Automatic differentiation of thyroid scintigram by deep convolutional neural network: a dual center study.

Journal: BMC medical imaging
PMID: 34823482

Abstract

BACKGROUND: Tc-pertechnetate thyroid scintigraphy is a valid complementary avenue for evaluating thyroid disease in the clinic, the image feature of thyroid scintigram is relatively simple but the interpretation still has a moderate consistency among physicians. Thus, we aimed to develop an artificial intelligence (AI) system to automatically classify the four patterns of thyroid scintigram.

Authors

  • Pei Yang
    Department of Bone and Joint Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
  • Yong Pi
    Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, 610065, PR China.
  • Tao He
  • Jiangming Sun
    Hit Discovery, Discovery Sciences, IMED Biotech Unit , AstraZeneca , Pepparedsleden 1 , 43153 Mölndal , Sweden.
  • Jianan Wei
    Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, 610065, People's Republic of China.
  • Yongzhao Xiang
    Department of Nuclear Medicine, West China Hospital of Sichuan University, Chengdu 610041, PR China.
  • Lisha Jiang
    Laboratory of Clinical Nuclear Medicine, Department of Nuclear Medicine, West China Hospital of Sichuan University, No. 37 Guo Xue Alley, Chengdu, 610041, People's Republic of China.
  • Lin Li
    Department of Medicine III, LMU University Hospital, LMU Munich, Munich, Germany.
  • Zhang Yi
  • Zhen Zhao
  • Huawei Cai
    Department of Nuclear Medicine, West China Hospital of Sichuan University, Chengdu 610041, PR China. Electronic address: hw.cai@yahoo.com.

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