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Image-Based Flow Prediction of vocal folds Using 3D Convolutional Neural Networks

Journal of Bionic Engineering 2024年第2期21卷 991-1002页

作者： Yang Zhang Tianmei Pu Jiasen Xu Chunhua Zhou College of Astronautics Nanjing University of Aeronautics and AstronauticsNanjing210016China College of Electrical Energy and Power EngineeringYangzhou UniversityYangzhou225127China College of Aeronautics Nanjing University of Aeronautics and AstronauticsNanjing210016China

In this work,a three dimensional(3D)convolutional neural network(CNN)model based on image slices of various normal and pathological vocal folds is proposed for accurate and efficient prediction of glottal *** 3D CNN model is composed of the feature extraction block and regression *** feature extraction block is capable of learning low dimensional features from the high dimensional image data of the glottal shape,and the regression block is employed to flatten the output from the feature extraction block and obtain the desired glottal flow *** input image data is the condensed set of 2D image slices captured in the axial plane of the 3D vocal folds,where these glottal shapes are synthesized based on the equations of normal vibration *** output flow data is the corresponding flow rate,averaged glottal pressure and nodal pressure distributions over the glottal *** 3D CNN model is built to establish the mapping between the input image data and output flow *** ground-truth flow variables of each glottal shape in the training and test datasets are obtained by a high-fidelity sharp-interface immersed-boundary *** proposed model is trained to predict the concerned flow variables for glottal shapes in the test *** present 3D CNN model is more efficient than traditional Computational Fluid Dynamics(CFD)models while the accuracy can still be retained,and more powerful than previous data-driven prediction models because more details of the glottal flow can be *** prediction performance of the trained 3D CNN model in accuracy and efficiency indicates that this model could be promising for future clinical applications.

关键词： vocal folds Computational fluid dynamics Machine learning 3D convolutional neural network

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Stroboscopic Evaluation of the vocal folds’ Morphometric Characteristics, between the Different Voice Categories on Professional Singers

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International Journal of Otolaryngology and Head & Neck Surgery 2021年第5期10卷 345-358页

作者： Athina Zarachi Aikaterini Lianou Angelos Liontos Konstantinos Garefis Eleni Litsou Dionysios Tafiadis Efthymis Dimakis Ioannis Kastanioudakis Georgios Exarchakos Department of Otorhinolaryngology Head and Neck Surgery Faculty of Medicine School of Health Sciences University of Ioannina Ioannina Greece Department of Internal Medicine Faculty of Medicine School of Health Sciences University of Ioannina Ioannina Greece 2nd Academic ORL Head and Neck Surgery Department Papageorgiou Hospital Aristotle University of Thessaloniki Thessaloniki Greece Department of Speech & Language Therapy School of Health Sciences University of Ioannina Ioannina Greece Department of Health Sciences Speech and Language Therapy European University Cyprus Nicosia Cyprus Division of Biostatistics and Epidemiology Department of Medical School University of Ioannina Ioannina Greece

Objective(s): The aim of this study is to explore if there is a correlation between the typical voice classification and the morphometric characteristics of the vocal folds, using video laryngeal stroboscopy, on professional singers in Greece. Methods: 70 professional singers, 50% men (35 men) and 50% women (35 women), were recruited for this study. A stroboscopic evaluation was prepared for every participant. Additionally, the voice classification and features of individuals were correlated statistically. Results: Statistically significant difference were observed between the voice categories and the size of stomatopharynx, the shape of the hard palate, the length of the vocal folds, the width and thickness of the vocal folds, as well as the tone of speech, in male singers. Statistically significant differences were also observed between the voice categories and all the variables except the shape of epiglottis, the color of speech and the shape of the vocal folds, in female singers. Conclusions: There is correlation between the morphometric characteristics of the vocal folds and the voice categories.

关键词： Stroboscopy vocal folds Professional Singers Voice Category Morphometric Characteristics

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