Abstract
Hearing loss (HL) is the most common neurodegenerative disease worldwide. Despite its prevalence, clinical testing does not yield a cell or molecular based identification of the underlying etiology of hearing loss making development of pharmacological or molecular treatments challenging. A key to improving the diagnosis of inner ear disorders is the development of reliable biomarkers for different inner ear diseases. Analysis of microRNAs (miRNA) in tissue and body fluid samples has gained significant momentum as a diagnostic tool for a wide variety of diseases. In previous work, we have shown that miRNA profiling in inner ear perilymph is feasible and may demonstrate distinctive miRNA expression profiles unique to different diseases. A first step in developing miRNAs as biomarkers for inner ear disease is linking patterns of miRNA expression in perilymph to clinically available metrics. Using machine learning (ML), we demonstrate we can build disease specific algorithms that predict the presence of sensorineural hearing loss using only miRNA expression profiles. This methodology not only affords the opportunity to understand what is occurring on a molecular level, but may offer an approach to diagnosing patients with active inner ear disease.
Highlights
There are myriad of etiologies that can lead to hearing loss, including: genetic, infectious, noise trauma, and multifactorial disorders such as presbycusis
A total of five out of twelve patients with severe sensorineural hearing loss (SNHL) were classified as having residual hearing (PTA < 80 dB), with a mean pure tone average (PTA) of 69.4 dB (Fig. 1)
As the use of machine learning (ML) to predict inner ear disease using miRNA signatures represents a novel application, we elected to build and test multiple ML models to ascertain if one was superior to others
Summary
There are myriad of etiologies that can lead to hearing loss, including: genetic, infectious, noise trauma, and multifactorial disorders such as presbycusis. An example unsupervised learning would the use of gene expression microarray data to identify molecular subtypes of a given disease, or otherwise groups/clusters of subjects with a similar gene expression profile. We used ML to build disease specific algorithms to predict the presence of sensorineural hearing loss in different inner ear pathologies based on perilymph-derived miRNA expression profiles of the inner ear. We applied our algorithms to de-identified patient samples and established the presence and varying degree of sensorineural hearing loss through miRNA expression profile alone. This methodology offers a promising approach for inner diagnosis, prognosis, and monitoring for various neurotologic diseases. Likewise, using this approach we may be able to understand what may be occurring on a molecular level in inner ear disease specific states in a manner that was previously not possible
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