Constructing an automatic diagnosis and severity-classification model for acromegaly using facial photographs by deep learning.

Journal: Journal of hematology & oncology
PMID: 32620135

Abstract

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.

Authors

  • Yanguo Kong
    Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, No. 1 Shuaifuyuan Hutong, Dongcheng District, Beijing 100730, China. Electronic address: kongyg@pumch.cn.
  • Xiangyi Kong
    Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, No. 1 Shuaifuyuan Hutong, Dongcheng District, Beijing 100730, China; Department of Breast Surgical Oncology, China National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Chaoyangqu, Panjiayuan-Nanli 17, Beijing 100021, PR China.
  • Cheng He
    Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation, Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China.
  • Changsong Liu
    Department of Electronic Engineering, Tsinghua University, Beijing, 100084, China.
  • Liting Wang
    Department of Electronic Engineering, Tsinghua University, Beijing, 100084, China.
  • Lijuan Su
    College of Computer Science and Technology, Zhejiang University, No. 38 Zheda Road, Hangzhou, Zhejiang 310027, China; Healthcare big data lab, Tencent Technology (Shenzhen) Company Limited, Kejizhongyi Avenue, Hi-tech Park, Nanshan District, Shenzhen, 518057, China.
  • Jun Gao
    Physics of Complex Fluids, MESA+ Institute for Nanotechnology, University of Twente, Enschede 7500 AE, The Netherlands.
  • Qi Guo
    School of Food and Biological Engineering, Jiangsu University, 301 Xuefu Road, Zhenjiang 212013, Jiangsu, China; School of Medicine, Jiangsu University, 301 Xuefu Road, Zhenjiang 212013, Jiangsu, China.
  • Ran Cheng
    Shenzhen Key Laboratory of Computational Intelligence, University Key Laboratory of Evolving Intelligent Systems of Guangdong Province, Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China. Electronic address: ranchengcn@gmail.com.

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