PDF(Pubmed)
Abstract：
Hearing problems are commonly diagnosed with the use of tonal audiometry, which measures a patient\'s hearing threshold in both air and bone conduction at various frequencies. Results of audiometry tests, usually represented graphically in the form of an audiogram, need to be interpreted by a professional audiologist in order to determine the exact type of hearing loss and administer proper treatment. However, the small number of professionals in the field can severely delay proper diagnosis. The presented work proposes a neural network solution for classification of tonal audiometry data. The solution, based on the Bidirectional Long Short-Term Memory architecture, has been devised and evaluated for classifying audiometry results into four classes, representing normal hearing, conductive hearing loss, mixed hearing loss, and sensorineural hearing loss. The network was trained using 15,046 test results analysed and categorised by professional audiologists. The proposed model achieves 99.33% classification accuracy on datasets outside of training. In clinical application, the model allows general practitioners to independently classify tonal audiometry results for patient referral. In addition, the proposed solution provides audiologists and otolaryngologists with access to an AI decision support system that has the potential to reduce their burden, improve diagnostic accuracy, and minimise human error.
摘要：
听力问题通常通过使用音调测听来诊断，测量患者在各种频率的空气和骨传导中的听力阈值。听力测试结果，通常以听力图的形式图形表示，需要由专业听力学家进行解释，以确定听力损失的确切类型并进行适当的治疗。然而,该领域的少数专业人员会严重延误正确的诊断。提出的工作提出了一种用于音调测听数据分类的神经网络解决方案。解决方案,基于双向长短期记忆架构，已经设计和评估了将测听结果分为四类，代表正常听力,传导性听力损失,混合性听力损失,和感觉神经性听力损失。使用由专业听力学家分析和分类的15,046个测试结果对网络进行了培训。所提出的模型在训练之外的数据集上实现了99.33%的分类准确率。在临床应用中,该模型允许全科医生独立地对患者转诊的音调测听结果进行分类.此外,拟议的解决方案为听力学家和耳鼻喉科医生提供了AI决策支持系统的访问权限，该系统有可能减轻他们的负担，提高诊断准确性，减少人为错误。
