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

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  • Additional Information
    • Source:
      Publisher: Blackwell Country of Publication: England NLM ID: 9504453 Publication Model: Print Cited Medium: Internet ISSN: 1600-0846 (Electronic) Linking ISSN: 0909752X NLM ISO Abbreviation: Skin Res Technol Subsets: MEDLINE
    • Publication Information:
      Publication: <1998-> : Oxford : Blackwell
      Original Publication: Copenhagen, Denmark ; Cambridge, MA : Munksgaard, c1995-
    • Subject Terms:
      Smartphone* ; Artificial Intelligence* ; Mobile Applications* ; Skin*/diagnostic imaging ; Skin*/pathology; Humans ; Pilot Projects ; Female ; Adult ; Male ; Algorithms ; Healthy Volunteers ; Young Adult ; Feasibility Studies ; Skin Diseases/diagnosis ; Skin Diseases/diagnostic imaging ; Skin Diseases/pathology ; Middle Aged ; Sensitivity and Specificity
    • Abstract:
      Background: The development of artificial intelligence (AI) is rapidly expanding, showing promise in the dermatological field. Skin checks are a resource-heavy challenge that could potentially benefit from AI-tool assistance, particularly if provided in widely available AI solutions. A novel smartphone application(app)-based AI system, "SCAI," was developed and trained to recognize spots in paired images of skin, pursuing identification of new skin lesions. This pilot study aimed to investigate the feasibility of the SCAI-app to identify simulated skin changes in vivo.
      Materials and Methods: The study was conducted in a controlled setting with healthy volunteers and standardized, simulated skin changes (test spots), consisting of customized 3-mm adhesive spots in three colors (black, brown, and red). Each volunteer had a total of eight test spots adhered to four areas on back and legs. The SCAI-app collected smartphone- and template-guided standardized images before and after test spot application, using its backend AI algorithms to identify changes between the paired images.
      Results: Twenty-four volunteers were included, amounting to a total of 192 test spots. Overall, the detection algorithms identified test spots with a sensitivity of 92.0% (CI: 88.1-95.9) and a specificity of 95.5% (CI: 95.0-96.0). The SCAI-app's positive predictive value was 38.0% (CI: 31.0-44.9), while the negative predictive value was 99.7% (CI: 99.0-100).
      Conclusion: This pilot study showed that SCAI-app could detect simulated skin changes in a controlled in vivo setting. The app's feasibility in a clinical setting with real-life skin lesions remains to be investigated, where the challenge with false positives in particular needs to be addressed.
      (© 2024 The Author(s). Skin Research and Technology published by John Wiley & Sons Ltd.)
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    • Contributed Indexing:
      Keywords: AI; artificial intelligence; detection; feasibility study; pilot study; skin change; smartphone
    • Publication Date:
      Date Created: 20241004 Date Completed: 20241004 Latest Revision: 20241007
    • Publication Date:
      20241007
    • Accession Number:
      PMC11452258
    • Accession Number:
      10.1111/srt.70056
    • Accession Number:
      39366915