Pure tone audiogram classification using deep learning techniques

作者：Zhiyong Dou, Yingqiang Li, Dongzhou Deng, Yunxue Zhang, Anran Pang, Cong Fang, Xiang Bai, Dan Bing · 发表于：Clinical Otolaryngology · 年份：2024 · DOI：10.1111/coa.14170 · 被引用次数：5 · 研究领域：Hearing Loss and Rehabilitation、Hearing, Cochlea, Tinnitus, Genetics、Noise Effects and Management

OBJECTIVE: Pure tone audiometry has played a critical role in audiology as the initial diagnostic tool, offering vital insights for subsequent analyses. This study aims to develop a robust deep learning framework capable of accurately classifying audiograms across various commonly encountered tasks. DESIGN, SETTING, AND PARTICIPANTS: This single-centre retrospective study was conducted in accordance with the STROBE guidelines. A total of 12 518 audiograms were collected from 6259 patients aged between 4 and 96 years, who underwent pure tone audiometry testing between February 2018 and April 2022 at Tongji Hospital, Tongji Medical College, Wuhan, China. Three experienced audiologists independently annotated the audiograms, labelling the hearing loss in degrees, types and configurations of each audiogram. MAIN OUTCOME MEASURES: A deep learning framework was developed and utilised to classify audiograms across three tasks: determining the degrees of hearing loss, identifying the types of hearing loss, and categorising the configurations of audiograms. The classification performance was evaluated using four commonly used metrics: accuracy, precision, recall and F1-score. RESULTS: The deep learning method consistently outperformed alternative methods, including K-Nearest Neighbors, ExtraTrees, Random Forest, XGBoost, LightGBM, CatBoost and FastAI Net, across all three tasks. It achieved the highest accuracy rates, ranging from 96.75% to 99.85%. Precision values fell within the ran...