人工智能技术在听力损失临床诊疗中的应用
The application of artificial intelligence in clinical management of hearing loss
杨冰雨;严晓虹;赵宇
1:四川大学华西医院耳鼻咽喉头颈外科
[1] WORLD HEALTH O.World report on hearing[M].Geneva:World Health Organization,2021.
[2] 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.
[3] ALSAMHORI J F,ALSAMHORI A R F,AMOURAH R M,et al.Artificial intelligence for hearing loss prevention,diagnosis,and management[J].Journal of Medicine,Surgery,and Public Health,2024,3:100133.
[4] WASMANN J A,LANTING C P,HUINCK W J,et al.Computational audiology:new approaches to advance hearing health care in the digital age[J].Ear Hear,2021,42(6):1499-1507.
[5] WIMALARATHNA H,ANKMNAL-VEERANNA S,ALLAN C,et al.Machine learning approaches used to analyze auditory evoked responses from the human auditory brainstem:a systematic review[J].Computer Methods and Programs in Biomedicine,2022,226:107118.
[6] WIMALARATHNA H,ANKMNAL-VEERANNA S,ALLAN C,et al.Comparison of machine learning models to classify auditory brainstem responses recorded from children with auditory processing disorder[J].Computer Methods and Programs in Biomedicine,2021,200:105942.
[7] MOLINA M E,PEREZ A,VALENTE J P.Classification of auditory brainstem responses through symbolic pattern discovery[J].Artificial Intelligence in Medicine,2016,70:12-30.
[8] 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.
[9] DASS S,HOLI M S,SOUNDARARAJAN K.Classification of brainstem auditory evoked potentials using artificial neural network based on time and frequency domain features[J].Journal of Clinical Engineering,2016,41(2):72-82.
[10] SINGH V,AGRAWAL U,CHAUDHARY A K,et al.Study of variation and latency of wave V of brainstem evoked response audiometry in north central India[J].Indian Journal of Otolaryngology and Head & Neck Surgery,2019,71:1408-1411.
[11] QUINONES A,ROMAN A,LEON J,et al.New horizons for intelligent interpretation of ABRs and baeps:a brief review,challenges,and opportunities[C].International KES conference on innovation in medicine and healthcare,2024.
[12] DOU Z,LI Y,DENG D,et al.Pure tone audiogram classification using deep learning techniques[J].Clinical Otolaryngology,2024,49(5):595-603.
[13] SADEGH-ZADEH S A,SOLEIMANI MAMALO A,KAVIANPOUR K,et al.Artificial intelligence approaches for tinnitus diagnosis:leveraging high-frequency audiometry data for enhanced clinical predictions[J].Frontiers in Artificial Intelligence,2024,7:1381455.
[14] ELKHOULY A,ANDREW A M,RAHIM H A,et al.A novel unsupervised spectral clustering for pure-tone audiograms towards hearing aid filter bank design and initial configurations[J].Applied Sciences,2021,12(1):298.
[15] TSUZUKI N,KITAMA T,WASANO K,et al.Characteristics of pure tone audiogram in patients with untreated sporadic vestibular schwannoma:analysis of audiometric shape and interaural differences stratified by age and mode of onset[J].Auris Nasus Larynx,2024,51(2):347-355.
[16] KASSJANSKI M,KULAWIAK M,PRZEWOZNY T,et al.Automated hearing loss type classification based on pure tone audiometry data[J].Scientific Reports,2024,14(1):14203.
[17] CROWSON M G,LEE J W,HAMOUR A,et al.Autoaudio:deep learning for automatic audiogram interpretation[J].Journal of Medical Systems,2020,44(9):163.
[18] LIU X,GUO P,WANG D,et al.Applications of machine learning in Meniere’s disease assessment based on pure-tone audiometry[J].Otolaryngology—Head and Neck Surgery,2025,172(1):233-242.
[19] HOFF M,G??THBERG H,TENGSTRAND T,et al.Accuracy of automated pure-tone audiometry in population-based samples of older adults[J].International Journal of Audiology,2024,63(8):622-630.
[20] LI X,GONG Z,YIN H,et al.A 3D deep supervised densely network for small organs of human temporal bone segmentation in CT images[J].Neural Networks,2020,124:75-85.
[21] SONG D,KIM T,LEE Y,et al.Image-based artificial intelligence technology for diagnosing middle ear diseases:a systematic review[J].Journal of Clinical Medicine,2023,12(18):5831.
[22] BOUCHER F,LIAO E,SRINIVASAN A.Diffusion-weighted imaging of the head and neck (including temporal bone)[J].Magnetic Resonance Imaging Clinics,2021,29(2):205-232.
[23] LINGAM R K,BASSETT P.A meta-analysis on the diagnostic performance of non-echoplanar diffusion-weighted imaging in detecting middle ear cholesteatoma:10 years on[J].Otology & Neurotology,2017,38(4):521-528.
[24] WANG D,ZHANG Y,ZHANG K,et al.Focalmix:semi-supervised learning for 3D medical image detection[C].Proceedings of the IEEE/CVF conference on computer vision and pattern recognition,2020.
[25] CHEN B,LI Y,SUN Y,et al.A 3D and explainable artificial intelligence model for evaluation of chronic otitis media based on temporal bone computed tomography:model development,validation,and clinical application[J].J Med Internet Res,2024,26:e51706.
[26] SUNDGAARD J V,HARTE J,BRAY P,et al.Deep metric learning for otitis media classification[J].Med Image Anal,2021,71:102034.
[27] OGAWA M,KISOHARA M,YAMAMOTO T,et al.Utility of unsupervised deep learning using a 3D variational autoencoder in detecting inner ear abnormalities on CT images[J].Computers in Biology and Medicine,2022,147:105683.
[28] LIM J,ABILY A,BEN SALEM D,et al.Training and validation of a deep learning U-Net architecture general model for automated segmentation of inner ear from CT[J].European Radiology Experimental,2024,8(1):104.
[29] MICHELS T C,DUFFY M T,ROGERS D J.Hearing loss in adults:differential diagnosis and treatment[J].Am Fam Physician,2019,100(2):98-108.
[30] KORZEPA M,PETERSEN M K,LARSEN J E,et al.Simulation environment for guiding the design of contextual personalization systems in the context of hearing aids[C].28th ACM conference on user modeling,adaptation and personalization,2020.
[31] DILLON H,ZAKIS J A,MCDERMOTT H,et al.The trainable hearing aid:what will it do for clients and clinicians?[J].The Hearing Journal,2006,59(4):30.
[32] FABRY D A,BHOWMIK A K.Improving speech understanding and monitoring health with hearing aids using artificial intelligence and embedded sensors[C].Seminars in hearing,2021.
[33] RAMACHANDRA B,NALINA H.A survey of recent advances in hearing aid technologies and trends[J].IRJAEH,2024,2(2):303-308.
[34] FENG QY,LI S,CAO YM,et al.Establishment of quantitative evaluation system for cochlear implant effectiveness for hearing-impaired children[J].Journal of Otology,2024,19(2):77-84.
[35] SHAFIEIBAVANI E,GOUDEY B,KIRAL I,et al.Predictive models for cochlear implant outcomes:performance,generalizability,and the impact of cohort size[J].Trends in Hearing,2021,25:23312165211066174.
[36] BING D,YING J,MIAO J,et al.Predicting the hearing outcome in sudden sensorineural hearing loss via machine learning models[J].Clinical Otolaryngology,2018,43(3):868-874.
[37] TANG X.The role of artificial intelligence in medical imaging research[J].BJR Open,2019,2(1):20190031.
[38] PINTO-COELHO L.How artificial intelligence is shaping medical imaging technology:a survey of innovations and applications[J].Bioengineering,2023,10(12):1435.

