人工智能助力改善老年听力障碍的研究进展

doi:10.3969/j.issn.1003-9198.2024.02.002

[1] YAMASOBA T, LIN F R, SOMEYA S, et al. Current concepts in age-related hearing loss: epidemiology and mechanistic pathways[J]. Hear Res, 2013, 303:30-38.
[2] World Health Organization.Deafness and hearing loss[EB/OL].(2023-02-27)[2023-10-17] . https://www.who.int/news-room/fact-sheets/detail/deafness-and-hearing-loss.
[3] AMIEVA H, OUVRARD C. Does treating hearing loss in older adults improve cognitive outcomes? A review[J]. J Clin Med, 2020, 9(3): 805.
[4] GOMAN A M, LIN F R. Prevalence of hearing loss by severity in the United States[J]. Am J Public Health, 2016, 106(10): 1820-1822.
[5] GUAN J. Artificial intelligence in healthcare and medicine: promises, ethical challenges and governance[J]. Chin Med Sci J, 2019, 34(2): 76-83.
[6] HAUG C J, DRAZEN J M. Artificial intelligence and machine learning in clinical medicine, 2023[J]. N Engl J Med, 2023, 388(13): 1201-1208.
[7] WU S, ROBERTS K, DATTA S, et al. Deep learning in clinical natural language processing: a methodical review[J]. J Am Med Inform Assoc, 2020, 27(3): 457-470.
[8] TOPOL E J. High-performance medicine: the convergence of human and artificial intelligence[J]. Nat Med, 2019, 25(1): 44-56.
[9] 黄治物, 吴皓. 年龄相关性听力损失研究进展与临床干预策略[J]. 上海交通大学学报(医学版), 2022, 42(9): 1182-1187.
[10] ABD GHANI M K, NOMA N G, MOHAMMED M A, et al. Innovative artificial intelligence approach for hearing-loss symptoms identification model using machine learning techniques[J]. Sustainability, 2021, 13(10): 5406.
[11] HEISEY K L, WALKER A M, XIE K, et al. Dynamically masked audiograms with machine learning audiometry[J]. Ear Hear, 2020, 41(6): 1692-1702.
[12] AL OSMAN R, AL OSMAN H. On the use of machine learning for classifying auditory brainstem responses: a scoping review[J]. IEEE Access, 2021, 9:110592-110600.
[13] RAJA SANKARI V M, SNEKHALATHA U, MURUGAPPAN M, et al. Artificial intelligence-based hearing loss detection using acoustic threshold and speech perception level[J]. Arab J Sci Eng, 2023, 48:14883-14899.
[14] ABD GHANI M K, NOMA N G, MOHAMMED M A, et al. Innovative artificial intelligence approach for hearing-loss symptoms identification model using machine learning techniques[J]. Sustainability, 2021, 13(10): 5406.
[15] CHA D, PAE C, SEONG S B, et al. Automated diagnosis of ear disease using ensemble deep learning with a big otoendoscopy image database[J]. EBioMedicine, 2019, 45:606-614.
[16] YANCEY K L, CHEROMEI L J, MUHANDO J, et al. Pediatric hearing screening in low-resource settings: incorporation of video-otoscopy and an electronic medical record[J].Int J Pediatr Otorhinolaryngol, 2019, 126:109633.
[17] LI B. Hearing loss classification via AlexNet and extreme learning machine[J]. Int J Cogn Comput Eng, 2021, 2:144-153.
[18] ORJI A, KAMENOV K, DIRAC M, et al. Global and regional needs, unmet needs and access to hearing aids[J]. Int J Audiol, 2020, 59(3): 166-172.
[19] YOU E, LIN V, MIJOVIC T,et al.Artificial intelligence applications in otology: a state of the art review[J]. Otolaryngol Head Neck Surg, 2020, 163(6): 1123-1133.
[20] BALLING L W, MØLGAARD L L, TOWNEND O, et al. The collaboration between hearing aid users and artificial intelligence to optimize sound[J]. Semin Hear, 2021, 42(3): 282-294.
[21] LI Z, LIU F, YANG W, et al. A survey of convolutional neural networks: analysis, applications, and prospects[J]. IEEE TNeur Net Lear, 2022,32(12): 6999-7019.
[22] BHAT G S, SHANKAR N, PANAHI I M S. Automated machine learning based speech classification for hearing aid applications and its real-time implementation on smartphone[J]. Annu Int Conf IEEE Eng Med Biol Soc, 2020, 2020:956-959.
[23] FAN X, SUN T, CHEN W, et al. Deep neural network based environment sound classification and its implementation on hearing aid APP[J]. Measurement, 2020, 159(9): 107790.
[24] RAHME M, FOLKEARD P, SCOLLIE S. Evaluating the accuracy of step tracking and fall detection in the Starkey Livio artificial intelligence hearing aids: a pilot study[J]. Am J Audiol, 2021, 30(1): 182-189.
[25] MANERO A, CRAWFORD K E, PROCK-GIBBS H, et al.Improving disease prevention, diagnosis, and treatment using novel bionic technologies[J]. Bioeng Transl Med, 2023, 8(1): e10359.
[26] OLZE H, UECKER F C, HAUSSLER S M, et al. Hearing implants in the era of digitization[J]. Laryngorhinootologie, 2019, 98(S1): S82-S128.
[27] LUO C, OMELCHENKO I, MANSON R, et al. Direct intracochlear acoustic stimulation using a PZT microactuator[J]. TrendsHear, 2015, 19: 2331216515616942.
[28] ABOUSETTA A, EL KHOLY W, HEGAZY M, et al. A scoring system for cochlear implant candidate selection using artificial intelligence[J]. Hearing Balanc Commun, 2023, 21(2): 114-121.
[29] AGHABIGLOU A, EKSIOGLU E M. Projection-Based cascaded U-Net model for MR image reconstruction[J]. Comput Methods Programs in Biomed, 2021, 207:106151.
[30] LI C, LI X, ZHOU R. Cochlear CT image segmentation based on U-Net neural network[J]. J Radiat Res Appl Sci, 2023, 16(2): 100560.
[31] LEVIN S V, KUZOVKOV V, LEVINA E A, et al. Rehabilitation of patients with a cochlear implant using artificial intelligence algorithms[J]. J Hearing Sci, 2022, 12(1): 183-184.
[32] WALTZMAN S B, KELSALL D C. The use of artificial intelligence to program cochlear implants[J]. Otol Neurotol, 2020, 41(4): 452-457.
[33] CONVERY E, KEIDSER G, MCLELLAND M, et al. A smartphone APP to facilitate remote patient-provider communication in hearing health care: usability and effect on hearing aid outcomes[J]. Telemed J E Health, 2020, 26(6): 798-804.
[34] MEEUWS M, PASCOAL D, JANSSENS DE VAREBEKE S, et al. Cochlear implant telemedicine: remote fitting based on psychoacoustic self-tests and artificial intelligence[J]. Cochlear Implants Int, 2020, 21(5): 260-268.
[35] KOTELUK O, WARTECKI A, MAZUREK S, et al. How do machines learn? Artificial intelligence as a new era in medicine[J]. J Pers Med, 2021, 11(1): 32.
[36] ROWE M. An introduction to machine learning for clinicians[J]. Acad Med, 2019, 94(10): 1433-1436.
[37] MURDOCH B. Privacy and artificial intelligence: challenges for protecting health information in a new era[J]. BMC Med Ethics, 2021, 22(1): 122.

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