Evidence map›Paper›PMID 41760883›Full record

ArticleNpj imaging2026

Predictive modeling of chronic foot ulcer outcomes using longitudinal photoacoustic imaging.

Yanda Cheng, Chuqin Huang, Shu-Liang Yu, Saptarshi Chakraborty, Yunqi Xi, Robert W Bing, Huijuan Zhang, Xiaoyu Zhang, Isabel Komornicki, Linda M Harris and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Npj imaging, 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. 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

12 authors.

Yanda ChengDepartment of Biomedical Engineering, University at Buffalo, State University of New York, Buffalo, NY, USA.
Chuqin HuangDepartment of Biomedical Engineering, University at Buffalo, State University of New York, Buffalo, NY, USA.
Shu-Liang YuDepartment of Biostatistics, University at Buffalo, State University of New York, Buffalo, NY, USA.
Saptarshi ChakrabortyDepartment of Biostatistics, University at Buffalo, State University of New York, Buffalo, NY, USA.
Yunqi XiDepartment of Biomedical Engineering, University at Buffalo, State University of New York, Buffalo, NY, USA.
Robert W BingDepartment of Biomedical Engineering, University at Buffalo, State University of New York, Buffalo, NY, USA.
Huijuan ZhangDepartment of Biomedical Engineering, University at Buffalo, State University of New York, Buffalo, NY, USA.
Xiaoyu ZhangDepartment of Computer Science and Engineering, University at Buffalo, State University of New York, Buffalo, NY, USA.
Isabel KomornickiDepartment of Surgery, University at Buffalo, State University of New York, Buffalo, NY, USA.
Linda M HarrisDepartment of Surgery, University at Buffalo, State University of New York, Buffalo, NY, USA.
Wenyao XuDepartment of Computer Science and Engineering, University at Buffalo, State University of New York, Buffalo, NY, USA.
Jun XiaDepartment of Biomedical Engineering, University at Buffalo, State University of New York, Buffalo, NY, USA. junxia@buffalo.edu.

Funding

mHealth Technologies for Assessing Blood Perfusion in Chronic WoundsR01EB035188 · STATE UNIVERSITY OF NEW YORK AT BUFFALO · 2025 to 2025
$674k
NIBIB NIH HHS R01EB028978NIBIB NIH HHS R01EB035188
6 · The paper itself

Abstract

This study reports the first clinical longitudinal photoacoustic imaging (PAI) of chronic foot ulcers, a major complication in patients with peripheral vascular disorders. Compared to traditional methods such as ABI or near-infrared spectroscopy, the photoacoustic imaging approach provides non-invasive, high-resolution, and quantitative monitoring of vascular dynamics over time. Our system provided dorsal-side imaging of vascular structures with an expanded field of view and incorporated a skin artifact suppression algorithm to improve visualization of subdermal vasculature. From the acquired 2D and 3D images, we extracted a set of 45 quantitative features, representing signal intensity, texture complexity, and morphological changes associated with ulcer progression. Using a LASSO-based feature selection strategy, we identified the top-12 feature subset and validated them through multi-seed cross-validation. Our selection achieved an average classification accuracy of 79.6% and a macro-averaged AUC of 86.6% in distinguishing healing, worsening, and healthy cases. These findings demonstrate the clinical utility of photoacoustic biomarkers for personalized ulcer tracking and risk stratification.

Identifiers

PMID41760883
PMCPMC12948959

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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 Socratic graph.