Evidence map›Paper›PMID 38087014›Full record

ArticleScientific reports2023

A noval pulmonary function evaluation method based on ResNet50 + SVR model and cough.

Wenlong Xu, Guoqiang He, Dan Shen, Bingqiao Xu, Peirong Jiang, Feng Liu, Xiaomin Lou, Lingling Guo, Li Ma

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

Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.8field-weighted citation impact, top 23% 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

1 citing paper in PubMed, 3 citations in OpenAlex.

  1. Article
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

9 authors at 6 institutions in 2 countries.

Wenlong XuCollege of Information Engineering, China Jiliang University, Hangzhou, China.
Guoqiang HeCollege of Information Engineering, China Jiliang University, Hangzhou, China.
Dan ShenThe First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China.
Bingqiao XuCollege of Information Engineering, China Jiliang University, Hangzhou, China.
Peirong JiangLishui People's Hospital, Lishui, China.
Feng LiuSchool of Information Technology and Electrical Engineering, University of Queensland, Brisbane, Australia.
Xiaomin LouHangzhou Chest Hospital Affiliated, Zhejiang University Medical College, Hangzhou, China.
Lingling GuoCollege of Chemical Engineering, Zhejiang University of Technology, Hangzhou, China. lguo@zjut.edu.cn.
Li MaZhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, China. contentedness@163.com.
China Jiliang University · CNZhejiang University · CNHangzhou Medical College · CNLishui City People's Hospital · CNQueensland University of Technology · AUZhejiang University of Technology · CN

Funding

Key R&D projects of Zhejiang Province 2023C03107Natural Science Foundation of China 61672476
6 · The paper itself

Abstract

Traditionally, the clinical evaluation of respiratory diseases was pulmonary function testing, which can be used for the detection of severity and prognosis through pulmonary function parameters. However, this method is limited by the complex process, which is impossible for patients to monitor daily. In order to evaluate pulmonary function parameters conveniently with less time and location restrictions, cough sound is the substitute parameter. In this paper, 371 cough sounds segments from 150 individuals were separated into 309 and 62 as the training and test samples. Short-time Fourier transform (STFT) was applied to transform cough sound into spectrogram, and ResNet50 model was used to extract 2048-dimensional features. Through support vector regression (SVR) model with biological attributes, the data were regressed with pulmonary function parameters, FEV1, FEV1%, FEV1/FVC, FVC, FVC%, and the performance of this models was evaluated with fivefold cross-validation. Combines with deep learning and machine learning technologies, the better results in the case of small samples were achieved. Using the coefficient of determination (R

Indexed as

Respiration DisordersRespiratory Tract DiseasesCoughForced Expiratory VolumeHumansLungSpirometryVital Capacity

Identifiers

PMID38087014
PMCPMC10716123
OpenAlexW4389613736

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.