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.
Department of Clinical Genetics, Shengjing Hospital of China Medical University, Shenyang 110004, China.
Med Eng Phys. 2023 Nov;121:104064. doi: 10.1016/j.medengphy.2023.104064. Epub 2023 Oct 17.
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.
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.
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 %.
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.
核型分析是细胞遗传学实践中用于早期诊断遗传疾病的重要技术。临床核型分析既繁琐又耗时，且容易出错。我们的研究目的是开发一种基于单阶段深度卷积神经网络（DCNN）的模型，以便能够端到端地自动分类正常和异常染色体。
我们分析了 2424 条正常染色体和 544 条异常染色体。初步开发了支持向量机（SVM）模型来评估数据集上的基本识别性能。然后提出了一种基于 DCNN 的模型来处理相同的数据集。
通过使用 SVM 模型，24 条正常染色体的分类准确率为 86.01%。24 种正常和异常染色体的准确率为 85.37%。基于 DCNN 的模型对 24 条正常染色体分类的准确率为 91.75%。32 种类型分类的准确率为 87.76%。对于八种常见的结构性异常，我们得到的准确率范围为 90.84%至 100%，AUC 值范围为 91.81%至 100%。
我们提出的基于 DCNN 的模型能够有效地进行端到端的核型分类。它有能力作为遗传诊断中异常核型检测和筛查的预测工具，无需初始特征提取。我们相信，我们的工作对于遗传分诊管理具有重要意义，可以降低临床实践中的成本。
