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. 2023 Jan 25:14:1113095.
doi: 10.3389/fgene.2023.1113095. eCollection 2023.

Preliminary investigation of the diagnosis and gene function of deep learning PTPN11 gene mutation syndrome deafness

Xionghui Wu  1 , Min Huang  1 , Weiqing Huang  2 , Sijun Zhao  1 , Jiang Xie  1 , Guangliang Liu  1 , Shuting Chang  2
Affiliations

Preliminary investigation of the diagnosis and gene function of deep learning PTPN11 gene mutation syndrome deafness

Xionghui Wu et al. Front Genet. .

Abstract

Syndromic deafness caused by PTPN11 gene mutation has gradually come into the public's view. In the past, many people did not understand its application mechanism and role and only focused on non-syndromic deafness, so the research on syndromic deafness is not in-depth and there is a large degree of lack of research in this area. In order to let the public know more about the diagnosis and gene function of deafness caused by PTPN11 gene mutation syndrome, this paper used deep learning technology to study the diagnosis and gene function of deafness caused by syndrome with the concept of intelligent medical treatment, and finally drew a feasible conclusion. This paper provided a theoretical and practical basis for the diagnosis of deafness caused by PTPN11 gene mutation syndrome and the study of gene function. This paper made a retrospective analysis of the clinical data of 85 deaf children who visited Hunan Children's Hospital,P.R. China from January 2020 to December 2021. The conclusion were as follows: Children aged 1-6 years old had multiple syndrome deafness, while children under 1 year old and children aged 6-12 years old had relatively low probability of complex deafness; girls were not easy to have comprehensive deafness, but there was no specific basis to prove that the occurrence of comprehensive deafness was necessarily related to gender; the hearing loss of patients with Noonan Syndrome was mainly characterized by moderate and severe damage and abnormal inner ear and auditory nerve; most of the mutation genes in children were located in Exon1 and Exon3, with a total probability of 57.65%. In the course of the experiment, it was found that deep learning was effective in the diagnosis of deafness with PTPN11 gene mutation syndrome. This technology could be applied to medical diagnosis to facilitate the diagnosis and treatment of more patients with deafness with syndrome. Intelligent medical treatment was also becoming a hot topic nowadays. By using this concept to analyze and study the pathological characteristics of deafness caused by PTPN11 gene mutation syndrome, it not only promoted patients to find diseases in time, but also helped doctors to diagnose and treat such diseases, which was of great significance to patients and doctors. The study of PTPN11 gene mutation syndrome deafness was also of great significance in genetics. The analysis of its genes not only enriched the gene pool, but also provided reference for future research.

Keywords: PTPN11 gene mutation; deep learning; diagnosis and gene function; intelligent medicine; syndrome deafness.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Common comprehensive deafness.
FIGURE 2
FIGURE 2
Common molecular diagnostic techniques for deafness.
FIGURE 3
FIGURE 3
Age profile of patients.
FIGURE 4
FIGURE 4
Gender status of patients.
FIGURE 5
FIGURE 5
Hearing impairment of patients.
FIGURE 6
FIGURE 6
Imaging studies.
See this image and copyright information in PMC

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