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

doi:10.1016/j.medengphy.2023.104064

	Chromosome classification via deep learning and its application to patients with structural abnormalities of chromosomes
	Yang, Chuan 1,2; Li, Tingting1 ; Dong, Qiulei3,4,5 ; Zhao, Yanyan 1
发表期刊	MEDICAL ENGINEERING & PHYSICS
ISSN	1350-4533
	2023-11-01
卷号	121 页码:11
通讯作者	Zhao, Yanyan(yyzhao@sj-hospital.org)
摘要	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.Methods: We analyzed 2,424 normal chromosomes and 544 abnormal chromosomes. A preliminary support vector machine (SVM) model was developed to evaluate the basic recognition performance on the dataset. A DCNN-based model was then proposed to process the same dataset.Results: By utilizing the SVM model, the classification accuracy of 24 normal chromosomes was 86.01 %. The 32 types of normal and abnormal chromosomes got an accuracy of 85.37 %. The accuracy of the DCNN-based model performing the 24 normal chromosomal classification was 91.75 %. The accuracy of the 32 type classification was 87.76 %. To differentiate eight common structural abnormalities, we obtained accuracies that ranged from 90.84 % to 100 %, and the values of the AUC ranged from 91.81 % to 100 %.Conclusions: Our proposed DCNN-based model effectively performed the karyotype classification in an end-to-end manner. It had the competence to be used as a prediction tool for abnormal karyotype detection and screening in genetic diagnosis without initial feature extraction. We believe our work is meaningful for genetic triage management to lower the cost in clinical practice.
关键词	Karyotyping Chromosome classification Machine learning Deep convolutional neural network
DOI	10.1016/j.medengphy.2023.104064
关键词[WOS]	GENETICS ; REGION ; NUMBER
收录类别	SCI
语种	英语
资助项目	National Key Research and Devel-opment Program of China[2021YFC1005300]
项目资助者	National Key Research and Devel-opment Program of China
WOS研究方向	Engineering
WOS类目	Engineering, Biomedical
WOS记录号	WOS:001104003800001
出版者	ELSEVIER SCI LTD
引用统计	被引频次：2[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/55105
专题	多模态人工智能系统全国重点实验室
通讯作者	Zhao, Yanyan
作者单位	1.China Med Univ, Dept Clin Genet, Shengjing Hosp, 36 Sanhao St, Shenyang 110004, Peoples R China 2.China Med Univ, Shengjing Hosp, Dept Cardiol, Shenyang 110004, Peoples R China 3.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artiicial Intelligence Sy, Beijing 100190, Peoples R China 4.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 5.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Yang, Chuan,Li, Tingting,Dong, Qiulei,et al. Chromosome classification via deep learning and its application to patients with structural abnormalities of chromosomes[J]. MEDICAL ENGINEERING & PHYSICS,2023,121:11.
APA	Yang, Chuan,Li, Tingting,Dong, Qiulei,&Zhao, Yanyan.(2023).Chromosome classification via deep learning and its application to patients with structural abnormalities of chromosomes.MEDICAL ENGINEERING & PHYSICS,121,11.
MLA	Yang, Chuan,et al."Chromosome classification via deep learning and its application to patients with structural abnormalities of chromosomes".MEDICAL ENGINEERING & PHYSICS 121(2023):11.