Machine Learning and Cochlear Implantation-A Structured Review of Opportunities and Challenges
Machine Learning and Cochlear Implantation-A Structured Review of Opportunities and Challenges
复制标题
DOI：
10.1097/mao.0000000000002440
复制
发表时间：
2020-01-01
影响因子：
2.1
通讯作者：
Chan, Timothy C. Y.
中科院分区：
文献类型：
--
作者：
Crowson, Matthew G.;Lin, Vincent;Chan, Timothy C. Y.
目的：在过去的5年里，使用机器学习技术来自动化智能流程并提高医学临床流程效率的情况呈爆炸式增长。机器学习在自动化模式识别和使学习的表示适应新设置方面表现出色。此外，机器学习技术具有整合复杂性的优势，并且不受传统确定性方法的许多限制。由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CI映射，因此，人工耳蜗植入物（CI）是机器学习技术的独特选择。然而，除了信号处理之外，还有许多其他机会可以让机器学习帮助CI。本综述的目的是综合过去机器学习技术在儿科和成人CI中的应用，并描述研究和开发的新机会。数据来源：PubMed/MEDLINE、EMBASE、Scopus和ISI Web of Know-ledge数据库，使用定向搜索策略进行挖掘，以确定CI和人工智能/机器学习文献之间的联系。手动评价并排除非英语文章、无可用摘要或全文的文章以及不相关文章。对纳入的文章进行了特定机器学习方法、内容和应用成功的评估。数据合成：数据库搜索识别出298篇文章。根据可用的摘要/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。从2013年到2018年，出版物逐年明显增加。机器学习技术的应用涉及语音/信号处理优化（17篇;占文献的43.6%），自动诱发电位测量（6; 15.4%），术后性能/疗效预测（5; 12.8%），手术解剖位置预测（3; 7.7%）和2（5.1%）在机器人、电极放置性能和生物材料性能方面。随着最近报告成功应用的出版物的增加，CI和人工智能之间的关系正在加强。相当大的努力已经指向使用机器学习算法增强信号处理和自动化术后MAP。其他有前景的应用包括增强CI手术机制和个性化医疗方法，以提高CI患者的表现。未来的机会包括解决可扩展性以及研究和临床社区对机器学习算法作为有效技术的接受。
Objective: 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.Data Sources: The PubMed/MEDLINE, EMBASE, Scopus, and ISI Web of Know-ledge databases were mined using a directed search strategy to identify the nexus between CI and artificial intelligence/machine learning literature.Study Selection: 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.Data Synthesis: 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.Conclusion: The relationship between Cl 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 scalahility and the research and clinical communities' acceptance of machine learning algorithms as effective techniques.