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使用植入前磁共振成像预测带有耳植入的儿童的口语发展
Yanlin Wang1, Di Yuan1,2, Shani Dettman3
1Brain and Mind Institute, The Chinese University of Hong Kong, Hong Kong Special Administrative Region (SAR), China.
JAMA otolaryngology-- head & neck surgery
|December 26, 2025
概括
深度转移学习 (DTL) 准确地预测使用大脑MRI扫描的耳植入物儿童的言语和语言发育. 这种人工智能工具可以识别需要有针对性的干预措施以获得更好的语言成果的儿童.
科学领域:
- 神经科学是一个神经科学.
- 人工智能的人工智能
- 语音语言病理学 语言病理学
背景情况:
- 耳植入物显著改善了严重到严重的感觉神经听力损失的儿童的口语.
- 耳植入后的语言结果非常可变,很难预测.
- 目前的预测方法无法可靠地确定哪些孩子会经历较差的语言发展.
研究的目的:
- 为了比较传统机器学习 (ML) 和深度转移学习 (DTL) 算法的预测精度.
- 用脑MRI的神经解剖学特征来预测儿童的耳植入后口语发育.
- 将儿童分为高语言改进者或低语言改进者.
主要方法:
- 一项多中心诊断研究招募了美国,澳大利亚和香港的278名带有耳植入器的儿童.
- 在手术前使用3D体积大脑MRI扫描来提取神经解剖特征.
- 训练了ML和DTL算法,以预测高与低的语言改进.
主要成果:
- DTL模型实现了92.39%的精度,91.22%的灵敏度和93.56%的特异性.
- 在所有结果指标中,DTL显著优于传统的ML模型.
- 对于DTL,曲线下的面积为0.98,表明了高预测性能.
结论:
- 与传统的ML相比,DTL表现出卓越的准确性,灵敏度和特异性,用于预测cochlear植入后的个人语言改进.
- 通过代表性学习捕获歧视性信息的DTL的能力提供了优势.
- 一个单一的DTL模型可能是可行的全球应用,使个性化干预以改善语言结果.
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