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

Imaging Studies II: Positron Emission Tomography and Scintigraphy01:25

Imaging Studies II: Positron Emission Tomography and Scintigraphy

682
Positron Emission Tomography (PET) is a medical imaging technique that provides crucial insights into the body's physiological functions at a molecular level. It is an indispensable resource for diagnosing, staging, and monitoring various illnesses, notably cancer, neurological disorders, and cardiovascular conditions.
Fundamental Principles of PET
682
Positron Emission Tomography01:29

Positron Emission Tomography

7.9K
Positron emission tomography (PET) is a medical imaging technique involving radiopharmaceuticals — substances that emit short-lived radiation. Although the first PET scanner was introduced in 1961, it took 15 more years before radiopharmaceuticals were combined with the technique and revolutionized its potential.
One of the main requirements of a PET scan is a positron-emitting radioisotope, which is produced in a cyclotron and then attached to a substance used by the part of the body...
7.9K

You might also read

Related Articles

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

Sort by
Same author

Exercise-stimulated primary cilia on preosteoclasts promote periosteal-bone formation.

Experimental & molecular medicine·2026
Same author

Protective association of free flap reconstruction with vascular restenosis after angioplasty in patients with diabetic foot.

Journal of plastic, reconstructive & aesthetic surgery : JPRAS·2026
Same author

The Association between Sarcopenia and Chronic Kidney Disease among the Patients of Diabetes Mellitus: Korean National Health and Nutrition Examination Survey 2008-2011.

Journal of bone metabolism·2026
Same author

Legal Infoveillance of Unlicensed Medical Practices in South Korea Through Criminal Court Decisions Using Machine Learning: Retrospective Observational Study.

JMIR public health and surveillance·2026
Same author

Surgical outcomes of Bethesda System for Reporting Thyroid Cytopathology diagnostic category class I, II, and III thyroid nodules.

Frontiers in endocrinology·2026
Same author

The reconstructive burden of inter-hospital transfers: resource utilization and clinical implications in tertiary trauma care.

Frontiers in surgery·2026
Same journal

A new low uncertainty measurement of the <sup>111</sup>Ag half-life.

Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine·2026
Same journal

Novel PdO-modified borate glasses with improved radiation shielding and mechanical features.

Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine·2026
Same journal

Assessment of radionuclide contents and radiological health risks of infant formulae consumed in Türkiye.

Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine·2026
Same journal

Radiation surveillance during cyclotron commissioning and pilot-scale FDG production at the Institute of Nuclear Medical Physics.

Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine·2026
Same journal

Energy-dependent shielding performance of high-Z epoxy composite shielding media for radioactive waste drums: A Monte Carlo transport and dose-rate analysis.

Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine·2026
Same journal

Radiological performance of cemented FMA-VC radioactive waste packages for transport: A PHITS Monte Carlo study.

Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine·2026
See all related articles

Related Experiment Video

Updated: Mar 6, 2026

Automated 90Sr Separation and Preconcentration in a Lab-on-Valve System at Ppq Level
08:53

Automated 90Sr Separation and Preconcentration in a Lab-on-Valve System at Ppq Level

Published on: June 6, 2018

8.5K

Feasibility study of fast nuclide identification for beta-emitting sources using learning-based models.

Min Ji Kim1, Hee Reyoung Kim1

  • 1Nuclear Engineering, Ulsan National Institute of Science and Technology, Ulsan, 44919, Republic of Korea.

Applied Radiation and Isotopes : Including Data, Instrumentation and Methods for Use in Agriculture, Industry and Medicine
|March 4, 2026
PubMed
Summary
This summary is machine-generated.

This study demonstrates a fast, AI-powered method for identifying beta-emitting radionuclides using beta spectrum data. This approach enhances radiological safety by enabling rapid nuclide identification in emergencies and nuclear facilities.

Keywords:
Artificial intelligenceBeta-emitting radionuclidesFast identificationSupport vector machineTime-series classification with transformer

More Related Videos

Visualization of Low-Level Gamma Radiation Sources Using a Low-Cost, High-Sensitivity, Omnidirectional Compton Camera
06:28

Visualization of Low-Level Gamma Radiation Sources Using a Low-Cost, High-Sensitivity, Omnidirectional Compton Camera

Published on: January 30, 2020

13.3K
A Basic Positron Emission Tomography System Constructed to Locate a Radioactive Source in a Bi-dimensional Space
14:19

A Basic Positron Emission Tomography System Constructed to Locate a Radioactive Source in a Bi-dimensional Space

Published on: February 1, 2016

9.0K

Related Experiment Videos

Last Updated: Mar 6, 2026

Automated 90Sr Separation and Preconcentration in a Lab-on-Valve System at Ppq Level
08:53

Automated 90Sr Separation and Preconcentration in a Lab-on-Valve System at Ppq Level

Published on: June 6, 2018

8.5K
Visualization of Low-Level Gamma Radiation Sources Using a Low-Cost, High-Sensitivity, Omnidirectional Compton Camera
06:28

Visualization of Low-Level Gamma Radiation Sources Using a Low-Cost, High-Sensitivity, Omnidirectional Compton Camera

Published on: January 30, 2020

13.3K
A Basic Positron Emission Tomography System Constructed to Locate a Radioactive Source in a Bi-dimensional Space
14:19

A Basic Positron Emission Tomography System Constructed to Locate a Radioactive Source in a Bi-dimensional Space

Published on: February 1, 2016

9.0K

Area of Science:

  • Nuclear Physics
  • Radiological Science
  • Artificial Intelligence

Background:

  • Rapid identification of beta-emitting radionuclides is crucial for radiological safety during emergencies and in nuclear facilities.
  • Current methods for beta nuclide identification are often slow, requiring chemical preprocessing or sophisticated equipment due to continuous beta spectra.
  • Fast identification is needed for environmental monitoring (e.g., radiostrontium) and immediate response to airborne contamination alarms.

Purpose of the Study:

  • To investigate the feasibility of rapidly identifying beta-emitting radionuclides using machine learning models and beta spectrum data measured in air.
  • To develop and validate an artificial intelligence-based approach for fast beta nuclide identification.
  • To enhance the speed and efficiency of beta nuclide identification for improved radiation protection strategies.

Main Methods:

  • An experimental system was established using beta-disk sources and a suitable detection system.
  • Two learning-based models, Support Vector Machine (SVM) and Time-Series Classification with Transformer (TSCT), were employed.
  • The models were trained to classify 15 nuclide combinations from 60Co, 90Sr/90Y, 137Cs, and 152Eu.

Main Results:

  • The SVM and TSCT models achieved high classification accuracies of 100% and 98.0%, respectively.
  • Both trained models could identify nuclide combinations within seconds under controlled laboratory conditions.
  • The proposed AI-based method significantly enhances the speed of beta nuclide identification.

Conclusions:

  • The study confirms the feasibility of using learning-based models for rapid beta nuclide identification from air-measured beta spectrum data.
  • The developed AI approach offers a significant improvement in the speed of nuclide identification compared to traditional methods.
  • Further validation in variable field conditions is recommended to apply this method to real-world radiation protection scenarios.