随着计算机视觉的进步,计算面部分析已成为诊断罕见疾病的强大而有效的工具。这项技术,也称为下一代表型(NGP),在过去的十年里取得了显著的进步。这篇综述论文将介绍三种关键的NGP方法。2014年,Ferry等人。首次提出的临床面部表型空间(CFPS)接受了八种综合征的培训。五年后,Gurovich等人。提议的DeepGestalt,一个深度卷积神经网络,在超过21,000张患有216种疾病的患者图像上训练。它被认为是最先进的疾病分类框架。2022年,Hsieh等人。开发了GestaltMatcher来支持DeepGestalt中不支持的超罕见和新颖疾病。它进一步使得能够分析在给定群组或多种病症中呈现的面部相似性。此外,本文将介绍NGP在变体优先级排序和面部完形轮廓中的用法。尽管NGP方法已经证明了它们在帮助诊断许多疾病方面的能力,许多限制仍然存在。本文将介绍两个未来的方向,以解决两个主要的局限性:实现满足FAIR原则的医学成像数据库的全球协作以及合成患者图像以保护患者隐私。最后,随着越来越多的NGP方法的出现,我们设想,在不久的将来,NGP技术可以帮助临床医生和研究人员从多个方向诊断患者和分析疾病.
With the advances in computer vision, computational facial analysis has become a powerful and effective tool for diagnosing rare disorders. This technology, also called next-generation phenotyping (NGP), has progressed significantly over the last decade. This review paper will introduce three key NGP approaches. In 2014, Ferry et al. first presented Clinical Face Phenotype Space (CFPS) trained on eight syndromes. After 5 years, Gurovich et al. proposed DeepGestalt, a deep convolutional neural network trained on more than 21,000 patient images with 216 disorders. It was considered a state-of-the-art disorder classification framework. In 2022, Hsieh et al. developed GestaltMatcher to support the ultra-rare and novel disorders not supported in DeepGestalt. It further enabled the analysis of facial similarity presented in a given cohort or multiple disorders. Moreover, this article will present the usage of NGP for variant prioritization and facial gestalt delineation. Although NGP approaches have proven their capability in assisting the diagnosis of many disorders, many limitations remain. This article will introduce two future directions to address two main limitations: enabling the global collaboration for a medical imaging database that fulfills the FAIR principles and synthesizing patient images to protect patient privacy. In the end, with more and more NGP approaches emerging, we envision that the NGP technology can assist clinicians and researchers in diagnosing patients and analyzing disorders in multiple directions in the near future.