Development of an AI-based audiogram classification method for patient referral

• Autorzy: Kassjański Michał , Kulawiak Marcin , Przewoźny Tomasz

Identyfikatory

DOI

10.15439/2022F66

• Konferencja: Federated Conference on Computer Science and Information Systems (17 ; 04-07.09.2022 ; Sofia, Bulgaria)
• Języki publikacji: EN

Abstrakty

EN

Hearing loss is one of the most significant sensory disabilities. It can have various negative effects on a person's quality of life, ranging from impeded school and academic performance to total social isolation in severe cases. It is therefore vital that early symptoms of hearing loss are diagnosed quickly and accurately. Audiology tests are commonly performed with the use of tonal audiometry, which measures a patient's hearing threshold both in air and bone conduction at different frequencies. The graphic result of this test is represented on an audiogram, which is a diagram depicting the values of the patient's measured hearing thresholds. In the course of the presented work several different artificial neural network models, including MLP, CNN and RNN, have been developed and tested for classification of audiograms into two classes - normal and pathological represented hearing loss. The networks have been trained on a set of 2400 audiograms analysed and classified by professional audiologists. The best classification performance was achieved by the RNN architecture (represented by simple RNN, GRU and LSTM), with the highest out-of-training accuracy being 98% for LSTM. In clinical application, the developed classifier can significantly reduce the workload of audiology specialists by enabling the transfer of tasks related to analysis of hearing test results towards general practitioners. The proposed solution should also noticeably reduce the patient's average wait time between taking the hearing test and receiving a diagnosis. Further work will concentrate on automating the process of audiogram interpretation for the purpose of diagnosing different types of hearing loss.

Słowa kluczowe

EN

graphics; computer science; pathology; recurrent neural network; auditory system; medical service; distance measurement

PL

grafika; informatyka; patologia; rekurencyjna sieć neuronowa; usługi medyczne; pomiar odległości

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2022
• Tom: Vol. 30
• Strony: 163--168
• Opis fizyczny: Bibliogr. 24 poz., tab., wykr.

Twórcy

autor

Kassjański Michał

• Department of Geoinformatics, Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Gdansk, Poland

autor

Kulawiak Marcin

• Department of Geoinformatics, Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Gdansk, Poland

autor

Przewoźny Tomasz

• Department of Otolaryngology, Medical University of Gdansk, Smoluchowskiego Str. 17, 80-214 Gdansk, Poland

Bibliografia

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Uwagi

1. Track 2: 1st Workshop on Artificial Intelligence for Next-Generation Diagnostic Imaging

2. Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).