Evidence map›Paper›PMID 42515336›Full record

ArticleSensors (Basel, Switzerland)2026

A Robotic Ultrasound System for Automated Abdominal Aorta Screening: Feasibility Study in Healthy Volunteers.

Yixuan Zheng, Adam Geale, Philipp Kruse, Anoja Paraniroopasingam, Zhiyang Ma, Sarina Singh, Zhouyang Xu, Weizhao Wang, Yang Li, Shichao Zhang and 2 more

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

12 authors.

Yixuan ZhengSchool of Biomedical Engineering and Imaging Sciences, King's College London, London SE1 7EH, UK.ORCID 0009-0004-8798-407X
Adam GealeSchool of Biomedical Engineering and Imaging Sciences, King's College London, London SE1 7EH, UK.ORCID 0009-0009-5903-9237
Philipp KruseSchool of Biomedical Engineering and Imaging Sciences, King's College London, London SE1 7EH, UK.
Anoja ParaniroopasingamSchool of Biomedical Engineering and Imaging Sciences, King's College London, London SE1 7EH, UK.
Zhiyang MaSchool of Biomedical Engineering and Imaging Sciences, King's College London, London SE1 7EH, UK.
Sarina SinghDepartment of Biomedical Engineering, College of Engineering, University of Michigan, Ann Arbor, MI 48109, USA.
Zhouyang XuSchool of Biomedical Engineering and Imaging Sciences, King's College London, London SE1 7EH, UK.ORCID 0009-0004-9611-6514
Weizhao WangSchool of Biomedical Engineering and Imaging Sciences, King's College London, London SE1 7EH, UK.ORCID 0009-0002-0643-1496
Yang LiSchool of Biomedical Engineering and Imaging Sciences, King's College London, London SE1 7EH, UK.
Shichao ZhangSchool of Biomedical Engineering and Imaging Sciences, King's College London, London SE1 7EH, UK.
Richard James HousdenSchool of Biomedical Engineering and Imaging Sciences, King's College London, London SE1 7EH, UK.
Kawal RhodeSchool of Biomedical Engineering and Imaging Sciences, King's College London, London SE1 7EH, UK.ORCID 0000-0002-4707-7318

Funding

China Scholarship CouncilWellcome TrustWellcome Trust WT203148/Z/16/Z
6 · The paper itself

Abstract

Ultrasound is safe, portable, and relatively low cost, and robotic ultrasound research is expanding across many diagnostic applications. Within this context, abdominal aortic aneurysm (AAA) screening remains comparatively unexplored, with few systems reporting end-to-end autonomous scanning and clinician-validated evaluation in volunteers. We present a conditionally autonomous (Level-3) robotic ultrasound system in which the operator defines the region of interest and confirms the target force band, after which the robot performs surface-constrained abdominal sweeps under force control and automatically selects diagnostic frames and estimates aortic diameter without further manual interaction during scanning. The system combines RGB-depth-based patient-to-robot registration, hybrid position-force control with a low-cost force sensor, and a post-acquisition image-analysis pipeline comprising rule-based aorta localisation, a composite image quality assessment (IQA) metric, and a transfer-learned U-Net segmentation baseline. In a feasibility study on ten healthy volunteers spanning BMI 18.6-33 and diverse sex and skin-tone profiles, the robot maintained stable contact within the target force band in all sessions and produced aortic images rated diagnostically acceptable by clinicians in all participants. Automated diameter measurements showed a mean absolute difference of 1.45 mm relative to clinician reference values, with 9/10 cases within 3 mm and all within the 5 mm screening criterion. Volunteer questionnaires indicated high levels of comfort and trust in the system. These results demonstrate the feasibility of operator-supervised, force-aware robotic AAA scanning and highlight the potential of low-cost robotic ultrasound for wider automated vascular imaging.

Indexed as

Aorta, AbdominalRoboticsAdultAortic Aneurysm, AbdominalFeasibility StudiesFemaleHumansImage Processing, Computer-AssistedMaleMiddle AgedUltrasonographyabdominal aortic aneurysmautonomous robotic ultrasoundforce controlimage quality assessmentmedical image segmentationultrasound screening

Identifiers

PMID42515336
PMCPMC13416603

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.