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Updated: Jun 23, 2025

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自动听力损失类型分类基于纯音调听力测量数据
Michał Kassjański1, Marcin Kulawiak2, Tomasz Przewoźny3
1Department of Geoinformatics, Faculty of Electronics, Telecommunications and Informatics, Gdańsk University of Technology, G. Narutowicza 11/12, 80-233, Gdańsk, Poland. michal.kassjanski@pg.edu.pl.
一个新的AI模型从听力测量结果中准确地分类听力损失类型,加速诊断. 这种工具有助于全科医生和专家,提高了听力护理的准确性,减少了听力护理的延迟.
科学领域:
- 听力学 听力学是指听力学.
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 音调听力测量是诊断听力损失的标准,但解释需要专家,导致诊断延迟.
- 听力学家的有限可用性会影响及时诊断和治疗听力障碍.
研究的目的:
- 开发和评估一个神经网络用于自动分类的音调听力测量数据.
- 将听力损失分为四类:正常,导电,混合和感应神经.
主要方法:
- 使用双向长短期记忆 (BiLSTM) 神经网络架构.
- 训练模型的15046个听力测量结果由专业的听力学家分析和分类.
主要成果:
- 在一个独立的数据集上实现了99.33%的分类准确性.
- 该模型有效地区分正常听力和不同类型的听力损失.
结论:
- 人工智能模型提供了一种可靠的方法来分类音调听力测量结果,可能减少诊断负担.
- 允许全科医生对转诊结果进行分类,并支持听力学家/耳鼻科医生作为决策支持工具.
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