Detecting type of hearing loss with different AI classification methods: a performance review

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

Identyfikatory

DOI

10.15439/2023F3083

• Języki publikacji: EN

Abstrakty

EN

Hearing is one of the most crucial senses for all humans. It allows people to hear and connect with the environment, the people they can meet and the knowledge they need to live their lives to the fullest. Hearing loss can have a detrimental impact on a person's quality of life in a variety of ways, ranging from fewer educational and job opportunities due to impaired communication to social withdrawal in severe situations. Early diagnosis and treatment can prevent most hearing loss. Pure tone audiometry, which measures air and bone conduction hearing thresholds at various frequencies, is widely used to assess hearing loss. A shortage of audiologists might delay diagnosis since they must analyze an audiogram, a graphic representation of pure tone audiometry test results, to determine hearing loss type and treatment. In the presented work, several AI-based models were used to classify audiograms into three types of hearing loss: mixed, conductive, and sensorineural. These models included Logistic Regression, Support Vector Machines, Stochastic Gradient Descent, Decision Trees, RandomForest, Feedforward Neural Network (FNN), Convolutional Neural Network (CNN), Graph Neural Network (GNN), and Recurrent Neural Network (RNN). The models were trained using 4007 audiograms classified by experienced audiologists. The RNN architecture achieved the best classification performance, with an out-of-training accuracy of 94.46%. Further research will focus on increasing the dataset and enhancing the accuracy of RNN models.

Słowa kluczowe

EN

support vector machines; logistic regression; recurrent neural network; stochastic process; auditory system; graph neural networks; loss measurement

PL

maszyna wektorów nośnych; regresja logistyczna; rekurencyjna sieć neuronowa; proces stochastyczny; sieci neuronowe; pomiar strat

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2023
• Tom: Vol. 35
• Strony: 1017--1022
• Opis fizyczny: Bibliogr. 14 poz., wykr., tab.

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 Gdańsk, Poland

autor

Tretiakow Dmitry

• Department of Otolaryngology, Medical University of Gdańsk, Poland

autor

Kuryłowicz Jagoda

• Department of Otolaryngology, Medical University of Gdańsk, Poland

autor

Molisz Andrzej

• Department of Otolaryngology, Medical University of Gdańsk, Poland

autor

Koźmiński Krzysztof

• Student’s Scientific Circle of Otolaryngology, Medical University of Gdańsk, Poland

autor

Kwaśniewska Aleksandra

• Department of Otolaryngology, Laryngological Oncology and Maxillofacial Surgery, University Hospital No. 2, Bydgoszcz, Poland

autor

Mierzwińska-Dolny Paulina

• Student’s Scientific Circle of Otolaryngology, Medical University of Gdańsk, Poland

autor

Grono Miłosz

• Student’s Scientific Circle of Otolaryngology, Medical University of Gdańsk, Poland

Bibliografia

• 1. World Health Organization. 2021. World report on hearing. https://www. who.int/publications/i/item/world-report-on-hearing.
• 2. Guo, R., Liang, R., Wang, Q. et al. 2023. Hearing loss classification algorithm based on the insertion gain of hearing aid. Multimed Tools Appl, http://dx.doi.org/10.1007/s11042-023-14886-0
• 3. Belitz, C., Ali, H., Hansen, J. H. L. 2019. A Machine Learning Based Clustering Protocol for Determining Hearing Aid Initial Configurations from Pure-Tone Audiograms. Interspeech, 2325–2329, http://dx.doi.org/10.21437/interspeech.2019-3091
• 4. Elkhouly, A., Andrew, A.M., Rahim, H.A. et al. 2023. Data-driven audiogram classifier using data normalization and multi-stage feature selection. Sci Rep 13, 1854, http://dx.doi.org/10.1038/s41598-022-25411-y
• 5. Margolis, R. H., Saly, G. L. 2007. Toward a standard description of hearing loss. International journal of audiology, 46(12), 746–758, http://dx.doi.org/10.1080/14992020701572652
• 6. Elbaşı, E.¸ Obali, M. 2012. Classification of Hearing Losses Determined through the Use of Audiometry using Data Mining, Conference: 9th International Conference on Electronics,Computer and Computation
• 7. Crowson, M.G., Lee J.W., Hamour A., Mahmood, R., Babier, A., Lin, V., Tucci, D.L., Chan, T.C.Y. 2020. AutoAudio: Deep Learning for Automatic Audiogram Interpretation. J Med Syst. 44(9):163, http://dx.doi.org/10. 1007/s10916-020-01627-1
• 8. Barbour, D. L., Wasmann, J. W. 2021. Performance and Potential of Machine Learning Audiometry, The Hearing Journal: Volume 74 - Issue 3 - p 40,43,44, http://dx.doi.org/10.1097/01.HJ.0000737592.24476.88
• 9. Guidelines for manual pure-tone threshold audiometry. (1978). ASHA, 20(4), 297–301
• 10. Ciszkiewicz A., Milewski G., Lorkowski J., 2018. Baker's Cyst Classification Using Random Forests, 2018 Federated Conference on Computer Science and Information Systems (FedCSIS), Poznan, Poland, 2018, pp. 97-100, http://dx.doi.org/10.15439/2018F89
• 11. Kučera E., Haffner O., Stark E., 2017. A method for data classification in Slovak medical records, 2017 Federated Conference on Computer Science and Information Systems (FedCSIS), Prague, Czech Republic, 2017, pp. 181-184, http://dx.doi.org/10.15439/2017F44.
• 12. Landgrebe, T.C., Duin, R.P. 2006. A simplified extension of the Area under the ROC to the multiclass domain
• 13. Al-Askar, H., Radi, N. MacDermott, A. 2016. Chapter 7 - Recurrent Neural Networks in Medical Data Analysis and Classifications, In Emerging Topics in Computer Science and Applied Computing, Applied Computing in Medicine and Health, Morgan Kaufmann,147-165, 9780128034682, http://dx.doi.org/10.1016/B978-0-12-803468-2.00007-2
• 14. Kassjański, M., Kulawiak, M., Przewoźny, T. 2022. Development of an AI-based audiogram classification method for patient referral, 17th Conference on Computer Science and Intelligence Systems (FedCSIS), Sofia, Bulgaria, pp. 163-168, http://dx.doi.org/10.15439/2022F66.

Uwagi

1. Thematic Tracks Short Papers

2. Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).