Crowson Matthew G, Lin Vincent, Chen Joseph M, Chan Timothy C Y
Department of Otolaryngology-HNS, Sunnybrook Health Sciences Center.
Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario, Canada.
Otol Neurotol. 2020 Jan;41(1):e36-e45. doi: 10.1097/MAO.0000000000002440.
The use of machine learning technology to automate intellectual processes and boost clinical process efficiency in medicine has exploded in the past 5 years. Machine learning excels in automating pattern recognition and in adapting learned representations to new settings. Moreover, machine learning techniques have the advantage of incorporating complexity and are free from many of the limitations of traditional deterministic approaches. Cochlear implants (CI) are a unique fit for machine learning techniques given the need for optimization of signal processing to fit complex environmental scenarios and individual patients' CI MAPping. However, there are many other opportunities where machine learning may assist in CI beyond signal processing. The objective of this review was to synthesize past applications of machine learning technologies for pediatric and adult CI and describe novel opportunities for research and development.
The PubMed/MEDLINE, EMBASE, Scopus, and ISI Web of Knowledge databases were mined using a directed search strategy to identify the nexus between CI and artificial intelligence/machine learning literature.
Non-English language articles, articles without an available abstract or full-text, and nonrelevant articles were manually appraised and excluded. Included articles were evaluated for specific machine learning methodologies, content, and application success.
The database search identified 298 articles. Two hundred fifty-nine articles (86.9%) were excluded based on the available abstract/full-text, language, and relevance. The remaining 39 articles were included in the review analysis. There was a marked increase in year-over-year publications from 2013 to 2018. Applications of machine learning technologies involved speech/signal processing optimization (17; 43.6% of articles), automated evoked potential measurement (6; 15.4%), postoperative performance/efficacy prediction (5; 12.8%), and surgical anatomy location prediction (3; 7.7%), and 2 (5.1%) in each of robotics, electrode placement performance, and biomaterials performance.
The relationship between CI and artificial intelligence is strengthening with a recent increase in publications reporting successful applications. Considerable effort has been directed toward augmenting signal processing and automating postoperative MAPping using machine learning algorithms. Other promising applications include augmenting CI surgery mechanics and personalized medicine approaches for boosting CI patient performance. Future opportunities include addressing scalability and the research and clinical communities' acceptance of machine learning algorithms as effective techniques.
在过去5年中，利用机器学习技术实现医学知识流程自动化并提高临床流程效率的应用呈爆发式增长。机器学习擅长自动化模式识别，并能使学习到的表征适应新的环境。此外，机器学习技术具有纳入复杂性的优势，且不受许多传统确定性方法的限制。鉴于需要优化信号处理以适应复杂的环境场景和个体患者的人工耳蜗（CI）编程，人工耳蜗非常适合机器学习技术。然而，在人工耳蜗领域，除了信号处理之外，机器学习还有许多其他可能发挥作用的机会。这篇综述的目的是综合机器学习技术在儿童和成人人工耳蜗方面的既往应用，并描述新的研发机会。
使用定向搜索策略挖掘PubMed/MEDLINE、EMBASE、Scopus和ISI Web of Knowledge数据库，以确定人工耳蜗与人工智能/机器学习文献之间的联系。
人工评估并排除非英语文章、没有摘要或全文的文章以及不相关的文章。对纳入的文章评估其具体的机器学习方法、内容和应用成效。
数据库搜索共识别出298篇文章。根据摘要/全文、语言和相关性，排除了259篇文章（86.9%）。其余39篇文章纳入综述分析。2013年至2018年的年发表量显著增加。机器学习技术的应用包括语音/信号处理优化（17篇；占文章的43.6%）、自动诱发电位测量（6篇；15.4%）、术后表现/疗效预测（5篇；12.8%）、手术解剖位置预测（3篇；7.7%），以及机器人技术、电极放置性能和生物材料性能方面各2篇（5.1%）。
随着近期报告成功应用的文献增多，人工耳蜗与人工智能之间的关系正在加强。人们已投入大量精力利用机器学习算法增强信号处理并实现术后编程自动化。其他有前景的应用包括改进人工耳蜗手术操作以及采用个性化医疗方法提高人工耳蜗患者的表现。未来的机会包括解决可扩展性问题，以及使研究和临床界接受机器学习算法作为有效的技术。
