Evidence map›Paper›PMID 42389548›Full record

ArticlePhotoacoustics2026

An intelligent point-of-care photoacoustic and ultrasound dual-modality imaging system for Rheumatoid arthritis in fingers: Technical development and initial clinical test.

Xiaorui Peng, Kyoungmo Koo, Jianwei Qiu, Grigorios Karageorgos, Aaron Dentinger, Zhanpeng Xu, Siddarth Ragupathi, Zhaoyuan Yang, Soumya Ghose, Janggun Jo and 8 more

Abstract read
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In one paragraph

Article in Photoacoustics, 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

18 authors.

Xiaorui PengDepartment of Biomedical Engineering, University of Michigan, 1150 Medical Center Dr, Ann Arbor, MI, 48109, USA.
Kyoungmo KooDepartment of Biomedical Engineering, University of Michigan, 1150 Medical Center Dr, Ann Arbor, MI, 48109, USA.
Jianwei QiuGE HealthCare Technology & Innovation Center, Niskayuna, NY, 12309, USA.
Grigorios KarageorgosGE HealthCare Technology & Innovation Center, Niskayuna, NY, 12309, USA.
Aaron DentingerGE HealthCare Technology & Innovation Center, Niskayuna, NY, 12309, USA.
Zhanpeng XuDepartment of Biomedical Engineering, University of Michigan, 1150 Medical Center Dr, Ann Arbor, MI, 48109, USA.
Siddarth RagupathiDepartment of Biomedical Engineering, University of Michigan, 1150 Medical Center Dr, Ann Arbor, MI, 48109, USA.
Zhaoyuan YangGE HealthCare Technology & Innovation Center, Niskayuna, NY, 12309, USA.
Soumya GhoseGE HealthCare Technology & Innovation Center, Niskayuna, NY, 12309, USA.
Janggun JoDepartment of Biomedical Engineering, University of Michigan, 1150 Medical Center Dr, Ann Arbor, MI, 48109, USA.
Nada AbdulazizDivision of Rheumatology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, 48109, USA.
Guan XuDepartment of Biomedical Engineering, University of Michigan, 1150 Medical Center Dr, Ann Arbor, MI, 48109, USA.
Wei ZhaoSurvey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, 48104, USA.
David ChamberlandDivision of Rheumatology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, 48109, USA.
Mark DraelosDepartment of Robotics, University of Michigan, 2505 Hayward St, Ann Arbor, MI, 48109, USA.
Girish GandikotaDepartment of Radiology, University of Michigan, Ann Arbor, MI, 48109, USA.
David MillsGE HealthCare Technology & Innovation Center, Niskayuna, NY, 12309, USA.
Xueding WangDepartment of Biomedical Engineering, University of Michigan, 1150 Medical Center Dr, Ann Arbor, MI, 48109, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aiming at an intelligent point-of-care imaging technology for rheumatology clinics, a fully automatic 3D photoacoustic (PA) and ultrasound (US) dual-modality system driven by a robot and powered by deep learning (DL)-based image processing was developed. Automated scanning of volumetric images from patient joints, plus DL-based tissue segmentation and quantification of imaging biomarkers, ensures that the measurements from this system are objective and reproducible. Clinical validation was conducted via a longitudinal study on 43 finger joints from patients affected by inflammatory arthritis. Using manual segmentations as the gold standard, our DL algorithm utilizing the 3D Deep Attentive Feature (DAF3D) model showed satisfactory performance in automatic segmentation of joint space and synovial region and achieved a Dice score of 0.77±0.03 and an IoU of 0.64±0.03. Based on the tissue segmentations facilitated by the DAF3D model, six volumetric imaging biomarkers reflecting the activity of arthritis and its change in response to the treatment were quantified, including hyperemia, blood oxygenation, US power Doppler, joint space echogenicity, joint space volume, and synovial volume. The imaging biomarkers quantified from DL-based segmentation and manual segmentation showed moderate to strong correlations (

Indexed as

Clinical studyDeep learningMedical roboticsPhotoacousticsUltrasound

Identifiers

PMID42389548
PMCPMC13319379

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Registered trials

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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.