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

IF 2.9 4区 医学 Q3 ENGINEERING, BIOMEDICAL
Shuaichi Ma, Wenwen Liao, Yi Zhang, Fan Zhang, Yimiao Wang, Zhiyan Lu, Chen Zhao, Jianbo Yu, Peijie He
{"title":"Research on automatic assessment of the severity of unilateral vocal cord paralysis based on Mel-spectrogram and convolutional neural networks.","authors":"Shuaichi Ma, Wenwen Liao, Yi Zhang, Fan Zhang, Yimiao Wang, Zhiyan Lu, Chen Zhao, Jianbo Yu, Peijie He","doi":"10.1186/s12938-025-01401-9","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>This 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.</p><p><strong>Methods: </strong>To 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.</p><p><strong>Results: </strong>TripleConvNet achieved a classification accuracy of 74.3% in distinguishing between healthy voices and the UVCP decompensated, partially compensated, and fully compensated groups.</p><p><strong>Conclusion: </strong>This 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.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"24 1","pages":"76"},"PeriodicalIF":2.9000,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12181906/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"BioMedical Engineering OnLine","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1186/s12938-025-01401-9","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
引用次数: 0

Abstract

Background: This 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.

Methods: To 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.

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

Conclusion: This 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.

基于mel -谱图和卷积神经网络的单侧声带麻痹严重程度自动评估研究。
背景:本研究旨在开发基于ai的平台,利用mel谱图分析和卷积神经网络(CNN),通过语音分析实现单侧声带麻痹(UVCP)严重程度的自动化评估,为个性化临床治疗方案提供客观依据。方法:为了准确识别UVCP的严重程度,本研究建立了CNN模型TripleConvNet。采集复旦大学眼科耳鼻喉科医院131名健康个体和292名UVCP确诊患者的语音样本。根据声带代偿功能将患者分为失代偿组(84例)、部分代偿组(98例)和完全代偿组(110例)。使用mel谱图及其一阶和二阶差分特征作为输入,TripleConvNet模型按严重程度对患者进行分类,并系统地评估其在UVCP严重程度分级任务中的表现。结果:TripleConvNet在区分健康声音和UVCP失代偿、部分代偿和完全代偿组方面的分类准确率为74.3%。结论:本研究证明了基于深度学习的非侵入性语音分析对UVCP严重程度进行精确分级的潜力。提出的方法提供了一个有前途的临床工具,以协助医生在疾病评估和个性化的治疗计划。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
BioMedical Engineering OnLine
BioMedical Engineering OnLine 工程技术-工程:生物医学
CiteScore
6.70
自引率
2.60%
发文量
79
审稿时长
1 months
期刊介绍: BioMedical Engineering OnLine is an open access, peer-reviewed journal that is dedicated to publishing research in all areas of biomedical engineering. BioMedical Engineering OnLine is aimed at readers and authors throughout the world, with an interest in using tools of the physical and data sciences and techniques in engineering to understand and solve problems in the biological and medical sciences. Topical areas include, but are not limited to: Bioinformatics- Bioinstrumentation- Biomechanics- Biomedical Devices & Instrumentation- Biomedical Signal Processing- Healthcare Information Systems- Human Dynamics- Neural Engineering- Rehabilitation Engineering- Biomaterials- Biomedical Imaging & Image Processing- BioMEMS and On-Chip Devices- Bio-Micro/Nano Technologies- Biomolecular Engineering- Biosensors- Cardiovascular Systems Engineering- Cellular Engineering- Clinical Engineering- Computational Biology- Drug Delivery Technologies- Modeling Methodologies- Nanomaterials and Nanotechnology in Biomedicine- Respiratory Systems Engineering- Robotics in Medicine- Systems and Synthetic Biology- Systems Biology- Telemedicine/Smartphone Applications in Medicine- Therapeutic Systems, Devices and Technologies- Tissue Engineering
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信