Nair Anu Prasad Sreenivasan, Mishra Srikanta K, Alba Diaz Pedro Andres
Department of Speech Language & Hearing Sciences, University of Texas, Austin, TX, USA.
NPJ Digit Med. 2025 Jul 5;8(1):411. doi: 10.1038/s41746-025-01733-9.
Cochlear implants (CIs) have transformed the lives of over one million individuals with hearing impairment, including children as young as nine months. This systematic review critically examines the current literature on the application of machine learning (ML) techniques for predicting CI outcomes. A comprehensive search identified 20 relevant studies. Imaging-based studies demonstrated high predictive accuracy for language and speech perception outcomes. Neural function measures provided a feasible way to assess the functional status of the auditory nerve, while clinical and audiological predictors were extensively explored through data mining techniques. Additionally, ML-based speech enhancement algorithms showed promise in improving speech recognition in noisy environments, a major challenge for CI users. Despite these advancements, a significant gap remains in developing models that can be directly integrated into CI programming. Integrating ML into CIs- in areas like signal processing and device programming-holds immense potential to support personalized patient care for hearing-impaired individuals.
人工耳蜗（CI）改变了超过100万听力受损者的生活，其中包括年仅9个月的儿童。本系统评价批判性地审视了当前关于应用机器学习（ML）技术预测人工耳蜗效果的文献。全面检索确定了20项相关研究。基于成像的研究显示出对语言和言语感知结果的高预测准确性。神经功能测量提供了一种评估听神经功能状态的可行方法，而临床和听力学预测指标则通过数据挖掘技术得到了广泛探索。此外，基于机器学习的语音增强算法在改善嘈杂环境中的语音识别方面显示出前景，这是人工耳蜗使用者面临的一项重大挑战。尽管取得了这些进展，但在开发可直接集成到人工耳蜗编程中的模型方面仍存在重大差距。将机器学习集成到人工耳蜗中——在信号处理和设备编程等领域——对于支持听力受损个体的个性化患者护理具有巨大潜力。
