A multimodal machine learning framework for diagnosis of otitis media with effusion using 3D wideband acoustic immittance

Tariq Rahim; Fei Zhao

doi:10.1109/CCNC54725.2025.10976051

A multimodal machine learning framework for diagnosis of otitis media with effusion using 3D wideband acoustic immittance

Tariq Rahim
, Fei Zhao

Research output: Contribution to conference › Paper › peer-review

10 Downloads (Pure)

Abstract

Wideband acoustic immittance (WAI) technology has been known for over a decade, delivering an enhanced diagnosis of middle ear (ME) diseases across a wider frequency range than standard tympanometry. Nevertheless, its clinical usage confronts the limitations of restricted interpretation and insufficient explanation of the WAI outcomes. This paper proposes a multimodal machine learning (MML) approach for classifying ME diseases into normal ear and ear with abnormalalty i.e., otitis media with effusion. The proposed MML model is grounded on the integration of a 3 layered convolutional neural network and a multi-layer perception network. The outcomes exhibited that the proposed MML model surpasses the available methods by achieving 98.27% accuracy for classifying ME diseases using the WAI measurements.

Original language	English
DOIs	https://doi.org/10.1109/CCNC54725.2025.10976051
Publication status	Published - 10 Jan 2025
Event	2025 IEEE 22nd Consumer Communications & Networking Conference (CCNC) - Las Vegas, U.S. Duration: 10 Jan 2025 → 13 Jan 2025

Conference

Conference	2025 IEEE 22nd Consumer Communications & Networking Conference (CCNC)
Period	10/01/25 → 13/01/25

Bibliographical note

Note: Published in: 2025 IEEE 22nd Consumer Communications & Networking Conference (CCNC), Piscataway, U.S. : Institute of Electrical and Electronics Engineers, Inc, ISSN 2331-9852 ISBN 9798331508067. This work was supported by Kingston University London, United Kingdom.

Organising Body: Institute of Electrical and Electronics Engineers

Keywords

Computer science and informatics
Accuracy
Wideband acoustic immittance
convolutional neural network
machine learning
multi-layer perception

Access to Document

10.1109/CCNC54725.2025.10976051

Rahim-T-56889-AAMAccepted author manuscript, 1.95 MBLicence: Unspecified

A multimodal machine learning framework for diagnosis of otitis media with effusion using 3D wideband acoustic immittance
Rahim, T. & Zhao, F., 10 Jan 2025, 2025 IEEE 22nd Consumer Communications and Networking Conference, CCNC 2025. Piscataway, U.S.: IEEE Publishing, (Proceedings - IEEE Consumer Communications and Networking Conference, CCNC).
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Open Access
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Cite this

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title = "A multimodal machine learning framework for diagnosis of otitis media with effusion using 3D wideband acoustic immittance",

abstract = "Wideband acoustic immittance (WAI) technology has been known for over a decade, delivering an enhanced diagnosis of middle ear (ME) diseases across a wider frequency range than standard tympanometry. Nevertheless, its clinical usage confronts the limitations of restricted interpretation and insufficient explanation of the WAI outcomes. This paper proposes a multimodal machine learning (MML) approach for classifying ME diseases into normal ear and ear with abnormalalty i.e., otitis media with effusion. The proposed MML model is grounded on the integration of a 3 layered convolutional neural network and a multi-layer perception network. The outcomes exhibited that the proposed MML model surpasses the available methods by achieving 98.27\% accuracy for classifying ME diseases using the WAI measurements.",

keywords = "Computer science and informatics, Accuracy, Wideband acoustic immittance, convolutional neural network, machine learning, multi-layer perception",

author = "Tariq Rahim and Fei Zhao",

note = "Note: Published in: 2025 IEEE 22nd Consumer Communications \& Networking Conference (CCNC), Piscataway, U.S. : Institute of Electrical and Electronics Engineers, Inc, ISSN 2331-9852 ISBN 9798331508067. This work was supported by Kingston University London, United Kingdom. Organising Body: Institute of Electrical and Electronics Engineers; 2025 IEEE 22nd Consumer Communications \& Networking Conference (CCNC) ; Conference date: 10-01-2025 Through 13-01-2025",

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AU - Rahim, Tariq

AU - Zhao, Fei

N1 - Note: Published in: 2025 IEEE 22nd Consumer Communications & Networking Conference (CCNC), Piscataway, U.S. : Institute of Electrical and Electronics Engineers, Inc, ISSN 2331-9852 ISBN 9798331508067. This work was supported by Kingston University London, United Kingdom. Organising Body: Institute of Electrical and Electronics Engineers

PY - 2025/1/10

Y1 - 2025/1/10

N2 - Wideband acoustic immittance (WAI) technology has been known for over a decade, delivering an enhanced diagnosis of middle ear (ME) diseases across a wider frequency range than standard tympanometry. Nevertheless, its clinical usage confronts the limitations of restricted interpretation and insufficient explanation of the WAI outcomes. This paper proposes a multimodal machine learning (MML) approach for classifying ME diseases into normal ear and ear with abnormalalty i.e., otitis media with effusion. The proposed MML model is grounded on the integration of a 3 layered convolutional neural network and a multi-layer perception network. The outcomes exhibited that the proposed MML model surpasses the available methods by achieving 98.27% accuracy for classifying ME diseases using the WAI measurements.

AB - Wideband acoustic immittance (WAI) technology has been known for over a decade, delivering an enhanced diagnosis of middle ear (ME) diseases across a wider frequency range than standard tympanometry. Nevertheless, its clinical usage confronts the limitations of restricted interpretation and insufficient explanation of the WAI outcomes. This paper proposes a multimodal machine learning (MML) approach for classifying ME diseases into normal ear and ear with abnormalalty i.e., otitis media with effusion. The proposed MML model is grounded on the integration of a 3 layered convolutional neural network and a multi-layer perception network. The outcomes exhibited that the proposed MML model surpasses the available methods by achieving 98.27% accuracy for classifying ME diseases using the WAI measurements.

KW - Computer science and informatics

KW - Accuracy

KW - Wideband acoustic immittance

KW - convolutional neural network

KW - machine learning

KW - multi-layer perception

U2 - 10.1109/CCNC54725.2025.10976051

DO - 10.1109/CCNC54725.2025.10976051

M3 - Paper

T2 - 2025 IEEE 22nd Consumer Communications & Networking Conference (CCNC)

Y2 - 10 January 2025 through 13 January 2025

ER -

A multimodal machine learning framework for diagnosis of otitis media with effusion using 3D wideband acoustic immittance

Abstract

Conference

Bibliographical note

Keywords

Access to Document

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Research output

A multimodal machine learning framework for diagnosis of otitis media with effusion using 3D wideband acoustic immittance

Cite this