Abstract：
With recent advances in computer vision, many applications based on artificial intelligence have been developed to facilitate the diagnosis of rare genetic disorders through the analysis of patients\' two-dimensional frontal images. Some of these have been implemented on online platforms with user-friendly interfaces and provide facial analysis services, such as Face2Gene. However, users cannot run the facial analysis processes in house because the training data and the trained models are unavailable. This article therefore provides an introduction, designed for users with programming backgrounds, to the use of the open-source GestaltMatcher approach to run facial analysis in their local environment. The Basic Protocol provides detailed instructions for applying for access to the trained models and then performing facial analysis to obtain a prediction score for each of the 595 genes in the GestaltMatcher Database. The prediction results can then be used to narrow down the search space of disease-causing mutations or further connect with a variant-prioritization pipeline. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol: Using the open-source GestaltMatcher approach to perform facial analysis.
摘要：
随着计算机视觉的最新进展，已经开发了许多基于人工智能的应用程序，以通过分析患者的二维额叶图像来促进罕见遗传疾病的诊断。其中一些已经在具有用户友好界面的在线平台上实现，并提供面部分析服务，比如Face2Gene。然而,用户无法在内部运行面部分析流程，因为训练数据和训练的模型不可用。因此，本文提供了一个介绍，专为具有编程背景的用户设计，使用开源GestaltMatcher方法在其本地环境中运行面部分析。基本协议提供了详细的说明，用于申请访问经过训练的模型，然后执行面部分析以获得GestaltMatcher数据库中595个基因中每个基因的预测分数。然后，预测结果可用于缩小致病突变的搜索空间，或进一步与变体优先排序管道连接。©2023作者。WileyPeriodicalsLLC出版的当前协议。基本协议：使用开源GestaltMatcher方法进行面部分析。
