Implementing next-generation sequencing for diagnosis and management of hereditary hearing impairment: a comprehensive review.
Level 5 - mechanism / opinion, no new human data
Narrative review without systematic search or original human data
PubMed 39194060 · doi:10.1080/14737159.2024.2396866
What was done
This narrative review synthesized literature on the genetic causes of sensorineural hearing impairment (SNHI) and hereditary hearing impairment (HHI). The authors examined the clinical utility of different next-generation sequencing (NGS) modalities—including targeted gene panels, whole-exome sequencing, and whole-genome sequencing—in newborn screening, genetic counseling, prognostic prediction, and personalized management, while evaluating implementation challenges and emerging diagnostic technologies.
What was found
The abstract reports no numerical data, diagnostic yields, or statistical comparisons. It qualitatively notes that NGS provides high-throughput screening and sensitive detection of genetic etiologies of SNHI to guide clinical decisions. Key unresolved challenges identified include the trade-off between cost and diagnostic yield, difficulties detecting structural variants, and the need to interpret non-coding variants.
Why it matters
This review outlines the clinical integration of genomic sequencing for hereditary hearing impairment and identifies technological adjuncts—such as long-read sequencing and machine learning—needed to improve diagnostic rates.
Limits
As a narrative review, it presents expert perspective without systematic literature selection, original experimental data, or quantitative synthesis. The abstract provides no specific figures on test sensitivity, specificity, diagnostic yield across modalities, or clinical outcome differences.
Cited by
- context Machine learning and AI tools developed at Stanford in collaboration with Google can classify genetic variants of unknown significance in deafness genes, raising genetic diagnostic yield from 50% to 80%.