// Na Shen 1 , Jing Peng 1 , Xiong Wang 1 , Yaowu Zhu 1 , Weiyong Liu 1 , Aiguo Liu 2 and Yanjun Lu 1 1 Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China 2 Department of Otorhinolaryngology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China Correspondence to: Aiguo Liu, email: aiguoliu309@163.com Yanjun Lu, email: junyanlu_2000@163.com Keywords: hearing loss (HL), GJB2 , p.V37I, pedigree analysis, meta-analysis Received: February 03, 2017      Accepted: March 19, 2017      Published: April 21, 2017 ABSTRACT Pathogenic variants in the gap junction protein beta-2 ( GJB2 ) gene are the most common cause of hearing loss. Of these, the p.V37I variant of GJB2 has a high allele frequency (up to 10%) in East Asians. Characterization of the phenotypic spectrum associated with p.V37I, as well as the role of this variant in the onset of hearing loss could have a remarkable effect on future diagnostic strategies. Here, we performed a pedigree analysis of unrelated families exhibiting various hearing phenotypes, and then conducted a meta-analysis to comprehensively assess the association between the p.V37I and the risk of hearing loss. Pedigree analyses showed that both homozygous p.V37I variants, as well as compound heterozygous p.V37I with other GJB2 pathogenic variants, contributed to various phenotypes of hearing loss. Meanwhile, meta-analysis demonstrated that, compared with those in the wild type group, both p.V37I homozygotes and compound heterozygous p.V37I variants were at significantly higher risk of developing hearing loss (odds ratios = 7.14 and 3.63; 95% confidence intervals = 3.01-16.95 and 1.38–9.54, respectively). Conversely, heterozygous p.V37I variants alone did not increase the risk of hearing loss. Given the high allele carriage rate of p.V37I (up to 10%) within the general population, our work not only provides information that might influence future genetic screening policies, but also offers insight into clinical risk evaluation and genetic counseling regarding hearing loss.

/pdf/association-between-the-p-v37i-variant-of-gjb2-and-hearing-3b14z0e4h5.pdf

Association between the p.V37I variant of GJB2 and hearing loss: a pedigree and meta-analysis.

Due primarily to the excellent soft tissue contrast depictions provided by MRI, the widespread application of head and neck MRI in clinical practice serves to assess various diseases. Artificial intelligence (AI)-based methodologies, particularly deep learning analyses using convolutional neural networks, have recently gained global recognition and have been extensively investigated in clinical research for their applicability across a range of categories within medical imaging, including head and neck MRI. Analytical approaches using AI have shown potential for addressing the clinical limitations associated with head and neck MRI. In this review, we focus primarily on the technical advancements in deep-learning-based methodologies and their clinical utility within the field of head and neck MRI, encompassing aspects such as image acquisition and reconstruction, lesion segmentation, disease classification and diagnosis, and prognostic prediction for patients presenting with head and neck diseases. We then discuss the limitations of current deep-learning-based approaches and offer insights regarding future challenges in this field. 

Current State of Artificial Intelligence in Clinical Applications for Head and Neck MR Imaging

Autosomal recessive (AR) non-syndromic hearing loss (NSHL) is the most common form of hereditary deafness. Mutations in the gap junction protein beta 2 (GJB2) gene encoding connexin 26 (Cx26) account for about 50% of cases of ARNSHL. In the current study, a combination of exome sequencing and Sanger sequencing in a Chinese Dong family with ARNSHL allowed identification of a novel compound heterozygous mutation c.240G>C(p. Q80H)/C.109G>A(p.V37I) in exon 2 of the GJB2 gene, which co-segregated with the disease phenotype in this family and was not evident in 100 healthy controls. Bioinformatic analysis revealed that the two mutations in the GJB2 gene were probably pathogenic. Results indicated that the compound heterozygous variants, p.Q80H and p.V37I, in the GJB2 gene are associated with ARNSHL. The Q80H variant was initially identified in patients of Dong Chinese origin with NSHL. The current results broaden the spectrum of GJB2 mutations responsible for NSHL and have important implications for molecular diagnosis, treatment, and genetic counseling for this family.

A novel compound heterozygous mutation in the GJB2 gene is associated with non-syndromic hearing loss in a Chinese family.

A common infectious disease, otitis media (OM) has a low rate of early diagnosis, which significantly increases the difficulty of treating the disease and the likelihood of serious complications developing including hearing loss, speech impairment, and even intracranial infection. Several areas of healthcare have shown great promise in the application of artificial intelligence (AI) systems, such as the accurate detection of diseases, the automated interpretation of images, and the prediction of patient outcomes. Several articles have reported some machine learning (ML) algorithms such as ResNet, InceptionV3 and Unet, were applied to the diagnosis of OM successfully. The use of these techniques in the OM is still in its infancy, but their potential is enormous. We present in this review important concepts related to ML and AI, describe how these technologies are currently being applied to diagnosing, treating, and managing OM, and discuss the challenges associated with developing AI-assisted OM technologies in the future.

https://www.mdpi.com/2075-4418/13/13/2309/pdf?version=1688981775

Diagnosis, Treatment, and Management of Otitis Media with Artificial Intelligence

Background Barakat syndrome is an autosomal dominant disorder characterized by the triad of hypoparathyroidism, sensorineural deafness, and renal anomalies and is caused by mutations in GATA3 gene. SLC34A3 is the cause gene of hypophosphatemic rickets with hypercalciuria, and heterozygous carriers may have milder clinical symptoms. The aim of this study was to identify the underlying genetic cause of a patient who initially presented with renal failure, hypercalciuria, kidney stone, and bilateral sensorineural deafness. Methods A 6-year-old boy with complex clinical presentations was investigated. Comprehensive medical evaluations were performed including auditory function tests, endocrine function tests, metabolic studies, and imaging examinations. Molecular diagnoses were analyzed by trio whole-exome sequencing. Results One novel de novo deleterious variant (c. 324del) of the GATA3 gene was identified in the patient. The patient can be diagnosed with Barakat syndrome. In addition, one novel variant (c. 589A>G) of the SLC34A3 gene was detected, which was inherited from the father. This heterozygous variant can explain the hypercalciuria and kidney stone that occurred in both the patient and his father. Conclusion This study provides a special case which is phenotype-driven dual diagnoses, and the two novel variants can parsimoniously explain the complex clinical presentations of this patient.

Novel heterozygous GATA3 and SLC34A3 variants in a 6‐year‐old boy with Barakat syndrome and hypercalciuria

In order to compare magnetic resonance imaging (MRI) findings of patients with large vestibular aqueduct syndrome (LVAS) in the stable hearing loss (HL) group and the fluctuating HL group, this paper provides reference for clinicians' early intervention. From January 2001 to January 2016, patients with hearing impairment diagnosed as LVAS in infancy in the Department of Otorhinolaryngology, Head and Neck Surgery, Children's Hospital of Fudan University were collected and divided into the stable HL group (n = 29) and the fluctuating HL group (n = 30). MRI images at initial diagnosis were collected, and various deep learning neural network training models were established based on PyTorch to classify and predict the two series. Vgg16_bn, vgg19_bn, and ResNet18, convolutional neural networks (CNNs) with fewer layers, had favorable effects for model building, with accs of 0.9, 0.8, and 0.85, respectively. ResNet50, a CNN with multiple layers and an acc of 0.54, had relatively poor effects. The GoogLeNet-trained model performed best, with an acc of 0.98. We conclude that deep learning-based radiomics can assist doctors in accurately predicting LVAS patients to classify them into either fluctuating or stable HL types and adopt differentiated treatment methods.

/pdf/prediction-of-hearing-prognosis-of-large-vestibular-aqueduct-3g7s00qt.pdf

Prediction of Hearing Prognosis of Large Vestibular Aqueduct Syndrome Based on the PyTorch Deep Learning Model

The homozygote p.V27I/p.E114G variant of GJB2 is a putative indicator of nonsyndromic hearing loss in Chinese infants

Short-term outcomes of infants with hyperbilirubinemia-associated auditory neuropathy spectrum disorder in neonatal intensive care unit.

OBJECTIVE
To investigate the diagnostic value of deep learning (DL) in differentiating otitis media (OM) caused by otitis media with effusion (OME) and primary ciliary dyskinesia (PCD), so as to provide reference for early intervention.


METHODS
From January 2010 to January 2021, 31 patients with PCD who had temporal bone computed tomography (TBCT) in the Children's Hospital of Fudan University were retrospectively analyzed. Another 30 age-matched cases of OME with TBCT were collected as the control group. The CT imaging signatures of children were observed. Besides, a variety of DL neural network training models were established based on PyTorch, and the optimal models were trained and selected for PCD screening.


RESULTS
The google net-trained model worked best, with an accuracy of 0.99. Vgg16_bn, vgg19_bn, resnet18, and resnet34; having neural networks with fewer layers, better model effects, with an accuracy rate of 0.86, 0.9, 0.86, and 0.86, respectively. Resnet50 and other neural networks with more layers had relatively poor results.


CONCLUSION
DL-based CT radiomics can accurately distinguish OM caused by OME from that induced by PCD, which can be used for screening the PCD.

Temporal bone CT-based deep learning models for differential diagnosis of primary ciliary dyskinesia related otitis media and simple otitis media with effusion.

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