Kosilek R P, Frohner R, Würtz R P, Berr C M, Schopohl J, Reincke M, Schneider H J
Medizinische Klinik und Poliklinik IVLudwig-Maximilians-Universität München, Ziemssenstraße 1, 80336 Munich, GermanyInstitute for Neural ComputationRuhr-Universität Bochum, Bochum, Germany.
Medizinische Klinik und Poliklinik IVLudwig-Maximilians-Universität München, Ziemssenstraße 1, 80336 Munich, GermanyInstitute for Neural ComputationRuhr-Universität Bochum, Bochum, Germany
Eur J Endocrinol. 2015 Oct;173(4):M39-44. doi: 10.1530/EJE-15-0429. Epub 2015 Jul 10.
Cushing's syndrome (CS) and acromegaly are endocrine diseases that are currently diagnosed with a delay of several years from disease onset. Novel diagnostic approaches and increased awareness among physicians are needed. Face classification technology has recently been introduced as a promising diagnostic tool for CS and acromegaly in pilot studies. It has also been used to classify various genetic syndromes using regular facial photographs. The authors provide a basic explanation of the technology, review available literature regarding its use in a medical setting, and discuss possible future developments. The method the authors have employed in previous studies uses standardized frontal and profile facial photographs for classification. Image analysis is based on applying mathematical functions evaluating geometry and image texture to a grid of nodes semi-automatically placed on relevant facial structures, yielding a binary classification result. Ongoing research focuses on improving diagnostic algorithms of this method and bringing it closer to clinical use. Regarding future perspectives, the authors propose an online interface that facilitates submission of patient data for analysis and retrieval of results as a possible model for clinical application.
库欣综合征(CS)和肢端肥大症是目前从疾病发作起数年才得以诊断的内分泌疾病。需要新的诊断方法以及提高医生的认识。面部分类技术最近在试点研究中作为一种用于CS和肢端肥大症的有前景的诊断工具被引入。它也已被用于使用常规面部照片对各种遗传综合征进行分类。作者对该技术进行了基本解释,回顾了有关其在医学环境中使用的现有文献,并讨论了可能的未来发展。作者在先前研究中采用的方法使用标准化的正面和侧面面部照片进行分类。图像分析基于将评估几何形状和图像纹理的数学函数应用于半自动放置在相关面部结构上的节点网格,从而产生二元分类结果。正在进行的研究专注于改进该方法的诊断算法并使其更接近临床应用。关于未来展望,作者提出了一个在线界面,该界面便于提交患者数据进行分析并检索结果,作为临床应用的一种可能模式。