Computational audiology

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Computational audiology is a branch of audiology that employs techniques from mathematics and computer science to improve clinical treatments and scientific understanding of the auditory system. Computational audiology is closely related to computational medicine, which uses quantitative models to develop improved methods for general disease diagnosis and treatment.[1]

Overview

In contrast to traditional methods in audiology and hearing science research, computational audiology emphasizes predictive modeling and large-scale analytics ("big data") rather than inferential statistics and small-cohort hypothesis testing. The aim of computational audiology is to translate advances in hearing science, data science, information technology, and machine learning to clinical audiological care. Research to understand hearing function and auditory processing in humans as well as relevant animal species represents translatable work that supports this aim. Research and development to implement more effective diagnostics and treatments represent translational work that supports this aim.[2]

For people with hearing difficulties, tinnitus, hyperacusis, or balance problems, these advances might lead to more precise diagnoses, novel therapies, and advanced rehabilitation options including smart prostheses and e-Health/mHealth apps. For care providers, it can provide actionable knowledge and tools for automating part of the clinical pathway.[3]

The field is interdisciplinary and includes foundations in audiology, auditory neuroscience, computer science, data science, machine learning, psychology, signal processing, natural language processing, and vestibulology.

Applications

In computational audiology, models and algorithms are used to understand the principles that govern the auditory system, to screen for hearing loss, to diagnose hearing disorders, to provide rehabilitation, and to generate simulations for patient education, among others.

Computational models of hearing, speech and auditory perception

For decades, phenomenological & biophysical (computational) models have been developed to simulate characteristics of the human auditory system. Examples include models of the mechanical properties of the basilar membrane,[4] the electrically stimulated cochlea,[5][6] middle ear mechanics,[7] bone conduction,[8] and the central auditory pathway.[9] Saremi et al. (2016) compared 7 contemporary models including parallel filterbanks, cascaded filterbanks, transmission lines and biophysical models.[10] More recently, convolutional neural networks (CNNs) have been constructed and trained that can replicate human auditory function[11] or to reproduce complex cochlear mechanics with high accuracy.[12] Although inspired by the interconnectivity of biological neural networks, the architecture of CNNs is distinct from the organization of the natural auditory system.

e-Health / mHealth (connected hearing healthcare, wireless- and internet-based services)

Online pure-tone threshold audiometry (or screening) tests, electrophysiological measures, for example distortion-product OAEs (DPOAEs) and speech-in-noise screening tests are becoming increasingly available as a tools to promote awareness and enable accurate early identification of hearing loss across ages, monitor the effects of ototoxicity and/or noise, and guide ear and hearing care decisions and support to clinicians.[13][14] Smartphone-based tests have been proposed to detect middle ear fluid using acoustic reflectometry and machine learning.[15] Smartphone attachments have also been designed to perform tympanometry for acoustic evaluation of the middle ear eardrum.[16][17] Low-cost earphones attached to smartphones have also been prototyped to help detect the faint otoacoustic emissions from the cochlea and perform new-born hearing screening.[18][19]

Big data and AI in audiology and hearing healthcare

Collecting large numbers of audiograms (e.g. the NIOSH[20] or NHANES databases) provides researchers opportunities to find patterns of hearing status in the population[21][22] or to train AI systems that can classify audiograms.[23] Machine learning can be used to predict the relationship between multiple factors e.g. predict depression based on self-reported hearing loss[24] or the relation between genetic profile and self-reported hearing loss.[25] Hearing aids and wearable provide the option to monitor the soundscape of the user or log the usage patterns which can be used to automatically recommend settings that are expected to benefit the user.[26]

Computational approaches to improving hearing devices and auditory implants

Methods to improve rehabilitation by auditory implants include improving music perception,[27] models of the electrode-neuron interface,[28] and an AI based Cochlear Implant fitting assistant.[29]

Data-based investigations into hearing loss and tinnitus

Online surveys processed with ML-based classification have been used to diagnose somatosensory tinnitus.[30] Automated NLP techniques, unsupervised and supervised Machine Learning have been used to analyze social posts about tinnitus and analyze the heterogeneity of symptoms.[31][32]

Diagnostics for hearing problems, acoustics to facilitate hearing

Machine learning has been applied to audiometry to create flexible, efficient estimation tools that do not require excessive testing time to determine someone's individual's auditory profile.[33][34] Similarly, machine learning based versions of other auditory tests including determining dead regions in the cochlea or equal loudness contours,[35] have been created.

e-Research (remote testing, online experiments, new tools and frameworks)

Examples of e-Research tools include including the Remote Testing Wiki,[36] the Portable Automated Rapid Testing (PART), Ecological Momentary Assessment (EMA) and the NIOSH sound level meter. A number of tools can be found online.[37]

Software and tools

Software and large datasets are important for the development and adoption of computational audiology. As with many scientific computing fields, much of the field of computational audiology existentially depends on open source software and its continual maintenance, development, and advancement.[38]

Related fields

Computational biology, computational medicine, computational pathology are all interdisciplinary approaches to the life sciences that draw from quantitative disciplines such as mathematics and information science.

See also

References

  1. ↑ Winslow, Raimond L.; Trayanova, Natalia; Geman, Donald; Miller, Michael I. (31 October 2012). "Computational Medicine: Translating Models to Clinical Care". Science Translational Medicine 4 (158): 158rv11. doi:10.1126/scitranslmed.3003528. PMID 23115356. 
  2. ↑ Gannon, Frank (November 2014). "The steps from translatable to translational research". EMBO Reports 15 (11): 1107–1108. doi:10.15252/embr.201439587. ISSN 1469-221X. PMID 25296643. 
  3. ↑ Wasmann, Jan-Willem A.; Lanting, Cris P.; Huinck, Wendy J.; Mylanus, Emmanuel A. M.; van der Laak, Jeroen W. M.; Govaerts, Paul J.; Swanepoel, De Wet; Moore, David R. et al. (November–December 2021). "Computational Audiology: New Approaches to Advance Hearing Health Care in the Digital Age". Ear and Hearing 42 (6): 1499–1507. doi:10.1097/AUD.0000000000001041. PMID 33675587. 
  4. ↑ De Boer, Egbert (1996), Dallos, Peter; Popper, Arthur N.; Fay, Richard R., eds., "Mechanics of the Cochlea: Modeling Efforts", The Cochlea, Springer Handbook of Auditory Research (New York, NY: Springer New York) 8: pp. 258–317, doi:10.1007/978-1-4612-0757-3_5, ISBN 978-1-4612-6891-8, http://link.springer.com/10.1007/978-1-4612-0757-3_5, retrieved 2022-01-18 
  5. ↑ Frijns, J. H. M.; de Snoo, S. L.; Schoonhoven, R. (1995-07-01). "Potential distributions and neural excitation patterns in a rotationally symmetric model of the electrically stimulated cochlea" (in en). Hearing Research 87 (1): 170–186. doi:10.1016/0378-5955(95)00090-Q. ISSN 0378-5955. PMID 8567435. https://dx.doi.org/10.1016/0378-5955%2895%2900090-Q. 
  6. ↑ Rubinstein, Jay T.; Hong, Robert (September 2003). "Signal Coding in Cochlear Implants: Exploiting Stochastic Effects of Electrical Stimulation". Annals of Otology, Rhinology & Laryngology 112 (9_suppl): 14–19. doi:10.1177/00034894031120s904. ISSN 0003-4894. PMID 14533839. https://doi.org/10.1177/00034894031120S904. 
  7. ↑ Sun, Q.; Gan, R. Z.; Chang, K.-H.; Dormer, K. J. (2002-10-01). "Computer-integrated finite element modeling of human middle ear" (in en). Biomechanics and Modeling in Mechanobiology 1 (2): 109–122. doi:10.1007/s10237-002-0014-z. ISSN 1617-7959. PMID 14595544. https://doi.org/10.1007/s10237-002-0014-z. 
  8. ↑ Stenfelt, Stefan (2016-10-01). "Model predictions for bone conduction perception in the human" (in en). Hearing Research. MEMRO 2015 – Basic Science meets Clinical Otology 340: 135–143. doi:10.1016/j.heares.2015.10.014. ISSN 0378-5955. PMID 26657096. https://www.sciencedirect.com/science/article/pii/S0378595515300769. 
  9. ↑ Meddis, Ray; Lopez-Poveda, Enrique A.; Fay, Richard R. et al., eds (2010). "Computational Models of the Auditory System" (in en-gb). Springer Handbook of Auditory Research 35. doi:10.1007/978-1-4419-5934-8. ISBN 978-1-4419-1370-8. ISSN 0947-2657. https://link.springer.com/book/10.1007/978-1-4419-5934-8. 
  10. ↑ Saremi, Amin; Beutelmann, Rainer; Dietz, Mathias; Ashida, Go; Kretzberg, Jutta; Verhulst, Sarah (September 2016). "A comparative study of seven human cochlear filter models". The Journal of the Acoustical Society of America 140 (3): 1618–1634. doi:10.1121/1.4960486. ISSN 0001-4966. PMID 27914400. Bibcode: 2016ASAJ..140.1618S. http://dx.doi.org/10.1121/1.4960486. 
  11. ↑ Kell, Alexander J. E.; Yamins, Daniel L. K.; Shook, Erica N.; Norman-Haignere, Sam V.; McDermott, Josh H. (2018-05-02). "A Task-Optimized Neural Network Replicates Human Auditory Behavior, Predicts Brain Responses, and Reveals a Cortical Processing Hierarchy" (in English). Neuron 98 (3): 630–644.e16. doi:10.1016/j.neuron.2018.03.044. ISSN 0896-6273. PMID 29681533. 
  12. ↑ Baby, Deepak; Van Den Broucke, Arthur; Verhulst, Sarah (February 2021). "A convolutional neural-network model of human cochlear mechanics and filter tuning for real-time applications" (in en). Nature Machine Intelligence 3 (2): 134–143. doi:10.1038/s42256-020-00286-8. ISSN 2522-5839. PMID 33629031. 
  13. ↑ Paglialonga, Alessia; Cleveland Nielsen, Annette; Ingo, Elisabeth; Barr, Caitlin; Laplante-Lévesque, Ariane (2018-07-31). "eHealth and the hearing aid adult patient journey: a state-of-the-art review". BioMedical Engineering OnLine 17 (1): 101. doi:10.1186/s12938-018-0531-3. ISSN 1475-925X. PMID 30064497. 
  14. ↑ Frisby, Caitlin; Eikelboom, Robert; Mahomed-Asmail, Faheema; Kuper, Hannah; Swanepoel, De Wet (2021-12-30). "MHealth Applications for Hearing Loss: A Scoping Review". Telemedicine and e-Health 28 (8): 1090–1099. doi:10.1089/tmj.2021.0460. ISSN 1530-5627. PMID 34967683. https://www.liebertpub.com/doi/10.1089/tmj.2021.0460. 
  15. ↑ Chan, Justin; Raju, Sharat; Nandakumar, Rajalakshmi; Bly, Randall; Gollakota, Shyamnath (2019-05-15). "Detecting middle ear fluid using smartphones" (in en). Science Translational Medicine 11 (492): eaav1102. doi:10.1126/scitranslmed.aav1102. ISSN 1946-6234. PMID 31092691. 
  16. ↑ Chan, Justin; Najafi, Ali; Baker, Mallory; Kinsman, Julie; Mancl, Lisa R.; Norton, Susan; Bly, Randall; Gollakota, Shyamnath (2022-06-16). "Performing tympanometry using smartphones" (in en). Communications Medicine 2 (1): 57. doi:10.1038/s43856-022-00120-9. ISSN 2730-664X. PMID 35721828. 
  17. ↑ Community, Nature Portfolio Bioengineering (2022-06-15). "Computing for Audiology: Smartphone tympanometer for diagnosing middle ear disorders" (in en). http://bioengineeringcommunity.nature.com/posts/computing-for-audiology-smartphone-tympanometer-for-diagnosing-middle-ear-disorders. 
  18. ↑ Goodman, Shawn S. (2022-10-31). "Affordable hearing screening" (in en). Nature Biomedical Engineering 6 (11): 1199–1200. doi:10.1038/s41551-022-00959-2. ISSN 2157-846X. PMID 36316370. https://www.nature.com/articles/s41551-022-00959-2. 
  19. ↑ Chan, Justin; Ali, Nada; Najafi, Ali; Meehan, Anna; Mancl, Lisa R.; Gallagher, Emily; Bly, Randall; Gollakota, Shyamnath (2022-10-31). "An off-the-shelf otoacoustic-emission probe for hearing screening via a smartphone" (in en). Nature Biomedical Engineering 6 (11): 1203–1213. doi:10.1038/s41551-022-00947-6. ISSN 2157-846X. PMID 36316369. 
  20. ↑ Masterson, Elizabeth A.; Tak, SangWoo; Themann, Christa L.; Wall, David K.; Groenewold, Matthew R.; Deddens, James A.; Calvert, Geoffrey M. (June 2013). "Prevalence of hearing loss in the United States by industry" (in en). American Journal of Industrial Medicine 56 (6): 670–681. doi:10.1002/ajim.22082. PMID 22767358. https://onlinelibrary.wiley.com/doi/10.1002/ajim.22082. 
  21. ↑ Charih, François; Bromwich, Matthew; Mark, Amy E.; Lefrançois, Renée; Green, James R. (December 2020). "Data-Driven Audiogram Classification for Mobile Audiometry" (in en). Scientific Reports 10 (1): 3962. doi:10.1038/s41598-020-60898-3. ISSN 2045-2322. PMID 32127604. Bibcode: 2020NatSR..10.3962C. 
  22. ↑ Cox, Marco; de Vries, Bert (2021). "Bayesian Pure-Tone Audiometry Through Active Learning Under Informed Priors". Frontiers in Digital Health 3: 723348. doi:10.3389/fdgth.2021.723348. ISSN 2673-253X. PMID 34713188. 
  23. ↑ Crowson, Matthew G.; Lee, Jong Wook; Hamour, Amr; Mahmood, Rafid; Babier, Aaron; Lin, Vincent; Tucci, Debara L.; Chan, Timothy C. Y. (2020-08-07). "AutoAudio: Deep Learning for Automatic Audiogram Interpretation" (in en). Journal of Medical Systems 44 (9): 163. doi:10.1007/s10916-020-01627-1. ISSN 1573-689X. PMID 32770269. https://doi.org/10.1007/s10916-020-01627-1. 
  24. ↑ Crowson, Matthew G.; Franck, Kevin H.; Rosella, Laura C.; Chan, Timothy C. Y. (July–August 2021). "Predicting Depression From Hearing Loss Using Machine Learning" (in en-US). Ear and Hearing 42 (4): 982–989. doi:10.1097/AUD.0000000000000993. ISSN 1538-4667. PMID 33577219. https://journals.lww.com/ear-hearing/Abstract/2021/07000/Predicting_Depression_From_Hearing_Loss_Using.21.aspx. 
  25. ↑ Wells, Helena RR.; Freidin, Maxim B.; Zainul Abidin, Fatin N.; Payton, Antony; Dawes, Piers; Munro, Kevin J.; Morton, Cynthia C.; Moore, David R. et al. (2019-02-14). Genome-wide association study identifies 44 independent genomic loci for self-reported adult hearing difficulty in the UK Biobank cohort. doi:10.1101/549071. http://dx.doi.org/10.1101/549071. Retrieved 2022-01-20. 
  26. ↑ Christensen, Jeppe H.; Saunders, Gabrielle H.; Porsbo, Michael; Pontoppidan, Niels H. (2021). "The everyday acoustic environment and its association with human heart rate: evidence from real-world data logging with hearing aids and wearables". Royal Society Open Science 8 (2): 201345. doi:10.1098/rsos.201345. PMID 33972852. Bibcode: 2021RSOS....801345C. 
  27. ↑ Tahmasebi, Sina; Gajȩcki, Tom; Nogueira, Waldo (2020). "Design and Evaluation of a Real-Time Audio Source Separation Algorithm to Remix Music for Cochlear Implant Users". Frontiers in Neuroscience 14: 434. doi:10.3389/fnins.2020.00434. ISSN 1662-453X. PMID 32508564. 
  28. ↑ Garcia, Charlotte; Goehring, Tobias; Cosentino, Stefano; Turner, Richard E.; Deeks, John M.; Brochier, Tim; Rughooputh, Taren; Bance, Manohar et al. (2021-10-01). "The Panoramic ECAP Method: Estimating Patient-Specific Patterns of Current Spread and Neural Health in Cochlear Implant Users" (in en). Journal of the Association for Research in Otolaryngology 22 (5): 567–589. doi:10.1007/s10162-021-00795-2. ISSN 1438-7573. PMID 33891218. PMC 8476702. https://doi.org/10.1007/s10162-021-00795-2. 
  29. ↑ Battmer, Rolf-Dieter; Borel, Stephanie; Brendel, Martina; Buchner, Andreas; Cooper, Huw; Fielden, Claire; Gazibegovic, Dzemal; Goetze, Romy et al. (2015-03-01). "Assessment of 'Fitting to Outcomes Expert' FOX™ with new cochlear implant users in a multi-centre study". Cochlear Implants International 16 (2): 100–109. doi:10.1179/1754762814Y.0000000093. ISSN 1467-0100. PMID 25118042. https://doi.org/10.1179/1754762814Y.0000000093. 
  30. ↑ Michiels, Sarah; Cardon, Emilie; Gilles, Annick; Goedhart, Hazel; Vesala, Markku; Schlee, Winfried (2021-07-14). "Somatosensory Tinnitus Diagnosis: Diagnostic Value of Existing Criteria". Ear & Hearing 43 (1): 143–149. doi:10.1097/aud.0000000000001105. ISSN 1538-4667. PMID 34261856. http://dx.doi.org/10.1097/aud.0000000000001105. 
  31. ↑ Palacios, Guillaume; Noreña, Arnaud; Londero, Alain (2020). "Assessing the Heterogeneity of Complaints Related to Tinnitus and Hyperacusis from an Unsupervised Machine Learning Approach: An Exploratory Study". Audiology and Neurotology 25 (4): 174–189. doi:10.1159/000504741. ISSN 1420-3030. PMID 32062654. 
  32. ↑ "What can we learn about tinnitus from social media posts?" (in en-US). 2021-06-07. https://computationalaudiology.com/what-can-we-learn-about-tinnitus-from-social-media-posts/. 
  33. ↑ Barbour, Dennis L.; Howard, Rebecca T.; Song, Xinyu D.; Metzger, Nikki; Sukesan, Kiron A.; DiLorenzo, James C.; Snyder, Braham R. D.; Chen, Jeff Y. et al. (July 2019). "Online Machine Learning Audiometry" (in en). Ear & Hearing 40 (4): 918–926. doi:10.1097/AUD.0000000000000669. ISSN 0196-0202. PMID 30358656. PMC 6476703. https://journals.lww.com/00003446-201907000-00014. 
  34. ↑ Schlittenlacher, Josef; Turner, Richard E.; Moore, Brian C. J. (2018-07-01). "Audiogram estimation using Bayesian active learning". The Journal of the Acoustical Society of America 144 (1): 421–430. doi:10.1121/1.5047436. ISSN 0001-4966. PMID 30075695. Bibcode: 2018ASAJ..144..421S. https://asa.scitation.org/doi/10.1121/1.5047436. 
  35. ↑ Schlittenlacher, Josef; Moore, Brian C. J. (2020). "Fast estimation of equal-loudness contours using Bayesian active learning and direct scaling". Acoustical Science and Technology 41 (1): 358–360. doi:10.1250/ast.41.358. https://www.jstage.jst.go.jp/article/ast/41/1/41_E19252/_article. 
  36. ↑ "PP Remote Testing Wiki | Main / RemoteTesting". https://www.spatialhearing.org/remotetesting/Main/RemoteTesting. 
  37. ↑ "Resources" (in en-US). https://computationalaudiology.com/resources/. 
  38. ↑ Fortunato, Laura; Galassi, Mark (2021-05-17). "The case for free and open source software in research and scholarship". Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 379 (2197): 20200079. doi:10.1098/rsta.2020.0079. PMID 33775148. Bibcode: 2021RSPTA.37900079F. https://royalsocietypublishing.org/doi/10.1098/rsta.2020.0079. 



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