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Artificial Intelligence Smartphone Application for Detection of Simulated Skin Changes: An In Vivo Pilot Study.

Gabriela Lladó Grove1, Gorm Reedtz2,3, Brian Vangsgaard4

  • 1Department of Dermatology, Copenhagen University Hospital - Bispebjerg, Copenhagen, Denmark.

Skin Research and Technology : Official Journal of International Society for Bioengineering and the Skin (ISBS) [And] International Society for Digital Imaging of Skin (ISDIS) [And] International Society for Skin Imaging (ISSI)
|October 4, 2024
PubMed
Summary

A new smartphone app using artificial intelligence (AI) shows promise for identifying simulated skin changes. This AI tool demonstrated high sensitivity and specificity in a controlled study, suggesting potential for future dermatological applications.

Keywords:
AIartificial intelligencedetectionfeasibility studypilot studyskin changesmartphone

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Area of Science:

  • Dermatology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Artificial intelligence (AI) is rapidly advancing, with significant potential in dermatology for tasks like skin lesion identification.
  • Current skin checks are resource-intensive, creating a need for accessible AI-driven solutions.
  • A novel smartphone application, SCAI, was developed to recognize skin spots and identify new lesions.

Purpose of the Study:

  • To investigate the feasibility of the SCAI smartphone application in detecting simulated skin changes.
  • To evaluate the in vivo performance of an AI system designed for dermatological image analysis.

Main Methods:

  • A controlled pilot study involved 24 healthy volunteers with standardized, simulated 3-mm skin lesions (test spots) in black, brown, and red.
  • The SCAI application guided standardized image capture of test spots before and after application on volunteers' skin.
  • Backend AI algorithms analyzed paired images to detect changes introduced by the test spots.

Main Results:

  • The SCAI app's detection algorithms achieved a sensitivity of 92.0% and a specificity of 95.5% across 192 test spots.
  • The positive predictive value was 38.0%, indicating a need to address false positives.
  • A high negative predictive value of 99.7% suggests the app is reliable in ruling out changes.

Conclusions:

  • The SCAI smartphone application demonstrated feasibility in detecting simulated skin changes in a controlled in vivo environment.
  • Further investigation is required to assess the app's performance with real-life skin lesions in clinical settings.
  • Addressing the challenge of false positives is crucial for the clinical utility of this AI-based dermatological tool.