Kolberg Courtney, Holbert Sarah O, Bogle Jamie M, Saoji Aniket A
Department of Otolaryngology-Head and Neck Surgery, Division of Audiology, Mayo Clinic, Scottsdale, AZ 85259, USA.
Department of Otolaryngology-Head and Neck Surgery, Division of Audiology, Mayo Clinic, Rochester, MN 55905, USA.
J Clin Med. 2025 Jul 27;14(15):5302. doi: 10.3390/jcm14155302.
: Traditional hearing aid noise reduction algorithms offer no additional benefit in noisy situations for bimodal cochlear implant (CI) users with a CI in one ear and a hearing aid (HA) in the other. Recent breakthroughs in deep neural network (DNN)-based noise reduction have improved speech understanding for hearing aid users in noisy environments. These advancements could also boost speech perception in noise for bimodal CI users. This study investigated the effectiveness of DNN-based noise reduction in the HAs used by bimodal CI patients. : Eleven bimodal CI patients, aged 71-89 years old, were fit with a Phonak Audéo Sphere Infinio 90 HA in their non-implanted ear and were provided with a Calm Situation program and Spheric Speech in Loud Noise program that uses DNN-based noise reduction. Sentence recognition scores were measured using AzBio sentences in quiet and in noise with the CI alone, hearing aid alone, and bimodally with both the Calm Situation and DNN HA programs. : The DNN program in the hearing aid significantly improved bimodal performance in noise, with sentence recognition scores reaching 79% compared to 60% with Calm Situation (a 19% average benefit, < 0.001). When compared to the CI-alone condition in multi-talker babble, the DNN HA program offered a 40% bimodal benefit, significantly higher than the 21% score seen with the Calm Situation program. : DNN-based noise reduction in HA significantly improves speech understanding in noise for bimodal CI users. Utilization of this technology is a promising option to address patients' common complaint of speech understanding in noise.
传统的助听器降噪算法对于一只耳朵植入人工耳蜗(CI)而另一只耳朵佩戴助听器(HA)的双模式人工耳蜗使用者在嘈杂环境中并无额外益处。基于深度神经网络(DNN)的降噪技术的最新突破改善了助听器使用者在嘈杂环境中的言语理解能力。这些进展也可能提高双模式人工耳蜗使用者在噪声环境中的言语感知能力。本研究调查了基于DNN的降噪技术在双模式人工耳蜗患者所使用的助听器中的有效性。
11名年龄在71 - 89岁的双模式人工耳蜗患者在其未植入人工耳蜗的耳朵中佩戴了峰力奥笛·天朗Infinio 90助听器,并为他们提供了一个安静环境程序以及使用基于DNN降噪技术的嘈杂环境中的球形语音程序。使用AzBio句子在安静环境以及在噪声环境中分别单独使用人工耳蜗、单独使用助听器以及同时使用安静环境程序和基于DNN的助听器程序的双模式情况下测量句子识别分数。
助听器中的DNN程序显著改善了双模式在噪声环境中的表现,句子识别分数达到了79%,而使用安静环境程序时为60%(平均提高了19%,<0.001)。与在多人交谈的嘈杂声中单独使用人工耳蜗的情况相比,基于DNN的助听器程序提供了40%的双模式增益,显著高于使用安静环境程序时的21%的分数。
基于DNN的助听器降噪技术显著提高了双模式人工耳蜗使用者在噪声环境中的言语理解能力。利用这项技术是解决患者关于噪声环境中言语理解这一常见抱怨的一个有前景的选择方案。