Younkyoung Kim, Chong Mi Lee, Seo Young Kang
[Development of a Scale for Alcohol Drinking Prevention Behavior in Early Elementary School Based on Ajzen's Theory of Planned Behavior]
Journal of Korean Academy of Nursing
pure tone audiometry played critical role audiology initial diagnostic tool offering vital insights subsequent analyses study aims develop robust deep learning framework capable accurately classifying audiograms across various commonly encountered tasks singlecentre retrospective study conducted accordance strobe guidelines total 12 518 audiograms collected 6259 patients aged 4 96 years underwent pure tone audiometry testing february 2018 april 2022 tongji hospital tongji medical college wuhan china three experienced audiologists independently annotated audiograms labelling hearing loss degrees types configurations audiogram deep learning framework developed utilised classify audiograms across three tasks determining degrees hearing loss identifying types hearing loss categorising configurations audiograms classification performance evaluated using four commonly used metrics accuracy precision recall f1score deep learning method consistently outperformed alternative methods including knearest neighbors extratrees random forest xgboost lightgbm catboost fastai net across three tasks achieved highest accuracy rates ranging 9675 9985 precision values fell within range 8893 9841 recall values spanned 8925 9838 f1score also exhibited strong performance ranging 8899 9839 study demonstrated deep learning approach could accurately classify audiograms respective categories could contribute assisting doctors particularly lacking audiology expertise experience better interpreting pure tone audiograms enhancing diagnostic accuracy primary care settings reducing misdiagnosis rate hearing conditions scenarios involving largescale audiological data automated classification system could used research tool efficiently provide comprehensive overview statistical analysis era mobile audiometry deep learning framework also help patients quickly reliably understand selftested audiograms potentially encouraging timely consultations audiologists evaluation intervention