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Uncovering Phenotypes in Sensorineural Hearing Loss: A Systematic Review of Unsupervised Machine Learning Approaches.

Ear and hearing - 1 Jan 2025

Dimitrov Lilia, Barrett Liam, Chaudhry Aizaz, Muzaffar Jameel, Lilaonitkul Watjana, Mehta Nishchay

Abstract excerpt

OBJECTIVES: The majority of the 1.5 billion people living with hearing loss are affected by sensorineural hearing loss (SNHL). Reliably categorizing these individuals into distinct subtypes remains a significant challenge, which is a critical step for developing tailored treatment approaches. Unsupervised machine learning, a branch of artificial intelligence (AI), offers a promising solution to this issue....

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Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

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PMID: 40770825
DOI: 10.1097/AUD.0000000000001696
PMCID: PMC12533775

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PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 8 Sept 2026.

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