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Volume 14
Issue 8
10.3390/computers14080303
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

White Matter Microstructure Differences Between Congenital and Acquired Hearing Loss Patients Using Diffusion Tensor Imaging (DTI) and Machine Learning

by
Fatimah Kayla Kameela
1,
Fikri Mirza Putranto
2,
Prasandhya Astagiri Yusuf
3,4,
Arierta Pujitresnani
3,
Vanya Vabrina Valindria
3,
Dodi Sudiana
1,5 and
Mia Rizkinia
1,5,*
1
Department of Electrical Engineering, Faculty of Engineering, Universitas Indonesia, Depok 16424, Indonesia
2
Department of Otorhinolaryngology-Head and Neck Surgery, Faculty of Medicine, Universitas Indonesia, Universitas Indonesia Hospital, Depok 16424, Indonesia
3
Medical Technology Cluster, Indonesian Medical Education and Research Institute, Universitas Indonesia, DKI Jakarta 10430, Indonesia
4
Department of Medical Physiology and Biophysics, Faculty of Medicine, Universitas Indonesia, DKI Jakarta 10430, Indonesia
5
Artificial Intelligence and Data Engineering (AIDE) Research Center, Faculty of Engineering, Universitas Indonesia, Depok 16424, Indonesia
*
Author to whom correspondence should be addressed.
Computers 2025, 14(8), 303; https://doi.org/10.3390/computers14080303
Submission received: 22 June 2025 / Revised: 23 July 2025 / Accepted: 24 July 2025 / Published: 25 July 2025
(This article belongs to the Special Issue Application of Artificial Intelligence and Modeling Frameworks in Health Informatics and Related Fields)
Versions Notes

Abstract

Diffusion tensor imaging (DTI) metrics provide insights into neural pathways, which can be pivotal in differentiating congenital and acquired hearing loss to support diagnosis, especially for those diagnosed late. In this study, we analyzed DTI parameters and developed machine learning to classify these two patient groups. The study included 29 patients with congenital hearing loss and 6 with acquired hearing loss. DTI scans were performed to obtain metrics, such as fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD), and mean diffusivity (MD). Statistical analyses based on p-values highlighted the cortical auditory system’s prominence in differentiating between groups, with FA and RD emerging as pivotal metrics. Three machine learning models were trained to classify hearing loss types for each of five dataset scenarios. Random forest (RF) trained on a dataset consisting of significant features demonstrated superior performance, achieving a specificity of 87.12% and F1 score of 96.88%. This finding highlights the critical role of DTI metrics in the classification of hearing loss. The experimental results also emphasized the critical role of FA in distinguishing between the two types of hearing loss, underscoring its potential clinical utility. DTI parameters, combined with machine learning, can effectively distinguish between congenital and acquired hearing loss, offering a robust tool for clinical diagnosis and treatment planning. Further research with larger and balanced cohorts is warranted to validate these findings.
Keywords: diffusion tensor imaging; hearing loss; congenital deafness; acquired deafness; machine learning diffusion tensor imaging; hearing loss; congenital deafness; acquired deafness; machine learning

Share and Cite

MDPI and ACS Style

Kameela, F.K.; Putranto, F.M.; Yusuf, P.A.; Pujitresnani, A.; Valindria, V.V.; Sudiana, D.; Rizkinia, M. White Matter Microstructure Differences Between Congenital and Acquired Hearing Loss Patients Using Diffusion Tensor Imaging (DTI) and Machine Learning. Computers 2025, 14, 303. https://doi.org/10.3390/computers14080303

AMA Style

Kameela FK, Putranto FM, Yusuf PA, Pujitresnani A, Valindria VV, Sudiana D, Rizkinia M. White Matter Microstructure Differences Between Congenital and Acquired Hearing Loss Patients Using Diffusion Tensor Imaging (DTI) and Machine Learning. Computers. 2025; 14(8):303. https://doi.org/10.3390/computers14080303

Chicago/Turabian Style

Kameela, Fatimah Kayla, Fikri Mirza Putranto, Prasandhya Astagiri Yusuf, Arierta Pujitresnani, Vanya Vabrina Valindria, Dodi Sudiana, and Mia Rizkinia. 2025. "White Matter Microstructure Differences Between Congenital and Acquired Hearing Loss Patients Using Diffusion Tensor Imaging (DTI) and Machine Learning" Computers 14, no. 8: 303. https://doi.org/10.3390/computers14080303

APA Style

Kameela, F. K., Putranto, F. M., Yusuf, P. A., Pujitresnani, A., Valindria, V. V., Sudiana, D., & Rizkinia, M. (2025). White Matter Microstructure Differences Between Congenital and Acquired Hearing Loss Patients Using Diffusion Tensor Imaging (DTI) and Machine Learning. Computers, 14(8), 303. https://doi.org/10.3390/computers14080303

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