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February 22, 2026Open Access

Automated triage of cancer-suspicious skin lesions with 3D total-body photography

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Authors

NKNicholas R KurtanskyMGMaura C. GillisOOOzan Oktay

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Overview

Automated triage improves skin cancer detection in patients, suggesting enhanced diagnostic efficiency.

Key Points

  • The aim is to improve early detection of skin cancer by utilizing automated triage methods for skin lesions.
  • Utilized over 900,000 lesion crops from 3D total body photography.
  • Conducted an online grand challenge for machine learning applications in skin cancer detection.
  • Compared models incorporating intra-patient context against traditional approaches.
  • Demonstrated superior performance of the model using intra-patient context.
  • Showed clinical plausibility for automatic triage of atypical skin lesions.
  • Highlighted computational advantages over previously published methods.

Cite This Study

Kurtansky et al. (2025) studied this question.

synapsesocial.com/papers/699a9e00482488d673cd4543https://doi.org/10.5167/uzh-292093
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Improved Skin Cancer Detection with 3D Total Body Photography: Integrating AI Algorithms for Precise Diagnosis2024 · 3 citations
  2. 2A protocol for annotation of total body photography for machine learning to analyze skin phenotype and lesion classification2024 · 7 citations
  3. 3The SLICE-3D dataset: 400,000 skin lesion image crops extracted from 3D TBP for skin cancer detection2024 · 13 citations
  4. 43D total body photography, a promising innovation for early skin cancer detection: scoping review.2025
  5. 5Performance of an automated total body mapping algorithm to detect melanocytic lesions of clinical relevance2024 · 5 citations