Chromosome classification via deep learning and its application to patients with structural abnormalities of chromosomes.

Journal: Medical engineering & physics
PMID: 37985030

Abstract

BACKGROUND AND OBJECTIVE: Karyotyping is an important technique in cytogenetic practice for the early diagnosis of genetic diseases. Clinical karyotyping is tedious, time-consuming, and error-prone. The objective of our study was to develop a single-stage deep convolutional neural networks (DCNN)-based model to automatically classify normal and abnormal chromosomes in an end-to-end manner.

Authors

  • Chuan Yang
    Department of Clinical Genetics, Shengjing Hospital of China Medical University, Shenyang 110004, China; Department of Cardiology, Shengjing Hospital of China Medical University, Shenyang 110004, China.
  • Tingting Li
    Key Laboratory of Biotechnology and Bioresources Utilization (Dalian Minzu University), Ministry of Education, Dalian, China.
  • Qiulei Dong
    National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China; and CAS Center for Excellence in Brain Science and Intelligence Technology, Beijing 100190, China qldong@nlpr.ia.ac.cn.
  • Yanyan Zhao
    Department of Clinical Genetics, Shengjing Hospital of China Medical University, Shenyang 110004, China. Electronic address: yyzhao@sj-hospital.org.

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