Evidence map›Paper›PMID 41977866›Full record

ReviewSensors (Basel, Switzerland)2026

A Review and Perspective of Techniques for Autonomous Robotic Ultrasound Acquisitions.

Yanding Qin, Lele Dang, Fan Ren, Zhuomao Li, Lijun Duan, Hongpeng Wang, Jianda Han

Abstract readReview
In one paragraph

Review in Sensors (Basel, Switzerland), 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

7 authors.

Yanding QinCollege of Artificial Intelligence, Nankai University, Tianjin 300350, China.ORCID 0000-0001-5162-1665
Lele DangCollege of Artificial Intelligence, Nankai University, Tianjin 300350, China.
Fan RenCollege of Artificial Intelligence, Nankai University, Tianjin 300350, China.
Zhuomao LiCollege of Artificial Intelligence, Nankai University, Tianjin 300350, China.ORCID 0009-0000-1697-7623
Lijun DuanDepartment of Endocrinology and Metabolism, Tianjin First Center Hospital, Tianjin 300190, China.
Hongpeng WangCollege of Artificial Intelligence, Nankai University, Tianjin 300350, China.
Jianda HanCollege of Artificial Intelligence, Nankai University, Tianjin 300350, China.ORCID 0000-0002-9664-4534

Funding

Beijing Tianjin Hebei Basic Research Cooperation Special Project 24JCZXJC00060Shenzhen Science and Technology Program KQTD20210811090143060
6 · The paper itself

Abstract

Ultrasound (US) imaging is a widely used diagnostic method in clinics. Real-time-generated US images are used for rapid diagnosis without harm to patients. The quality of US imaging highly depends on the skill of the physician due to the differences among physicians. Techniques for autonomous robotic ultrasound (AU-RUS) acquisitions are expected to become an effective means to improve the level of US diagnosis, reduce the workload of physicians, and improve the standardization of US imaging quality. This paper aims to summarize the current research status of techniques for AU-RUS acquisitions, and to discuss the research trends and challenges regarding related technologies. Firstly, the techniques for AU-RUS acquisitions and systems are outlined. The techniques for teleoperated or autonomous US acquisitions are briefly discussed. Representative RUS acquisition systems are introduced. Then, the current research status of AU-RUS acquisitions is reviewed from four research directions: force sensitivity and control, scanning path-planning and positioning, US treatment guidance, and US image processing technology and quality assessment optimization. This review provides a decision-oriented autonomy perspective by mapping typical methods to workflow components across the stages of perception, decision-making, and execution. We identify major deployment bottlenecks, including safety-verifiable autonomy and failure recovery, motion compensation under deformation, and the lack of standardized, clinically meaningful US image quality metrics. Finally, the shortcomings of current research are summarized and analyzed, and the research trends and challenges for AU-RUS acquisitions are prospected.

Indexed as

Image Processing, Computer-AssistedRoboticsAutonomous RobotsHumansUltrasonographyautonomous robotic systemmedical image processingmedical roboticsrobotic ultrasound acquisitionultrasound imaging

Identifiers

PMID41977866
PMCPMC13074658

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.