Chromosome classification via deep learning and its application to patients with structural abnormalities of chromosomes
文献类型：期刊论文
| 作者 | Yang, Chuan1,2; Li, Tingting1 ; Dong, Qiulei3,4,5; Zhao, Yanyan1
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| 刊名 | MEDICAL ENGINEERING & PHYSICS
 |
| 出版日期 | 2023-11-01 |
| 卷号 | 121页码:11 |
| ISSN号 | 1350-4533 |
| 关键词 | Karyotyping Chromosome classification Machine learning Deep convolutional neural network |
| DOI | 10.1016/j.medengphy.2023.104064 |
| 通讯作者 | Zhao, Yanyan(yyzhao@sj-hospital.org) |
| 英文摘要 | 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. |
| WOS关键词 | GENETICS ; REGION ; NUMBER |
| 资助项目 | National Key Research and Devel-opment Program of China[2021YFC1005300] |
| WOS研究方向 | Engineering |
| 语种 | 英语 |
| 出版者 | ELSEVIER SCI LTD |
| WOS记录号 | WOS:001104003800001 |
| 资助机构 | National Key Research and Devel-opment Program of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/55104]  |
| 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. |
入库方式： OAI收割
来源：自动化研究所
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