Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

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

Imaging-Derived Sarcopenic Obesity and Cardiovascular Outcomes: Insights Into Heart Failure Risk and Muscle Biology.

Journal of the American College of Cardiology·2026

Same author

Menstrual Cycle Phase Does Not Influence Training-Induced Muscle Hypertrophy or Strength: A Randomized Controlled Trial.

Medicine and science in sports and exercise·2026

Same author

A network-based atlas of human skeletal muscle aging.

medRxiv : the preprint server for health sciences·2026

Same author

Skeletal muscle metabolomic markers underlying the enhanced exercise-induced hypertrophy response to resistance training in older adults.

GeroScience·2026

Same author

Selective and Long-Term Stable Ammonia Electrolysis Using Pt-WO<sub>x</sub> Catalysts with Suppressed NO<sub>x</sub> Formation and Enhanced Activity.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2025

Same author

Oral contraceptive pill phase does not influence muscle protein synthesis or myofibrillar proteolysis at rest or in response to resistance exercise.

Journal of applied physiology (Bethesda, Md. : 1985)·2025

Search research articles

Related Experiment Video

Updated: Nov 7, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

Reverse Scan Conversion and Efficient Deep Learning Network Architecture for Ultrasound Imaging on a Mobile Device.

Kunkyu Lee¹, Min Kim², Changhyun Lim²

¹Department of Electronic Engineering, Sogang University, Seoul 04107, Korea.

Sensors (Basel, Switzerland)

|April 30, 2021

Summary

This study introduces a novel system for point-of-care ultrasound (POCUS) that combines real-time imaging with AI-driven guidance on mobile devices. A new dataset creation method enhances diagnostic accuracy for inexperienced users in emergency settings.

Keywords:

classification mobile system on-device AI point-of-care portable ultrasound

More Related Videos

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Related Experiment Videos

Last Updated: Nov 7, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Area of Science:

Medical Imaging
Artificial Intelligence
Ultrasound Technology

Background:

Point-of-care ultrasound (POCUS) is crucial in emergencies but faces challenges with untrained users.
Cloud-based AI systems for POCUS are limited by security, network, and energy concerns.

Purpose of the Study:

To develop an integrated POCUS system for simultaneous imaging and AI-guided diagnosis on mobile devices.
To improve the accuracy of deep learning models for POCUS by creating a specialized training dataset.

Main Methods:

Proposed a novel structure for integrated ultrasound imaging and mobile device-based AI guidance.
Introduced a reverse scan conversion (RSC) method for generating an ultrasound training dataset.

Main Results:

The integrated system achieved simultaneous ultrasound imaging and deep learning at up to 42.9 frames per second.
The RSC method improved image classification accuracy by over 3%.

Conclusions:

The proposed integrated POCUS system effectively addresses limitations of cloud-based AI.
The RSC method enhances deep learning model performance, improving diagnostic support for POCUS users.