Automated hearing loss type classification based on pure tone audiometry data

Michał Kassjański; Marcin Kulawiak; Tomasz Przewoźny; Dmitry Tretiakow; Jagoda Kuryłowicz; Andrzej Molisz; Krzysztof Koźmiński; Aleksandra Kwaśniewska; Paulina Mierzwińska-Dolny; Miłosz Grono

doi:10.1038/s41598-024-64310-2

Automated hearing loss type classification based on pure tone audiometry data

Abstrakt

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.

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Autorzy (10)

Michał Kassjański
Marcin Kulawiak dr hab. inż.
Tomasz Przewoźny dr hab.
Dmitry Tretiakow dr
Jagoda Kuryłowicz
Andrzej Molisz dr n. med.
Krzysztof Koźmiński
Aleksandra Kwaśniewska
Paulina Mierzwińska-Dolny
Miłosz Grono

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Kategoria:

Publikacja w czasopiśmie

Typ:

artykuły w czasopismach

Opublikowano w:

Scientific Reports nr 14,
ISSN: 2045-2322

Język:

angielski

Rok wydania:

2024

Opis bibliograficzny:

Kassjański M., Kulawiak M., Przewoźny T., Tretiakow D., Kuryłowicz J., Molisz A., Koźmiński K., Kwaśniewska A., Mierzwińska-Dolny P., Grono M.: Automated hearing loss type classification based on pure tone audiometry data// Scientific Reports -,iss. 1 (2024),

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