Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, NO.1 Shuaifuyuan Hutong of Dongcheng District, Beijing, 100730, China.
Department of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
J Hematol Oncol. 2020 Jul 3;13(1):88. doi: 10.1186/s13045-020-00925-y.
Due to acromegaly's insidious onset and slow progression, its diagnosis is usually delayed, thus causing severe complications and treatment difficulty. A convenient screening method is imperative. Based on our previous work, we herein developed a new automatic diagnosis and severity-classification model for acromegaly using facial photographs by deep learning on the data of 2148 photographs at different severity levels. Each photograph was given a score reflecting its severity (range 1~3). Our developed model achieved a prediction accuracy of 90.7% on the internal test dataset and outperformed the performance of ten junior internal medicine physicians (89.0%). The prospect of applying this model to real clinical practices is promising due to its potential health economic benefits.
由于肢端肥大症的起病隐匿和进展缓慢，其诊断通常被延误，从而导致严重的并发症和治疗困难。因此，需要一种方便的筛查方法。基于我们之前的工作，我们使用深度学习技术，对 2148 张不同严重程度的面部照片进行分析，建立了一种新的肢端肥大症自动诊断和严重程度分级模型。每张照片都被赋予了一个反映其严重程度的分数（范围为 1~3）。我们开发的模型在内部测试数据集上的预测准确率为 90.7%，优于 10 位初级内科医生（89.0%）的表现。由于该模型具有潜在的健康经济效益，因此有望应用于实际临床实践。
