Intelligent Medicine (Nov 2022)

Study design of deep learning based automatic detection of cerebrovascular diseases on medical imaging: a position paper from Chinese Association of Radiologists

  • Longjiang Zhang,
  • Zhao Shi,
  • Min Chen,
  • Yingmin Chen,
  • Jingliang Cheng,
  • Li Fan,
  • Nan Hong,
  • Wenxiao Jia,
  • Guihua Jiang,
  • Shenghong Ju,
  • Xiaogang Li,
  • Xiuli Li,
  • Changhong Liang,
  • Weihua Liao,
  • Shiyuan Liu,
  • Zaiming Lu,
  • Lin Ma,
  • Ke Ren,
  • Pengfei Rong,
  • Bin Song,
  • Gang Sun,
  • Rongpin Wang,
  • Zhibo Wen,
  • Haibo Xu,
  • Kai Xu,
  • Fuhua Yan,
  • Yizhou Yu,
  • Yunfei Zha,
  • Fandong Zhang,
  • Minwen Zheng,
  • Zhen Zhou,
  • Wenzhen Zhu,
  • Guangming Lu,
  • Zhengyu Jin

Journal volume & issue
Vol. 2, no. 4
pp. 221 – 229

Abstract

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In recent years, with the development of artificial intelligence, especially deep learning technology, researches on automatic detection of cerebrovascular diseases on medical images have made tremendous progress and these models are gradually entering into clinical practice. However, because of the complexity and flexibility of the deep learning algorithms, these researches have great variability on model building, validation process, performance description and results interpretation. The lack of a reliable, consistent, standardized design protocol has, to a certain extent, affected the progress of clinical translation and technology development of computer aided detection systems. After reviewing a large number of literatures and extensive discussion with domestic experts, this position paper put forward recommendations of standardized design on the key steps of deep learning-based automatic image detection models for cerebrovascular diseases. With further research and application expansion, this position paper would continue to be updated and gradually extended to evaluate the generalizability and clinical application efficacy of such tools.

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