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<p>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.</p>

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Predictive modeling of chronic foot ulcer outcomes using longitudinal photoacoustic imaging

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月27日

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  • Yanda Cheng,
  • Chuqin Huang,
  • Shu-liang Yu,
  • Saptarshi Chakraborty,
  • Yunqi Xi,
  • Robert W. Bing,
  • Huijuan Zhang,
  • Xiaoyu Zhang,
  • Isabel Komornicki,
  • Linda M. Harris,
  • Wenyao Xu,
  • Jun Xia

摘要

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.