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

Ultrasound II: Endoscopic Ultrasound and FibroScan01:25

Ultrasound II: Endoscopic Ultrasound and FibroScan

560
Endoscopic Ultrasound (EUS) and FibroScan are valuable diagnostic tools in gastroenterology and hepatology, each with specific applications and techniques.
Endoscopic Ultrasound (EUS):
560
Imaging Studies II: Ultrasonography01:24

Imaging Studies II: Ultrasonography

364
IntroductionUltrasonography, or renal ultrasound, is a noninvasive medical imaging technique that uses high-frequency sound waves to visualize the kidneys, ureters, bladder, and surrounding tissues.Indications for Urinary System UltrasonographyUrinary system ultrasonography is indicated in various clinical scenarios, such as:Kidney Stones (Urolithiasis): To detect and monitor the size and presence of kidney or urinary tract stones.Hydronephrosis: To assess the dilation of the renal pelvis and...
364

You might also read

Related Articles

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

Sort by
Same author

Integrated surveillance resolves Darién paradox of Oropouche virus emergence in Panama's migration corridor.

Research square·2026
Same author

The Canonical Wnt Surrogate Agonist scFv-Dkk1c Ameliorates Spinal Cord Injury in Rats.

Molecular neurobiology·2026
Same author

Revisiting the Deep Fatty and Fascial Layers Between the Temple and the Midface: An Ultrasound-based Investigation.

Aesthetic surgery journal·2026
Same author

Doppler Ultrasound Findings in Filler-Related Facial Vascular Adverse Events: An International Multicenter Study.

Diagnostics (Basel, Switzerland)·2026
Same author

Autoimmune/Inflammatory Syndrome Induced by Adjuvants (ASIA) in a Patient With Silicone Breast Implants and Scleroderma-Like Manifestations: A Case Report.

Cureus·2026
Same author

Prenatal cadmium exposure and behavioural and emotional problems across childhood: findings from the INMA birth cohort.

Environmental research·2026

Related Experiment Video

Updated: Jan 16, 2026

Non-invasive Parenchymal, Vascular and Metabolic High-frequency Ultrasound and Photoacoustic Rat Deep Brain Imaging
12:00

Non-invasive Parenchymal, Vascular and Metabolic High-frequency Ultrasound and Photoacoustic Rat Deep Brain Imaging

Published on: March 2, 2015

12.6K

Artificial Intelligence Deep Learning Ultrasound Discrimination of Cosmetic Fillers: A Multicenter Study.

Ximena Wortsman1,2,3,4, Manuel Lozano5, Francisco Javier Rodriguez5

  • 1Department of Dermatology, Faculty of Medicine, Universidad de Chile, Santiago, Chile.

Journal of Ultrasound in Medicine : Official Journal of the American Institute of Ultrasound in Medicine
|September 30, 2025
PubMed
Summary

Artificial intelligence (AI) effectively distinguishes hyaluronic acid (HA) and silicone oil (SO) cosmetic fillers using ultrasound (US) imaging. Further deep learning advancements are needed for precise identification of calcium hydroxyapatite (CaHA) and polymethylmethacrylate (PMMA) fillers.

Keywords:
aestheticartificial intelligencecosmeticdeep learningdermatologyfillersultrasound

More Related Videos

Universal Hand-held Three-dimensional Optoacoustic Imaging Probe for Deep Tissue Human Angiography and Functional Preclinical Studies in Real Time
09:56

Universal Hand-held Three-dimensional Optoacoustic Imaging Probe for Deep Tissue Human Angiography and Functional Preclinical Studies in Real Time

Published on: November 4, 2014

11.2K
Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment
13:01

Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment

Published on: June 3, 2022

4.5K

Related Experiment Videos

Last Updated: Jan 16, 2026

Non-invasive Parenchymal, Vascular and Metabolic High-frequency Ultrasound and Photoacoustic Rat Deep Brain Imaging
12:00

Non-invasive Parenchymal, Vascular and Metabolic High-frequency Ultrasound and Photoacoustic Rat Deep Brain Imaging

Published on: March 2, 2015

12.6K
Universal Hand-held Three-dimensional Optoacoustic Imaging Probe for Deep Tissue Human Angiography and Functional Preclinical Studies in Real Time
09:56

Universal Hand-held Three-dimensional Optoacoustic Imaging Probe for Deep Tissue Human Angiography and Functional Preclinical Studies in Real Time

Published on: November 4, 2014

11.2K
Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment
13:01

Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment

Published on: June 3, 2022

4.5K

Area of Science:

  • Dermatology and Medical Imaging
  • Artificial Intelligence in Medicine
  • Acoustic Imaging and Analysis

Background:

  • Cosmetic filler identification is crucial in dermatologic and aesthetic ultrasound (US).
  • Current diagnostic methods lack AI-driven discrimination capabilities for various filler types.
  • Need for objective, AI-powered tools to differentiate cosmetic fillers on US.

Purpose of the Study:

  • To investigate the efficacy of artificial intelligence (AI) in discriminating cosmetic fillers using ultrasound (US).
  • To develop and evaluate a deep learning (DL) model for identifying hyaluronic acid (HA), polymethylmethacrylate (PMMA), calcium hydroxyapatite (CaHA), and silicone oil (SO) fillers.
  • To assess the performance of the YOLO (you only look once) architecture in real-world US imaging conditions.

Main Methods:

  • An international collaborative group gathered and processed 1432 anonymized US images.
  • Deep learning (DL) techniques utilizing the YOLO (you only look once) architecture were employed.
  • Experts manually delineated regions of interest using a bounding box annotation tool for filler discrimination.

Main Results:

  • The AI model achieved a robust average accuracy of 0.92 ± 0.04 across cross-validation folds.
  • YOLOv11 demonstrated outstanding performance in detecting HA (F1 score: 0.96 ± 0.02) and SO (F1 score: 0.94 ± 0.04).
  • CaHA and PMMA identification showed less consistent performance, with F1 scores around 0.83.

Conclusions:

  • AI, specifically YOLOv11, reliably discriminates between HA and SO fillers across various US devices and operators.
  • Further AI and deep learning (DL) research is required to improve the accurate identification of CaHA and PMMA fillers.
  • This study highlights the potential of AI in advancing cosmetic filler characterization via ultrasound.