Uncovering Phenotypes in Sensorineural Hearing Loss: A Systematic Review of Unsupervised Machine Learning Approaches.
Lilia Dimitrov, Liam Barrett, Aizaz Chaudhry and 3 others
PMID 40770825WHAT IT FOUND
Unsupervised machine learning can group hearing loss patterns, but the current research is too weak to guide care.
Most studies were low quality, lacked validation, and failed to link clusters to patient outcomes, so these subtypes remain exploratory.
Key findings
01Only seven studies met inclusion criteria, and most were rated low quality with very high risk of bias.
02Machine learning models identified between 4 and 11 clusters, but only two studies linked these clusters to clinical endpoints like treatment response or genotype.
03The review concludes these subtyping approaches remain exploratory and need substantial refinement before they can be safely applied in clinical or research contexts.
STILL TO COME
How it was doneWhat they foundWhat it means for SLPs
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What it does not show
The review included only seven studies, limiting the scope of the analysis. Most included studies had very high risk of bias and low methodological quality. Heterogeneity in data and models prevented a quantitative comparison of clustering methods. Few studies linked identified clusters to meaningful clinical endpoints like treatment response.
Declared interests
The review was part of the first author's PhD, funded by the Medical Research Council (MRC) through a Clinical Research Training Fellowship. The authors declared no conflicts of interest.
The easy way to misread this
Do not interpret the identified clusters as established clinical phenotypes. The review found that most studies were low quality, lacked validation against patient outcomes, and failed to compare methods, so these groupings are not yet ready for clinical use.
Summarised by AI from the full paper, without a clinician reviewing it. Check it against the source before it changes what you do. Read it on PubMed →