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

You might also read

Related Articles

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

Sort by
Same author

Female Gastroenterologists Report Mastering Endoscopic Skills Later and Are Less Likely to Have Children. A Regional Survey.

Journal of gastrointestinal and liver diseases : JGLD·2025
Same author

The Role of Easy-to-use Non-invasive Scores in the Assessment of Hepatocellular Carcinoma Prognosis - Data from the Romanian Hepatocellular Carcinoma Registry.

Journal of gastrointestinal and liver diseases : JGLD·2025
Same author

Early diagnosis of gallbladder cancer in high-risk population focusing on ultrasound and CT imaging.

Medical ultrasonography·2025
Same author

Overexpression of IL-6 and STAT3 may provide new insights into ovine pulmonary adenocarcinoma development.

BMC veterinary research·2025
Same author

Integration of ultrasound and microwave pretreatments with solid-state fermentation enhances the release of sugars, organic acids, and phenolic compounds in wheat bran.

Food chemistry·2024
Same author

Impact of Coffee Consumption on Subjective Perception and Inflammatory Markers in Patients with Inflammatory Bowel Diseases.

Biomedicines·2024

Related Experiment Video

Updated: Oct 1, 2025

A High-Throughput Multiplexed Screening for Type 1 Diabetes, Celiac Diseases, and COVID-19
06:46

A High-Throughput Multiplexed Screening for Type 1 Diabetes, Celiac Diseases, and COVID-19

Published on: July 5, 2022

2.9K

Automated detection of celiac disease using Machine Learning Algorithms.

Cristian-Andrei Stoleru1, Eva H Dulf2, Lidia Ciobanu3

  • 1Automation Department, Faculty of Automation and Computer Science, Technical University of Cluj-Napoca, Memorandumului 28, 400014, Cluj-Napoca, Romania.

Scientific Reports
|March 9, 2022
PubMed
Summary
This summary is machine-generated.

A new AI-powered method uses capsule endoscopy images to diagnose celiac disease. This non-invasive approach identifies specific visual cues, offering a faster and accurate alternative to traditional biopsies.

More Related Videos

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.0K
A High-Throughput Electrochemiluminescence 7-Plex Assay Simultaneously Screening for Type 1 Diabetes and Multiple Autoimmune Diseases
06:50

A High-Throughput Electrochemiluminescence 7-Plex Assay Simultaneously Screening for Type 1 Diabetes and Multiple Autoimmune Diseases

Published on: May 29, 2020

2.7K

Related Experiment Videos

Last Updated: Oct 1, 2025

A High-Throughput Multiplexed Screening for Type 1 Diabetes, Celiac Diseases, and COVID-19
06:46

A High-Throughput Multiplexed Screening for Type 1 Diabetes, Celiac Diseases, and COVID-19

Published on: July 5, 2022

2.9K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.0K
A High-Throughput Electrochemiluminescence 7-Plex Assay Simultaneously Screening for Type 1 Diabetes and Multiple Autoimmune Diseases
06:50

A High-Throughput Electrochemiluminescence 7-Plex Assay Simultaneously Screening for Type 1 Diabetes and Multiple Autoimmune Diseases

Published on: May 29, 2020

2.7K

Area of Science:

  • Gastroenterology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Celiac disease diagnosis relies on invasive endoscopy and time-consuming histology.
  • Current AI methods for capsule endoscopy images often use complex algorithms.
  • There is a need for faster, non-invasive diagnostic tools for celiac disease.

Purpose of the Study:

  • To develop a simplified AI approach for diagnosing celiac disease using capsule endoscopy images.
  • To demonstrate the efficacy of identifying specific endoscopic artifacts for diagnosis.
  • To provide a computationally efficient diagnostic aid.

Main Methods:

  • Utilized capsule endoscopy images for non-invasive small bowel examination.
  • Developed an AI algorithm employing specific kernels to detect visual artifacts (mucosal atrophy, cracks, fold changes, villi reduction).
  • Applied classified machine learning algorithms to analyze detected artifacts, avoiding complex deep learning models.

Main Results:

  • Achieved a diagnostic accuracy of 94.1% and an F1 score of 94% in processing capsule endoscopy images.
  • The method successfully identified subtle signs of villous atrophy not apparent by visual inspection.
  • Demonstrated that simplified algorithms can yield competitive diagnostic performance.

Conclusions:

  • Computer-aided diagnosis of celiac disease is feasible using non-invasive capsule endoscopy and simplified AI algorithms.
  • The proposed method offers a computationally inexpensive and efficient alternative to complex AI approaches.
  • This tool can assist in early detection and monitoring of treatment response in celiac disease patients.