{
  "abstract": "Diabetic foot ulcer (DFU) screening benefits from thermography, but infrared cameras are costly and rarely available at home. We present an unpaired cross-modal framework that virtualizes thermal imaging from ordinary smartphone photos. Using only public Kaggle data (RGB DFU photographs and a thermography foot dataset), Stage A performs RGB↔thermal translation with a CycleGAN backbone enhanced by structural-consistency constraints and anatomical priors to preserve toe, metatarsal, arch, and heel geometry. Stage B trains a thermal-domain anomaly detector (EfficientNet-B0 with a feature pyramid) and projects its responses back to RGB via a cross-domain attention back-projection module, yielding interpretable thermal risk maps. Weak labels derived from thermal asymmetry and adaptive thresholding supervise a lightweight segmentation head; uncertainty gating filters noisy pseudo-masks. In experiments (PyTorch 2.1; dual RTX 3090s), the model achieves strong cross-domain fidelity (SSIM = 0.82, LPIPS = 0.14) and clinically usable detection of thermal abnormalities (AUROC = 0.91, AUPRC = 0.88). RGB-domain localization is accurate (IoU = 0.76; Top-1 hit rate = 87%), highlighting interdigital and metatarsophalangeal regions commonly implicated in pressure-related ulceration. Physiological consistency is demonstrated by close agreement between predicted temperature rise and clinical improvement (+ 4.3 °C vs. +4.5 °C dorsal foot temperature) and by a significant association between declining model-predicted risk area and vascular recovery (ABI/TBI; r = − 0.68, p < 0.01). Ablations confirm the role of each component: removing structural loss or anatomical priors degrades geometry and clinical alignment, while removing cross-domain attention causes the largest drop (AUROC = 0.80, IoU = 0.64, correlation = 0.32), underscoring its necessity for semantic alignment and interpretability.",
  "abstract_shown": true,
  "affiliations": [
    "School of Clinical Medicine, Shandong University",
    "Shengli Oilfield Central Hospital"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Ping Sui"
    },
    {
      "affs": [
        1
      ],
      "name": "Ganchun Wu"
    },
    {
      "affs": [
        1
      ],
      "name": "Juan Xu"
    },
    {
      "affs": [
        1
      ],
      "name": "Shouguang Sui"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/39wax1ymj1fs0.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-61426-5",
  "fulltext_url": "https://doi.org/10.1038/s41598-026-61426-5",
  "id": "39wax1ymj1fs0",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://doi.org/10.1038/s41598-026-61426-5",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "Q"
    ],
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      "lcc",
      "R"
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    [
      "openalex",
      "2741|Infrared Thermography in Medicine"
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  "openalex": "W7196927520",
  "pdf_url": "https://www.nature.com/articles/s41598-026-61426-5_reference.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-08-01",
  "publisher": "Nature Portfolio",
  "ris": "https://research.draconitic.com/paper/39wax1ymj1fs0.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:45:57Z",
      "native_id": "74d3a2c0007c4685be7e3f39a964c5db",
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      "url": "https://doi.org/10.1038/s41598-026-61426-5"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2741|Infrared Thermography in Medicine",
      "subfield": "imaging"
    }
  ],
  "title": "Unpaired RGB-to-thermal learning for explainable diabetic-foot risk screening",
  "title_text": "Unpaired RGB-to-thermal learning for explainable diabetic-foot risk screening",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:45:57Z",
  "url": "https://research.draconitic.com/paper/39wax1ymj1fs0",
  "venue": "Scientific Reports",
  "versions": []
}