背景:威廉姆斯-贝伦综合征,努南综合征,和Alagille综合征是常见类型的遗传综合征(GS),其特征是不同的面部特征,肺动脉狭窄,延迟增长。在临床实践中,区分这三个GSs仍然是一个挑战。面部手势作为识别威廉姆斯-贝伦综合征的诊断工具,努南综合征,和Alagille综合征.预训练基础模型(PFM)可以被认为是小规模任务的基础。通过使用基础模型进行预训练,我们提出了识别这些综合征的面部识别模型。
方法:共3297张(n=1666)面部照片取自诊断为Williams-Beuren综合征的儿童(n=174)。努南综合征(n=235),和Alagille综合征(n=51),以及没有GSs的儿童(n=1206)。照片被随机分为五个子集,每个综合征和非GS平均和随机分布在每个子集。训练集和测试集的比例为4:1。ResNet-100架构被用作骨干模型。通过使用基础模型进行预训练,我们构建了两个人脸识别模型:一个利用ArcFace损失函数,另一个使用CosFace损失函数。此外,我们使用相同的架构和损失函数开发了两个模型,但没有预训练。准确性,精度,召回,评价各模型的F1评分。最后,我们将面部识别模型的性能与五名儿科医生的性能进行了比较。
结果:在四个模型中,具有PFM和CosFace损失函数的ResNet-100实现了最佳精度(84.8%)。在相同的损失函数中,PFMs的性能显著提高(ArcFace损失函数从78.5%提高到84.5%,和从79.8%到84.8%的CosFace损失函数)。有和没有PFM,CosFace损失函数模型的性能与ArcFace损失函数模型的性能相似(79.8%vs无PFM的78.5%;PFM的84.8%vs84.5%)。在五名儿科医生中,最高准确度(0.700)是由接受遗传学培训的最高级儿科医生获得的.儿科医生的准确性和F1得分通常低于模型。
结论:基于面部识别的模型有可能改善对三种常见的肺动脉狭窄的GSs的识别。PFM对于构建面部识别的筛选模型可能很有价值。关键信息关于此主题的已知信息:遗传综合征(GS)的早期识别对于肺动脉狭窄(PS)儿童的治疗和预后至关重要。使用卷积神经网络(CNN)进行面部表型分析通常需要大规模的训练数据,限制了其对GSs的有用性。本研究补充的内容:我们使用CNN成功建立了基于人脸识别的多分类模型,以准确识别三种常见的PS关联GS。具有预训练基础模型(PFM)和CosFace损失函数的ResNet-100实现了最佳精度(84.8%)。用基础模型预训练,模型的性能显著提高,尽管损失函数类型的影响似乎很小。这项研究如何影响研究,实践,或策略:基于面部识别的模型有可能改善PS儿童对GS的识别。PFM对于构建用于面部检测的识别模型可能是有价值的。
BACKGROUND: Williams-Beuren syndrome, Noonan syndrome, and Alagille syndrome are common types of genetic syndromes (GSs) characterized by distinct facial features, pulmonary stenosis, and delayed growth. In clinical practice, differentiating these three GSs remains a challenge. Facial gestalts serve as a diagnostic tool for recognizing Williams-Beuren syndrome, Noonan syndrome, and Alagille syndrome. Pretrained foundation models (PFMs) can be considered the foundation for small-scale tasks. By pretraining with a foundation model, we propose facial recognition models for identifying these syndromes.
METHODS: A total of 3297 (n = 1666) facial photos were obtained from children diagnosed with Williams-Beuren syndrome (n = 174), Noonan syndrome (n = 235), and Alagille syndrome (n = 51), and from children without GSs (n = 1206). The photos were randomly divided into five subsets, with each syndrome and non-GS equally and randomly distributed in each subset. The proportion of the training set and the test set was 4:1. The ResNet-100 architecture was employed as the backbone model. By pretraining with a foundation model, we constructed two face recognition models: one utilizing the ArcFace loss function, and the other employing the CosFace loss function. Additionally, we developed two models using the same architecture and loss function but without pretraining. The accuracy, precision, recall, and F1 score of each model were evaluated. Finally, we compared the performance of the facial recognition models to that of five pediatricians.
RESULTS: Among the four models, ResNet-100 with a PFM and CosFace loss function achieved the best accuracy (84.8%). Of the same loss function, the performance of the PFMs significantly improved (from 78.5% to 84.5% for the ArcFace loss function, and from 79.8% to 84.8% for the CosFace loss function). With and without the PFM, the performance of the CosFace loss function models was similar to that of the ArcFace loss function models (79.8% vs 78.5% without PFM; 84.8% vs 84.5% with PFM). Among the five pediatricians, the highest accuracy (0.700) was achieved by the senior-most pediatrician with genetics training. The accuracy and F1 scores of the pediatricians were generally lower than those of the models.
CONCLUSIONS: A facial recognition-based model has the potential to improve the identification of three common GSs with pulmonary stenosis. PFMs might be valuable for building screening models for facial recognition. Key messages What is already known on this topic: Early identification of genetic syndromes (GSs) is crucial for the management and prognosis of children with pulmonary stenosis (PS). Facial phenotyping with convolutional neural networks (CNNs) often requires large-scale training data, limiting its usefulness for GSs. What this study adds: We successfully built multi-classification models based on face recognition using a CNN to accurately identify three common PS-associated GSs. ResNet-100 with a pretrained foundation model (PFM) and CosFace loss function achieved the best accuracy (84.8%). Pretrained with the foundation model, the performance of the models significantly improved, although the impact of the type of loss function appeared to be minimal. How this study might affect research, practice, or policy: A facial recognition-based model has the potential to improve the identification of GSs in children with PS. The PFM might be valuable for building identification models for facial detection.