Artificial Intelligence in Diagnosis of DFNA9

Study Overview
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Brief Summary
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To study the positive predictive value of Audiogene v.4.0 open source online machine learning tool in accurately predicting DFNA9 (DeaFNess autosomal dominant ninth) as top 3 gene loci in a large series of genetically confirmed c.151C>T,p.Pro51Ser (p.P51S) variant carriers in COCH (coagulation factor C Homology).

Detailed Description
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DFNA9 is an autosomal dominant hereditary adult-onset and progressive sensorineural hearing loss which is associated wit vestibular deterioration. Today, artificial intelligence plays an increasing role in diagnosis of Mendelian hearing losses and in fitting of cochlear implants. An application of this kind is the open source program, Audiogene v4.0, which was elaborated by the Center for Bioinformatics and Computational Biology, University of Iowa City, Iowa, USA. The shape of the audiogram (audioprofile) is easily recognizable in many autosomal dominantly inherited hearing losses. Machine learning based software tools, such as Audiogene v4.0, which was originally developed for prioritizing loci for the Sanger sequencing, could help the clinicians in early diagnosis of DFNA9. This tool only need subjects' age and hearing thresholds (decibel hearing loss (dB HL)) at frequency range of 0.125 - 8 kHz (kiloHerz), left, right or binaural average in order to predict top 3 gene loci according to the data entered in the program. Goal: to use auditory data of a large series of genetically confirmed p.P51S variant carriers causing DFNA9, which were previously collected for the genotype-phenotype correlation study which terminated recently. All individual left and right sided hearing thresholds (ranging from 0.125 to 8kHz, with the exception of 1.5 kHz) as well as binaural averaged thresholds were run through Audiogene v4.0. Descriptive statistics were assessed and statistical analysis was carried out to check for possible differences between age or hearing thresholds between the carrier group with accurate prediction against the carrier group with inaccurate prediction.

Official Title
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Positive Predictive Value of Machine Learning Tools (Audiogene v4.0) for Diagnosing DFNA9 in a Large Series of p.Pro51Ser Variant Carriers in COCH.

Conditions
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DFNA9, Sensorineural Hearing Loss

Intervention / Treatment
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* Diagnostic Test: Pure tone audiometry


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: at least 18 years genetically confirmed c.151 C>T, p.Pro51Ser variant carrier in COCH gene not contra-indication for audiometric testing Exclusion Criteria: <18 years no carrier status for c.151C>T, p.Pro51Ser no auditory data available

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Intervention/Treatment |
| --- |
|Diagnostic Test: Pure tone audiometry|pure tone audiometry|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| hearing threshold | audiometry (pure tone) decibel hearing level (dB HL) left, right ear , binaural average | 1 hour | 
| age | years, age at time of audiometry | 1 hour | 
| prediction gene locus | top 3 gene loci as predicted by Audiogene v4.0 machine learning tool | 1 hour | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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COCH, DFNA9, sensorineural hearing loss