Zhe Chen MD, Xue Zhao PhD, Haotian Liu PhD, Yuyang Wang PhD, Zhikai Zhang PhD, Yuxuan Zhang PhD, Yuhe Liu PhD
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Post-CI auditory and verbal communication performance was evaluated using clinical questionnaires with caretakers. Both classification and individualized regression models were constructed to predict post-CI behavioral improvement from fNIRS data using support vector machine (SVM) learning algorithms.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>Auditory cortical responses shortly after CI hearing onset yielded highly accurate prediction of behavioral development in young CI children. For classification models, optimal prediction was achieved using cortical responses to two or more sound conditions, with the highest accuracy of 98.20% (precision = 98.17%, sensitivity = 98.96%, area under the curve of the receiver operating characteristic curve = 99.61%) obtained with the combination of speech, noise, and music stimuli. Similarly, for regression models, best prediction of individual development was achieved using three (highest <i>r</i> = 0.919) or four (<i>r</i> = 0.966) sound conditions. The predictability of cortical responses far outperformed (Cohen's <i>d</i>: 18.56) that of the collection of audiological and demographic parameters (classification accuracy: 0.62) under the same SVM algorithms and could not benefit from the inclusion of the latter.</p>\n </section>\n \n <section>\n \n <h3> Conclusion</h3>\n \n <p>Machine learning models using auditory cortical hemodynamic responses shortly after CI activation were able to predict individualized post-CI behavioral improvement in children with prelingual deafness.</p>\n </section>\n \n <section>\n \n <h3> Level of Evidence</h3>\n \n <p>Level 5.</p>\n </section>\n </div>","PeriodicalId":48529,"journal":{"name":"Laryngoscope Investigative Otolaryngology","volume":"9 6","pages":""},"PeriodicalIF":1.6000,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11558700/pdf/","citationCount":"0","resultStr":"{\"title\":\"Individualized post-operative prediction of cochlear implantation outcomes in children with prelingual deafness using functional near-infrared spectroscopy\",\"authors\":\"Zhe Chen MD, Xue Zhao PhD, Haotian Liu PhD, Yuyang Wang PhD, Zhikai Zhang PhD, Yuxuan Zhang PhD, Yuhe Liu PhD\",\"doi\":\"10.1002/lio2.70035\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Objective</h3>\\n \\n <p>The goal of this study was to develop an objective measure and predictor of cochlear implantation (CI) outcomes using functional near-infrared spectroscopy (fNIRS) for young children with prelingual deafness.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methods</h3>\\n \\n <p>Sound-evoked hemodynamic responses were recorded from auditory and language-related cortical regions of 47 child CI recipients (35.47 ± 17.24 months of age) using fNIRS shortly after CI activation (0.26 ± 0.30 months). 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Similarly, for regression models, best prediction of individual development was achieved using three (highest <i>r</i> = 0.919) or four (<i>r</i> = 0.966) sound conditions. 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引用次数: 0
摘要
研究目的本研究的目的是利用功能性近红外光谱（fNIRS）为语前聋幼儿开发一种人工耳蜗植入（CI）结果的客观测量和预测方法：在 CI 激活后不久（0.26 ± 0.30 个月），使用 fNIRS 记录了 47 名 CI 儿童（35.47 ± 17.24 个月）听觉和语言相关皮层区域的声音诱发血流动力学反应。共有四种声音条件（自然语音、器乐、多扬声器咿呀噪音和噪音中的语音）。通过对护理人员进行临床问卷调查，评估了儿童植入人工耳蜗后的听觉和语言交流能力。利用支持向量机（SVM）学习算法构建了分类和个性化回归模型，以从 fNIRS 数据中预测重症监护后的行为改善情况：结果：CI听力开始后不久的听觉皮层反应能高度准确地预测年幼CI儿童的行为发展。对于分类模型，使用两种或两种以上声音条件下的皮层反应可实现最佳预测，其中语音、噪声和音乐刺激组合的准确率最高，达到 98.20%（精确度 = 98.17%，灵敏度 = 98.96%，接收者工作特征曲线下面积 = 99.61%）。同样，对于回归模型，使用三种（最高 r = 0.919）或四种（r = 0.966）声音条件可实现对个体发展的最佳预测。在相同的 SVM 算法下，皮层反应的可预测性（Cohen's d：18.56）远远优于听觉和人口学参数集合的可预测性（分类准确率：0.62），并且不能从包含后者中获益：机器学习模型使用 CI 激活后不久的听觉皮层血流动力学反应，能够预测语前聋儿童 CI 后行为改善的个体化情况：证据等级：5级。
Individualized post-operative prediction of cochlear implantation outcomes in children with prelingual deafness using functional near-infrared spectroscopy
Objective
The goal of this study was to develop an objective measure and predictor of cochlear implantation (CI) outcomes using functional near-infrared spectroscopy (fNIRS) for young children with prelingual deafness.
Methods
Sound-evoked hemodynamic responses were recorded from auditory and language-related cortical regions of 47 child CI recipients (35.47 ± 17.24 months of age) using fNIRS shortly after CI activation (0.26 ± 0.30 months). There were four sound conditions (natural speech, instrumental music, multi-speaker babble noise, and speech-in-noise). Post-CI auditory and verbal communication performance was evaluated using clinical questionnaires with caretakers. Both classification and individualized regression models were constructed to predict post-CI behavioral improvement from fNIRS data using support vector machine (SVM) learning algorithms.
Results
Auditory cortical responses shortly after CI hearing onset yielded highly accurate prediction of behavioral development in young CI children. For classification models, optimal prediction was achieved using cortical responses to two or more sound conditions, with the highest accuracy of 98.20% (precision = 98.17%, sensitivity = 98.96%, area under the curve of the receiver operating characteristic curve = 99.61%) obtained with the combination of speech, noise, and music stimuli. Similarly, for regression models, best prediction of individual development was achieved using three (highest r = 0.919) or four (r = 0.966) sound conditions. The predictability of cortical responses far outperformed (Cohen's d: 18.56) that of the collection of audiological and demographic parameters (classification accuracy: 0.62) under the same SVM algorithms and could not benefit from the inclusion of the latter.
Conclusion
Machine learning models using auditory cortical hemodynamic responses shortly after CI activation were able to predict individualized post-CI behavioral improvement in children with prelingual deafness.
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