inferring causative genetic variants from clinical sequencing data through functional genetic pathway comparisons. DOI:http://dx.doi.org/10.7554/eLife.02020.001 Rare genetic disorders affect around 8% of people, many of whom live with symptoms that greatly reduce their quality of life. Genetic diagnoses can provide doctors with information that cannot be obtained by assessing clinical symptoms, and this allows them to select more suitable treatments for patients. However, only a minority of patients currently receive a genetic diagnosis. Alterations in the face and skull are present in 30–40% of genetic disorders, and these alterations can help doctors to identify certain disorders, such as Down’s syndrome or Fragile X. Extending this approach, Ferry et al. trained a computer-based model to identify the patterns of facial abnormalities associated with different genetic disorders. The model compares data extracted from a photograph of the patient’s face with data on the facial characteristics of 91 disorders, and then provides a list of the most likely diagnoses for that individual. The model used 36 points to describe the space, including 7 for the jaw, 6 for the mouth, 7 for the nose, 8 for the eyes and 8 for the brow. This approach of Ferry et al. has three advantages. First, it provides clinicians with information that can aid their diagnosis of a rare genetic disorder. Second, it can narrow down the range of possible disorders for patients who have the same ultra-rare disorder, even if that disorder is currently unknown. Third, it can identify groups of patients who can have their genomes sequenced in order to identify the genetic variants that are associated with specific disorders. The work by Ferry et al. lays out the basic principles for automated approaches to analyze the shape of the face and skull. The next challenge is to integrate photos with genetic data for use in clinical settings. DOI:http://dx.doi.org/10.7554/eLife.02020.002