Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Classification of Signals01:30

Classification of Signals

532
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
532
Deconvolution01:20

Deconvolution

188
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
188
Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

720
Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
720

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Characteristics of Respiratory Microbiome in COPD-A Literature Review.

Advances in respiratory medicine·2026
Same author

Myocardial infarction due to delayed coronary obstruction after TAVI.

Kardiologia polska·2026
Same author

Retinal microcirculation as a window to coronary artery disease.

European journal of internal medicine·2026
Same author

Nucleotide Variant in the <i>SLC26A9</i> Gene in Two Siblings with Cystic Fibrosis.

Journal of clinical medicine·2026
Same author

Advancing anatomical education: comparative effectiveness of mixed reality and 3D printing technologies.

BMC medical education·2026
Same author

Safety of Performing Spirometry During Pregnancy: A Systematic Review.

Advances in respiratory medicine·2026

Related Experiment Video

Updated: Jul 19, 2025

Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing
06:25

Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing

Published on: February 23, 2024

630

Deep learning algorithm for visual quality assessment of the spirograms.

Damian Waląg1, Mateusz Soliński2, Łukasz Kołtowski3

  • 1Faculty of Physics, Warsaw University of Technology, Koszykowa St. 75, 00-662, Warsaw, Poland.

Physiological Measurement
|August 8, 2023
PubMed
Summary
This summary is machine-generated.

An automatic algorithm using a convolutional neural network (CNN) can accurately assess spirometry curve quality, improving test reliability, especially in unsupervised settings. This AI tool aids specialists in evaluating large datasets efficiently.

Keywords:
convolutional neural networkflow-volume curvequality assessmentspirometry

More Related Videos

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
04:17

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

801
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.2K

Related Experiment Videos

Last Updated: Jul 19, 2025

Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing
06:25

Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing

Published on: February 23, 2024

630
DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
04:17

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

801
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.2K

Area of Science:

  • Pulmonary Function Testing
  • Medical Imaging Analysis
  • Artificial Intelligence in Healthcare

Background:

  • Spirometry quality is vital for accurate interpretation of pulmonary function parameters.
  • Current American Thoracic Society and European Respiratory Society (ATS/ERS) standards require manual visual evaluation of spirometry curves.
  • Automated assessment of quantitative criteria exists, but visual assessment remains a bottleneck.

Purpose of the Study:

  • To develop and evaluate a convolutional neural network (CNN) algorithm for automatic quality assessment of spirometry curves.
  • To provide an alternative to manual verification of spirogram acceptability and usability.
  • To enhance the efficiency and consistency of spirometry quality control.

Main Methods:

  • A dataset of 1998 spirograms meeting ATS/ERS quantitative criteria was used.
  • Pulmonologists annotated each spirogram as 'confirm' or 'reject' for FEV1 and FVC.
  • A CNN classification algorithm was developed and optimized using cross-validation on an 80% training and 20% test split.

Main Results:

  • The CNN algorithm achieved high accuracy, sensitivity, and specificity for both FEV1 (92.6%, 93.1%, 90.0%) and FVC (94.1%, 95.6%, 88.3%).
  • The algorithm demonstrated robust performance in classifying spirometry curve quality.
  • Results indicate the algorithm's potential for reliable automated quality assessment.

Conclusions:

  • The developed CNN algorithm offers a significant improvement in spirometry test quality assessment.
  • It is particularly beneficial for unsupervised spirometry and can streamline quality control in clinical trials.
  • This automated tool can serve as a valuable adjunct to specialist review for large-scale spirometry data analysis.