Evidence map›Paper›PMID 35171218›Full record

ArticleJournal of biomechanical engineering2022

A Deep Learning-Based Generalized Empirical Flow Model of Glottal Flow During Normal Phonation.

Yang Zhang, Weili Jiang, Luning Sun, Jianxun Wang, Xudong Zheng, Qian Xue

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of biomechanical engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.6field-weighted citation impact, top 35% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 5 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 2 institutions in 1 country.

Yang ZhangDepartment of Mechanical Engineering, University of Maine, Orono, ME 04469.
Weili JiangDepartment of Mechanical Engineering, University of Maine, 204 Crosby Hall, Orono, ME 04473.
Luning SunDepartment of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN 46556.
Jianxun WangDepartment of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN 46556.
Xudong ZhengDepartment of Mechanical Engineering, University of Maine, Room 213 A, Boardman Hall, Orono, ME 04473.
Qian XueDepartment of Mechanical Engineering, University of Maine, Room 213, Boardman Hall, Orono, ME 04473.
University of Maine · USUniversity of Notre Dame · US

Funding

Development of an Accurate and Real-Time Voice Simulator for Voice DisordersR21DC016428 · NIDCD · UNIVERSITY OF MAINE ORONO · PI TITZE, INGO R, XUE, QIAN · 2018 to 2019
$407k
NIDCD NIH HHS R21 DC016428
6 · The paper itself

Abstract

This paper proposes a deep learning-based generalized empirical flow model (EFM) that can provide a fast and accurate prediction of the glottal flow during normal phonation. The approach is based on the assumption that the vibration of the vocal folds can be represented by a universal kinematics equation (UKE), which is used to generate a glottal shape library. For each shape in the library, the ground truth values of the flow rate and pressure distribution are obtained from the high-fidelity Navier-Stokes (N-S) solution. A fully connected deep neural network (DNN) is then trained to build the empirical mapping between the shapes and the flow rate and pressure distributions. The obtained DNN-based EFM is coupled with a finite element method (FEM)-based solid dynamics solver for fluid-structure-interaction (FSI) simulation of phonation. The EFM is evaluated by comparing the N-S solutions in both static glottal shapes and FSI simulations. The results demonstrate a good prediction performance in accuracy and efficiency.

Indexed as

Deep LearningComputer SimulationGlottisModels, BiologicalPhonationVibrationVocal Cords

Identifiers

PMID35171218
PMCPMC8990722
OpenAlexW4213244198

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.