Development and testing of an open source mobile application for audiometry test result analysis and diagnosis support

Hearing impairments are typically assessed using pure tone audiometry, a diagnostic method that allows for the identification of the degree, type and configuration of hearing loss. The results of this assessment are generally displayed in the form of an audiogram, which graphically represents the softest sounds perceivable by an individual across a range frequencies. This paper presents a novel Open Source mobile application for the Android operating system that allows users to scan and analyse audiograms using a smartphone camera and subsequently classify the type of hearing loss. The application workflow is divided into three main stages: scanning, digitalization and classification of the audiogram. For this purpose, the application implements several artificial intelligence and image processing techniques, including YOLOv5, Optical Character Recognition (OCR) and Hough Transform. The scanned audiogram is analysed by a clinically validated AI model for classification of audiometric test results, providing clinicians with valuable assistance in formulating a diagnosis. All implemented algorithms and models were optimized for functionality on mobile devices. The application was evaluated on three distinct classes of smartphones across various price points, demonstrating its efficacy and consistent performance. The presented mobile application constitutes an advanced AI-driven decision support system that is readily accessible to general practitioners, otolaryngologists and audiologists. Its integration in medical facilities presents a substantial opportunity to decrease clinical workload, enhance diagnostic accuracy and reduce the likelihood of human error in hearing loss evaluations, which is particularly important in developing countries.