Evidence map›Paper›PMID 42076262›Full record

ReviewMicromachines2026

Advances and Challenges of Capacitive Micromachined Ultrasonic Transducers in Medical Imaging.

Yuanyu Yu, Xin Liu, Jiujiang Wang, Shuang Zhang

Abstract readReview
In one paragraph

Review in Micromachines, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

4 authors.

Yuanyu YuSchool of Artificial Intelligence, Neijiang Normal University, Neijiang 641100, China.ORCID 0000-0003-4197-0460
Xin LiuSchool of Mathematics and Computer Science, Northwest Minzu University, Lanzhou 730030, China.ORCID 0000-0002-2859-0837
Jiujiang WangSchool of Artificial Intelligence, Neijiang Normal University, Neijiang 641100, China.ORCID 0000-0001-8964-378X
Shuang ZhangSchool of Artificial Intelligence, Neijiang Normal University, Neijiang 641100, China.ORCID 0000-0002-7134-0454

Funding

2025 Sichuan Provincial Innovative Experimental Project for Undergraduate Universities 143Foundation of the Sichuan Research Center of Applied Psychology (Chengdu Medical College) CSXL-25102Open Project Program of State Key Laboratory of Computer-Aided Design and Computer Graphics (CAD&CG)(Zhejiang University) A2533Open Research Fund of State Key Laboratory of Digital Medical Engineering(Southeast University) 2025-M10Science and Technology Innovation 2030-Brain Science and Brain-inspired Artificial Intelligence Key Project 2022ZD0208506Sichuan Science and Technology Program 2025ZNSFSC0780, 2024NSFSC0611, 2022JDRC0134
6 · The paper itself

Abstract

Capacitive micromachined ultrasonic transducers (CMUTs) have been developed over the past 30 years and achieved practical applications in both medical imaging and industrial non-destructive testing. This article presents the fundamental principles of CMUTs and surveys fabrication technologies, offering a comprehensive review of major advances and challenges in medical ultrasound and photoacoustic imaging applications. The article further reviews and analyzes three primary challenges currently confronting CMUTs in medical imaging applications: lower output acoustic pressure, dielectric charging effects, and the need for high bias voltage. It also presents and discusses a potential combined approach to comprehensively address these challenges, with the aim of enhancing CMUT performance and broadening clinical adoption.

Indexed as

capacitive micromachined ultrasonic transducers (CMUTs)CMUTs fabrication processphotoacoustic imagingultrasound imaging

Identifiers

PMID42076262
PMCPMC13118944

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.