Medical Genetics for Practicing Obstetrician

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

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.

MLPA as a genetic assay for the prenatal diagnosis of common aneuploidy: the first Egyptian experience

Background

The prenatal diagnosis of syndromes caused by chromosomal abnormality is a long-established part of obstetric care. Several DNA-based molecular approaches have provided rapid prenatal diagnosis of of cytogenomic abnormalities. MLPA has become available for rapid aneuploidy detection of the most common chromosome abnormalities.

Objectives

The aim of this study is to introduce the MLPA technique as a method for the prenatal detection of aneuploidy in Egypt by its validation compared to the FISH technique.

Methods

Fifty AF samples were collected for this study and were subjected to MLPA and FISH assays to detect the most common prenatal chromosomal abnormality.

Results and conclusions

Our study confirmed previous reports that MLPA is analogous to FISH for detecting common aneuploidies and could be a quick and dependable tool for prenatal diagnosis. Therefore, initial prompt testing of AF samples for the copy number of the most common occurring aneuploidies is recommended.