Method and device for detecting chromosome structure abnormality based on deep learning

Abstract

The invention provides a chromosome structure anomaly detection method and device based on deep learning. The detection method comprises the following steps: acquiring chromosome image data of a to-be-diagnosed user; according to the chromosome image data, obtaining a feature matrix of each chromosome through monomer sequence data, type data and stripe number data of sister dyeing monomers of each chromosome; obtaining a difference matrix representing the difference between the homologous chromosome pairs based on the two feature matrixes of the homologous chromosomes; and at least based on the difference matrix of the homologous chromosome pairs of various types in the at least one cell, judging whether the chromosome of the type of the user to be diagnosed has structural abnormality or not. According to the method, the chromosomes are represented through the feature matrix, and the difference between the homologous chromosome pairs is represented through the difference matrix, so that whether the chromosome structure abnormality exists in the user or not can be judged according to the difference matrix through deep learning, and the screening efficiency of the chromosome structure abnormality can be greatly improved.

Description

technical field [0001] The present invention relates to the detection of chromosome structural abnormalities, in particular to a deep learning-based detection method and device for chromosome structural abnormalities. Background technique [0002] Chromosomal abnormalities, including deletions, duplications or irregularities of chromosomal DNA, are the underlying cause of various genetic diseases. About 0.6% of live births have chromosomal abnormalities, which often lead to deformities and / or developmental disabilities. Diseases caused by chromosomal abnormalities can have serious consequences, such as 25% of miscarriages and stillbirths due to chromosomal abnormalities, and 50-60% of miscarriages in the first trimester. With the help of chromosomal abnormality detection, clinicians can identify all abnormalities that may lead to birth defects. According to the general understanding of chromosomal abnormalities, they can be roughly divided into two types: quantitative abno...

AI Technical Summary

Problems solved by technology

[0006] As described above, in order to solve the problem of complex and time-consuming manual detection of abnormal chromosome structure in the prior art, the present invention provides a method and device for detecting abnormal chromosome structure based on deep learning to realize chromosome abnormality by means of deep learning algorithms. Automatic screening of structural abnormalities, which can effectively improve the screening efficiency of chromosome structural abnormalities

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