Artificial Intelligence in Diagnosis of DFNA9

Overview

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).

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.

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Eligibility

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Outcomes