Evidence map›Paper›PMID 40544236›Full record

ArticleBiomedical engineering online2025

Research on automatic assessment of the severity of unilateral vocal cord paralysis based on Mel-spectrogram and convolutional neural networks.

Shuaichi Ma, Wenwen Liao, Yi Zhang, Fan Zhang, Yimiao Wang, Zhiyan Lu, Chen Zhao, Jianbo Yu, Peijie He

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PubMed Full text at PMC Publisher
In one paragraph

Article in Biomedical engineering online, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Shuaichi Ma *School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China.
Wenwen Liao *Academy for Engineering &Technology, Fudan University, Shanghai, China.
Yi ZhangENT Institute and Department of Otorhinolaryngology, Eye & ENT Hospital, Fudan University, 83 Fenyang Road, Shanghai, 200031, People's Republic of China.
Fan ZhangENT Institute and Department of Otorhinolaryngology, Eye & ENT Hospital, Fudan University, 83 Fenyang Road, Shanghai, 200031, People's Republic of China.
Yimiao WangENT Institute and Department of Otorhinolaryngology, Eye & ENT Hospital, Fudan University, 83 Fenyang Road, Shanghai, 200031, People's Republic of China.
Zhiyan LuENT Institute and Department of Otorhinolaryngology, Eye & ENT Hospital, Fudan University, 83 Fenyang Road, Shanghai, 200031, People's Republic of China.
Chen ZhaoENT Institute and Department of Otorhinolaryngology, Eye & ENT Hospital, Fudan University, 83 Fenyang Road, Shanghai, 200031, People's Republic of China.
Jianbo YuSchool of Microelectronics, Fudan University, Shanghai, China. jb_yu@fudan.edu.cn.
Peijie HeENT Institute and Department of Otorhinolaryngology, Eye & ENT Hospital, Fudan University, 83 Fenyang Road, Shanghai, 200031, People's Republic of China. hepj2002@sina.com.

Funding

the National Natural Science Foundation of China 81870710the Project of Shanghai Municipal Health Commission 202340219
6 · The paper itself

Abstract

backgroundThis study aims to develop an AI-powered platform using Mel-spectrogram analysis and convolutional neural networks (CNN) to automate the severity assessment of unilateral vocal fold paralysis (UVCP) through voice analysis, providing an objective basis for individualized clinical treatment plans.

methodsTo accurately identify the severity of UVCP, this study developed the CNN model TripleConvNet. Voice samples were collected from 131 healthy individuals and 292 confirmed UVCP patients from the Eye and ENT Hospital of Fudan University. Based on vocal fold compensation function, the patients were divided into three groups: decompensated (84 cases), partially compensated (98 cases), and fully compensated (110 cases). Using Mel-spectrograms and their first- and second-order differential features as inputs, the TripleConvNet model classified patients by severity and was systematically evaluated for its performance in UVCP severity grading tasks.

resultsTripleConvNet achieved a classification accuracy of 74.3% in distinguishing between healthy voices and the UVCP decompensated, partially compensated, and fully compensated groups.

conclusionThis study demonstrates the potential of deep learning-based non-invasive voice analysis for precise grading of UVCP severity. The proposed method offers a promising clinical tool to assist physicians in disease assessment and personalized treatment planning.

Indexed as

Neural Networks, ComputerSignal Processing, Computer-AssistedVocal Cord ParalysisAdultAgedAutomationConvolutional Neural NetworksDeep LearningFemaleHumansMaleMiddle AgedSeverity of Illness IndexSound SpectrographyYoung AdultConvolutional neural networksDeep learningMel-spectrogramUnilateral vocal fold paralysis

Identifiers

PMID40544236
PMCPMC12181906

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LicenceCC BY-NC-ND
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.