*Result*: Uncovering Phenotypes in Sensorineural Hearing Loss: A Systematic Review of Unsupervised Machine Learning Approaches.
Title:
Uncovering Phenotypes in Sensorineural Hearing Loss: A Systematic Review of Unsupervised Machine Learning Approaches.
Authors:
Dimitrov L; University College London Hospital (UCLH) Biomedical Research Centre (BRC) Hearing Theme, London, United Kingdom.; University College London (UCL), London, United Kingdom., Barrett L; University College London Hospital (UCLH) Biomedical Research Centre (BRC) Hearing Theme, London, United Kingdom.; University College London (UCL), London, United Kingdom., Chaudhry A; University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom., Muzaffar J; University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom., Lilaonitkul W; University College London (UCL), London, United Kingdom., Mehta N; University College London Hospital (UCLH) Biomedical Research Centre (BRC) Hearing Theme, London, United Kingdom.; University College London (UCL), London, United Kingdom.
Source:
Ear and hearing [Ear Hear] 2025 Nov-Dec 01; Vol. 46 (6), pp. 1401-1411. Date of Electronic Publication: 2025 Aug 07.
Publication Type:
Journal Article; Systematic Review
Language:
English
Journal Info:
Publisher: Williams And Wilkins Country of Publication: United States NLM ID: 8005585 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1538-4667 (Electronic) Linking ISSN: 01960202 NLM ISO Abbreviation: Ear Hear Subsets: MEDLINE
Imprint Name(s):
Publication: Baltimore Md : Williams And Wilkins
Original Publication: Baltimore, Williams & Wilkins.
Original Publication: Baltimore, Williams & Wilkins.
MeSH Terms:
References:
Allen P. D., Eddins D. A. (2010). Presbycusis phenotypes form a heterogeneous continuum when ordered by degree and configuration of hearing loss. Hear Res, 264, 10–20.
Alpaydin E. (2014). Introduction to Machine Learning (Adaptive Computation and Machine Learning).
Angelini E. D., Yang J., Balte P. P., Hoffman E. A., Manichaikul A. W., Sun Y., Shen W., Austin J. H., Allen N. B., Bleecker E. R., Bowler R. (2023). Pulmonary emphysema subtypes defined by unsupervised machine learning on CT scans. Thorax, 78, 1067–1079.
Anwar M. N., Oakes M. P. (2012). Data mining of audiology patient records: Factors influencing the choice of hearing aid type. BMC Med Inform Decis Mak, 12(Suppl 1), S6.
Banerjee A., Dashtban A., Chen S., Pasea L., Thygesen J. H., Fatemifar G., Hemingway H., Dyszynski T., Asselbergs F. W., Lund L. H., Lumbers T., Denaxas S., Hemingway H. (2023). Identifying subtypes of heart failure from three electronic health record sources with machine learning: An external, prognostic, and genetic validation study. Lancet Digital Health, 5, e370–e379.
Basile A. O., Ritchie M. D. (2018). Informatics and machine learning to define the phenotype. Expert Rev Mol Diagn, 18, 219–226.
Bisgaard N., Vlaming M. S., Dahlquist M. (2010). Standard audiograms for the IEC 60118-15 measurement procedure. Trends Amplif, 14, 113–120.
Carhart R. (1945). An improved method for classifying audiograms. Laryngoscope, 55, 640–662.
Chang Y. S., Yoon S. H., Kim J. R., Baek S. Y., Cho Y. S., Hong S. H., Moon I. J., Moon I. J. (2019). Standard audiograms for Koreans derived through hierarchical clustering using data from the Korean National Health and Nutrition Examination Survey 2009-2012. Sci Rep, 9, 3675.
Chen R. J., Lu M. Y., Chen T. Y., Williamson D. F. K., Mahmood F. (2021). Synthetic data in machine learning for medicine and healthcare. Nat Biomed Eng, 5, 493–497.
Cruickshanks K. J., Nondahl D. M., Fischer M. E., Schubert C. R., Tweed T. S. (2020). A novel method for classifying hearing impairment in epidemiological studies of aging: The Wisconsin Age-Related Hearing Impairment Classification Scale. Am J Audiol, 29, 59–67.
Dadu A., Satone V., Kaur R., Hashemi S. H., Leonard H., Iwaki H., Faghri F., Billingsley K. J., Bandres-Ciga S., Sargent L. J., Noyce A. J., Daneshmand A., Blauwendraat C., Marek K., Scholz S. W., Singleton A. B., Nalls M. A., Campbell R. H., Faghri F. (2022). Identification and prediction of Parkinson’s disease subtypes and progression using machine learning in two cohorts. Npj Parkinsons Dis, 8, 172.
Dubno J. R., Eckert M. A., Lee F. S., Matthews L. J., Schmiedt R. A. (2013). Classifying human audiometric phenotypes of age-related hearing loss from animal models. J Assoc Res Otolaryngol, 14, 687–701.
Elkhouly A., Andrew A. M., Rahim H. A., Abdulaziz N., Abdulmalek M., Mohd Yasin M. N., Siddique S., Sabapathy T., Siddique S. (2022). A novel unsupervised spectral clustering for pure-tone audiograms towards hearing aid filter bank design and initial configurations. Appl Sci, 12, 298.
Eshaghi A., Young A. L., Wijeratne P. A., Prados F., Arnold D. L., Narayanan S., Ciccarelli O., Barkhof F., Alexander D. C., Thompson A. J., Chard D., Ciccarelli O. (2021). Identifying multiple sclerosis subtypes using unsupervised machine learning and MRI data. Nat Commun, 12, 2078.
Fleuren L. M., Thoral P., Shillan D., Ercole A., Elbers P. W. G.; Right Data Right Now Collaborators. (2020). Machine learning in intensive care medicine: Ready for take-off? Intensive Care Med, 46, 1486–1488.
Gomaa N. A., Jimoh Z., Campbell S., Zenke J. K., Szczepek A. J. (2020). Biomarkers for inner ear disorders: Scoping review on the role of biomarkers in hearing and balance disorders. Diagnostics (Basel), 11, 42.
Hu Y., Yan H., Liu M., Gao J., Xie L., Zhang C., Jiang H., Ding Y., Jiang H. (2024). Detecting cardiovascular diseases using unsupervised machine learning clustering based on electronic medical records. BMC Med Res Methodol, 24, 309.
Kwong J. C. C., Khondker A., Lajkosz K., McDermott M. B. A., Frigola X. B., McCradden M. D., Johnson A. E. W., Kulkarni G. S., Johnson A. E. W. (2023). APPRAISE-AI tool for quantitative evaluation of AI studies for clinical decision support. JAMA Netw Open, 6, e2335377–e2335377.
Landis J. R., Koch G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33, 159–174.
Lee C. Y., Hwang J. H., Hou S. J., Liu T. C. (2010). Using cluster analysis to classify audiogram shapes. Int J Audiol, 49, 628–633.
Luo H., Yang T., Jin X., Pang X., Li J., Chai Y., Wu H., Zhang Y., Zhang L., Zhang Z., Wu W., Zhang Q., Hu X., Sun J., Jiang X., Fan Z., Huang Z., Wu H. (2013). Association of GRM7 variants with different phenotype patterns of age-related hearing impairment in an elderly male Han Chinese population. PLoS One, 8, e77153.
Mamo S. K., Pearlman J., Wheeler K. A. (2023). Associations between age-related hearing loss, cognitive impairment, and multiple chronic conditions in a group care setting. J Speech Lang Hear Res, 66, 5087–5108.
Margolis R. H., Saly G. L. (2007). Toward a standard description of hearing loss. Int J Audiol, 46, 746–758.
Nelson E., Hinojosa R. (2003). Presbycusis: A human temporal bone study of individuals with flat audiometric patterns of hearing loss using a new method to quantify stria vascularis volume. Laryngoscope, 113, 1672–1686.
Parthasarathy A., Romero Pinto S., Lewis R. M., Goedicke W., Polley D. B. (2020). Data-driven segmentation of audiometric phenotypes across a large clinical cohort. Sci Rep, 10, 6704.
Saak S., Huelsmeier D., Kollmeier B., Buhl M. (2022). A flexible data-driven audiological patient stratification method for deriving auditory profiles. Front Neurol, 13, 959582.
Sanchez Lopez R., Bianchi F., Fereczkowski M., Santurette S., Dau T. (2018). Data-driven approach for auditory profiling and characterization of individual hearing loss. Trends Hear, 22, 2331216518807400.
Sanchez-Lopez R., Fereczkowski M., Neher T., Santurette S., Dau T. (2020). Robust data-driven auditory profiling towards precision audiology. Trends Hear, 24, 2331216520973539.
Schilder A., Su M., Mandavia R., Anderson C., Landry E., Ferdous T., Blackshaw H. (2019). Early phase trials of novel hearing therapeutics: Avenues and opportunities. Hear Res, 380, 175–186.
Schilder A. G. M., Wolpert S., Saeed S., Middelink L. M., Edge A. S. B., Blackshaw H., Pastiadis K., Bibas A. G.; REGAIN Consortium. (2024). A phase I/IIa safety and efficacy trial of intratympanic gamma-secretase inhibitor as a regenerative drug treatment for sensorineural hearing loss. Nat Commun, 15, 1896.
Schmiedt R. (2010). The physiology of cochlear presbycusis. In S. Gordon-Salant, R. D. Frisina Fay R. R., A. N. Popper (Eds.), The Aging Auditory System. Springer.
Schuknecht H. F. (1964). Further observations on the pathology of presbycusis. Arch Otolaryngol, 80, 369–382.
Schuknecht H. F., Gacek M. R. (1993). Cochlear pathology in presbycusis. Ann Otol Rhinol Laryngol, 102(1_suppl), 1–16.
Wahyudi E., Meidelfi D., Nofrizal Saam, Z., Juandi (2023). The implementation of the K-medoid clustering for grouping hearing loss function on excessive smartphone use. JOIV Int J Inform Visualization, 7, 2523–2531.
Wang Q., Qian M., Yang L., Shi J., Hong Y., Han K., Wu H., Lin J., Huang Z., Wu H. (2021). Audiometric phenotypes of noise-induced hearing loss by data-driven cluster analysis and their relevant characteristics. Front Med (Lausanne), 8, 662045.
Watanabe T., Suzuki M. (2018). Analysis of the audiogram shape in patients with idiopathic sudden sensorineural hearing loss using a cluster analysis. Ear Nose Throat J, 97, E36–E40.
World Health Organization. (2018). WHO global estimates on prevalence of hearing loss. https://www.who.int/deafness/estimates/en/.
Xu J., Glicksberg B. S., Su C., Walker P., Bian J., Wang F. (2021). Federated learning for healthcare informatics. J Healthc Inform Res, 5, 1–19.
Alpaydin E. (2014). Introduction to Machine Learning (Adaptive Computation and Machine Learning).
Angelini E. D., Yang J., Balte P. P., Hoffman E. A., Manichaikul A. W., Sun Y., Shen W., Austin J. H., Allen N. B., Bleecker E. R., Bowler R. (2023). Pulmonary emphysema subtypes defined by unsupervised machine learning on CT scans. Thorax, 78, 1067–1079.
Anwar M. N., Oakes M. P. (2012). Data mining of audiology patient records: Factors influencing the choice of hearing aid type. BMC Med Inform Decis Mak, 12(Suppl 1), S6.
Banerjee A., Dashtban A., Chen S., Pasea L., Thygesen J. H., Fatemifar G., Hemingway H., Dyszynski T., Asselbergs F. W., Lund L. H., Lumbers T., Denaxas S., Hemingway H. (2023). Identifying subtypes of heart failure from three electronic health record sources with machine learning: An external, prognostic, and genetic validation study. Lancet Digital Health, 5, e370–e379.
Basile A. O., Ritchie M. D. (2018). Informatics and machine learning to define the phenotype. Expert Rev Mol Diagn, 18, 219–226.
Bisgaard N., Vlaming M. S., Dahlquist M. (2010). Standard audiograms for the IEC 60118-15 measurement procedure. Trends Amplif, 14, 113–120.
Carhart R. (1945). An improved method for classifying audiograms. Laryngoscope, 55, 640–662.
Chang Y. S., Yoon S. H., Kim J. R., Baek S. Y., Cho Y. S., Hong S. H., Moon I. J., Moon I. J. (2019). Standard audiograms for Koreans derived through hierarchical clustering using data from the Korean National Health and Nutrition Examination Survey 2009-2012. Sci Rep, 9, 3675.
Chen R. J., Lu M. Y., Chen T. Y., Williamson D. F. K., Mahmood F. (2021). Synthetic data in machine learning for medicine and healthcare. Nat Biomed Eng, 5, 493–497.
Cruickshanks K. J., Nondahl D. M., Fischer M. E., Schubert C. R., Tweed T. S. (2020). A novel method for classifying hearing impairment in epidemiological studies of aging: The Wisconsin Age-Related Hearing Impairment Classification Scale. Am J Audiol, 29, 59–67.
Dadu A., Satone V., Kaur R., Hashemi S. H., Leonard H., Iwaki H., Faghri F., Billingsley K. J., Bandres-Ciga S., Sargent L. J., Noyce A. J., Daneshmand A., Blauwendraat C., Marek K., Scholz S. W., Singleton A. B., Nalls M. A., Campbell R. H., Faghri F. (2022). Identification and prediction of Parkinson’s disease subtypes and progression using machine learning in two cohorts. Npj Parkinsons Dis, 8, 172.
Dubno J. R., Eckert M. A., Lee F. S., Matthews L. J., Schmiedt R. A. (2013). Classifying human audiometric phenotypes of age-related hearing loss from animal models. J Assoc Res Otolaryngol, 14, 687–701.
Elkhouly A., Andrew A. M., Rahim H. A., Abdulaziz N., Abdulmalek M., Mohd Yasin M. N., Siddique S., Sabapathy T., Siddique S. (2022). A novel unsupervised spectral clustering for pure-tone audiograms towards hearing aid filter bank design and initial configurations. Appl Sci, 12, 298.
Eshaghi A., Young A. L., Wijeratne P. A., Prados F., Arnold D. L., Narayanan S., Ciccarelli O., Barkhof F., Alexander D. C., Thompson A. J., Chard D., Ciccarelli O. (2021). Identifying multiple sclerosis subtypes using unsupervised machine learning and MRI data. Nat Commun, 12, 2078.
Fleuren L. M., Thoral P., Shillan D., Ercole A., Elbers P. W. G.; Right Data Right Now Collaborators. (2020). Machine learning in intensive care medicine: Ready for take-off? Intensive Care Med, 46, 1486–1488.
Gomaa N. A., Jimoh Z., Campbell S., Zenke J. K., Szczepek A. J. (2020). Biomarkers for inner ear disorders: Scoping review on the role of biomarkers in hearing and balance disorders. Diagnostics (Basel), 11, 42.
Hu Y., Yan H., Liu M., Gao J., Xie L., Zhang C., Jiang H., Ding Y., Jiang H. (2024). Detecting cardiovascular diseases using unsupervised machine learning clustering based on electronic medical records. BMC Med Res Methodol, 24, 309.
Kwong J. C. C., Khondker A., Lajkosz K., McDermott M. B. A., Frigola X. B., McCradden M. D., Johnson A. E. W., Kulkarni G. S., Johnson A. E. W. (2023). APPRAISE-AI tool for quantitative evaluation of AI studies for clinical decision support. JAMA Netw Open, 6, e2335377–e2335377.
Landis J. R., Koch G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33, 159–174.
Lee C. Y., Hwang J. H., Hou S. J., Liu T. C. (2010). Using cluster analysis to classify audiogram shapes. Int J Audiol, 49, 628–633.
Luo H., Yang T., Jin X., Pang X., Li J., Chai Y., Wu H., Zhang Y., Zhang L., Zhang Z., Wu W., Zhang Q., Hu X., Sun J., Jiang X., Fan Z., Huang Z., Wu H. (2013). Association of GRM7 variants with different phenotype patterns of age-related hearing impairment in an elderly male Han Chinese population. PLoS One, 8, e77153.
Mamo S. K., Pearlman J., Wheeler K. A. (2023). Associations between age-related hearing loss, cognitive impairment, and multiple chronic conditions in a group care setting. J Speech Lang Hear Res, 66, 5087–5108.
Margolis R. H., Saly G. L. (2007). Toward a standard description of hearing loss. Int J Audiol, 46, 746–758.
Nelson E., Hinojosa R. (2003). Presbycusis: A human temporal bone study of individuals with flat audiometric patterns of hearing loss using a new method to quantify stria vascularis volume. Laryngoscope, 113, 1672–1686.
Parthasarathy A., Romero Pinto S., Lewis R. M., Goedicke W., Polley D. B. (2020). Data-driven segmentation of audiometric phenotypes across a large clinical cohort. Sci Rep, 10, 6704.
Saak S., Huelsmeier D., Kollmeier B., Buhl M. (2022). A flexible data-driven audiological patient stratification method for deriving auditory profiles. Front Neurol, 13, 959582.
Sanchez Lopez R., Bianchi F., Fereczkowski M., Santurette S., Dau T. (2018). Data-driven approach for auditory profiling and characterization of individual hearing loss. Trends Hear, 22, 2331216518807400.
Sanchez-Lopez R., Fereczkowski M., Neher T., Santurette S., Dau T. (2020). Robust data-driven auditory profiling towards precision audiology. Trends Hear, 24, 2331216520973539.
Schilder A., Su M., Mandavia R., Anderson C., Landry E., Ferdous T., Blackshaw H. (2019). Early phase trials of novel hearing therapeutics: Avenues and opportunities. Hear Res, 380, 175–186.
Schilder A. G. M., Wolpert S., Saeed S., Middelink L. M., Edge A. S. B., Blackshaw H., Pastiadis K., Bibas A. G.; REGAIN Consortium. (2024). A phase I/IIa safety and efficacy trial of intratympanic gamma-secretase inhibitor as a regenerative drug treatment for sensorineural hearing loss. Nat Commun, 15, 1896.
Schmiedt R. (2010). The physiology of cochlear presbycusis. In S. Gordon-Salant, R. D. Frisina Fay R. R., A. N. Popper (Eds.), The Aging Auditory System. Springer.
Schuknecht H. F. (1964). Further observations on the pathology of presbycusis. Arch Otolaryngol, 80, 369–382.
Schuknecht H. F., Gacek M. R. (1993). Cochlear pathology in presbycusis. Ann Otol Rhinol Laryngol, 102(1_suppl), 1–16.
Wahyudi E., Meidelfi D., Nofrizal Saam, Z., Juandi (2023). The implementation of the K-medoid clustering for grouping hearing loss function on excessive smartphone use. JOIV Int J Inform Visualization, 7, 2523–2531.
Wang Q., Qian M., Yang L., Shi J., Hong Y., Han K., Wu H., Lin J., Huang Z., Wu H. (2021). Audiometric phenotypes of noise-induced hearing loss by data-driven cluster analysis and their relevant characteristics. Front Med (Lausanne), 8, 662045.
Watanabe T., Suzuki M. (2018). Analysis of the audiogram shape in patients with idiopathic sudden sensorineural hearing loss using a cluster analysis. Ear Nose Throat J, 97, E36–E40.
World Health Organization. (2018). WHO global estimates on prevalence of hearing loss. https://www.who.int/deafness/estimates/en/.
Xu J., Glicksberg B. S., Su C., Walker P., Bian J., Wang F. (2021). Federated learning for healthcare informatics. J Healthc Inform Res, 5, 1–19.
Contributed Indexing:
Keywords: Artificial intelligence; Hearing loss; Machine learning; Phenotypes
Entry Date(s):
Date Created: 20250807 Date Completed: 20251020 Latest Revision: 20251020
Update Code:
20260130
PubMed Central ID:
PMC12533775
DOI:
10.1097/AUD.0000000000001696
PMID:
40770825
Database:
MEDLINE
*Further Information*
*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. However, no study has yet compared the outcomes of different AI models in this context. The purpose of this review is to synthesize the existing literature on the application of unsupervised machine learning models to hearing health data for identifying subtypes of SNHL.
Design: A systematic search was performed of the following databases: MEDLINE, PsycINFO (Ovid version), EMBASE, CINAHL, IEEE, and Scopus as well as a search of grey literature using GitHub and Base, and manual search (Jan 1990-Mar 2024). Studies were included only if they reported on adult patients with SNHL and used an unsupervised machine-learning approach. Quality assessment was performed using the APPRAISE-AI tool. The heterogeneity of studies necessitated a narrative synthesis of the results.
Results: Seven studies were included in the analysis. Apart from one case-control study, all were cohort studies. Four different algorithms were used, with no study comparing the performance of more than one algorithm. Across these studies, only 2 distinct numbers of subtypes were identified: 4 and 11. However, the overall quality of the studies was deemed low, thus preventing definitive conclusions regarding model selection and the actual number of subtypes.
Conclusions: This systematic review identifies key methodological practices that need to be improved before the potential of unsupervised machine learning models to subtype SNHL can be realized. Future research in this field should justify model selection, ensure reproducibility, use high-quality hearing data, and validate model findings.
(Copyright © 2025 The Authors. Ear & Hearing is published on behalf of the American Auditory Society, by Wolters Kluwer Health, Inc.)*
*The authors have no conflicts of interest to disclose.*