自动图像分析在识别刚果受试者脆性X综合征整体特征方面的效用。
Usefulness of automated image analysis for recognition of the fragile X syndrome gestalt in Congolese subjects.
作者信息
Lubala Toni Kasole, Kayembe-Kitenge Tony, Mubungu Gerrye, Lumaka Aimé, Kanteng Gray, Savage Sarah, Luboya Oscar, Hagerman Randi, Devriendt Koenraad, Lukusa-Tshilobo Prosper
机构信息
Division of Dysmorphology & Birth Defects, Department of Pediatrics, University of Lubumbashi, Democratic Republic of the Congo.
Unit of Toxicology and Environment, School of Public Health, University of Lubumbashi, Democratic Republic of the Congo; Center for Environment and Health, Department of Public Health and Primary Care, KU Leuven, Leuven, Belgium; Higher Institute of Medical Techniques, Lubumbashi, Democratic Republic of the Congo.
出版信息
Eur J Med Genet. 2023 Sep;66(9):104819. doi: 10.1016/j.ejmg.2023.104819. Epub 2023 Jul 31.
BACKGROUND
Computer-aided software such as the facial image diagnostic aid (FIDA) and Face2Gene has been developed to perform pattern recognition of facial features with promising clinical results. The aim of this pilot study was to test Face2Gene's recognition performance on Bantu Congolese subjects with Fragile X syndrome (FXS) as compared to Congolese subjects with intellectual disability but without FXS (non-FXS).
METHOD
Frontal facial photograph from 156 participants (14 patients with FXS and 142 controls) predominantly young-adults to adults, median age 18.9 age range 4-39yo, were uploaded. Automated face analysis was conducted by using the technology used in proprietary software tools called Face2Gene CLINIC and Face2Gene RESEARCH (version 17.6.2). To estimate the statistical power of the Face2Gene technology in distinguishing affected individuals from controls, a cross validation scheme was used.
RESULTS
The similarity seen in the upper facial region (of males and females) is greater than the similarity seen in other parts of the face. Binary comparison of subjects with FXS versus non-FXS and subjects with FXS versus subjects with Down syndrome reveal an area under the curve values of 0.955 (p = 0.002) and 0.986 (p = 0.003).
CONCLUSION
The Face2Gene algorithm is separating well between FXS and Non-FXS subjects.
背景
诸如面部图像诊断辅助工具（FIDA）和Face2Gene等计算机辅助软件已被开发用于面部特征的模式识别，并取得了可观的临床成果。这项初步研究的目的是测试Face2Gene在患有脆性X综合征（FXS）的班图刚果受试者与患有智力障碍但无FXS（非FXS）的刚果受试者上的识别性能。
方法
上传了156名参与者（14名FXS患者和142名对照）的正面面部照片，主要为年轻人到成年人，中位年龄18.9岁，年龄范围4 - 39岁。使用名为Face2Gene CLINIC和Face2Gene RESEARCH（版本17.6.2）的专有软件工具中使用的技术进行自动面部分析。为了估计Face2Gene技术区分受影响个体与对照的统计功效，使用了交叉验证方案。
结果
在（男性和女性的）面部上半部分看到的相似度大于在面部其他部分看到的相似度。FXS受试者与非FXS受试者以及FXS受试者与唐氏综合征受试者的二元比较显示曲线下面积值分别为0.955（p = 0.002）和0.986（p = 0.003）。
结论
Face2Gene算法能够很好地区分FXS和非FXS受试者。
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