Evidence map›Paper›PMID 39409511›Full record

ReviewSensors (Basel, Switzerland)2024

Recent Advancements in High-Frequency Ultrasound Applications from Imaging to Microbeam Stimulation.

Min Gon Kim, Changhan Yoon, Hae Gyun Lim

Abstract readReview
In one paragraph

Review in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

  1. Article
  2. Article
  3. Recent advances in transducers for through-tissue ultrasonic power transfer.Progress in biomedical engineering (Bristol, England) · 2026
    Review
  4. Article
  5. Article
  6. Review
  7. Review
  8. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Min Gon KimDepartment of Biomedical Engineering, University of Southern California, Los Angeles, CA 90007, USA.
Changhan YoonDepartment of Biomedical Engineering, Inje University, Gimhae 50834, Republic of Korea.
Hae Gyun LimDepartment of Biomedical Engineering, Pukyong National University, Busan 48547, Republic of Korea.ORCID 0000-0002-4182-7130

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Ultrasound is a versatile and well-established technique using sound waves with frequencies higher than the upper limit of human hearing. Typically, therapeutic and diagnosis ultrasound operate in the frequency range of 500 kHz to 15 MHz with greater depth of penetration into the body. However, to achieve improved spatial resolution, high-frequency ultrasound (>15 MHz) was recently introduced and has shown promise in various fields such as high-resolution imaging for the morphological features of the eye and skin as well as small animal imaging for drug and gene therapy. In addition, high-frequency ultrasound microbeam stimulation has been demonstrated to manipulate single cells or microparticles for the elucidation of physical and functional characteristics of cells with minimal effect on normal cell physiology and activity. Furthermore, integrating machine learning with high-frequency ultrasound enhances diagnostics, including cell classification, cell deformability estimation, and the diagnosis of diabetes and dysnatremia using convolutional neural networks (CNNs). In this paper, current efforts in the use of high-frequency ultrasound from imaging to stimulation as well as the integration of deep learning are reviewed, and potential biomedical and cellular applications are discussed.

Indexed as

UltrasonographyAnimalsHumansNeural Networks, ComputerUltrasonic Waveshigh-frequency ultrasound imaginghigh-frequency ultrasound microbeammachine learning with high-frequency ultrasonic signals

Identifiers

PMID39409511
PMCPMC11479296

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.