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Publications, References, and Further Reading

This page compiles the peer-reviewed publications, conference presentations, and key references from the dermatology literature that support the acne severity endpoints provided by Legit.Health.

Primary publication​

ALADIN: Acne Lesion And Density INdex​

Sabater A, et al. “ALADIN: Acne Lesion And Density INdex. A Novel Tool for Automatic Acne Severity Assessment” Skin Health and Disease (British Association of Dermatologists). 2026. (Provisionally accepted)

  • Development of the IGA = N^a · (D + b) formula combining lesion count and spatial density into an IGA-aligned severity score
  • Calibration of formula constants against the consensus of three board-certified dermatologists
  • Validation of the CNN-based inflammatory lesion detection model (papules, pustules, nodules)
  • Evidence that incorporating spatial density alongside lesion count improves alignment with clinical severity perception

This is the foundational publication for the acne scoring methodology.

Image quality assessment​

DIQA: Dermatology Image Quality Assessment​

Hernández Montilla I, Mac Carthy T, Aguilar A, Medela A “Dermatology Image Quality Assessment (DIQA): Artificial intelligence to ensure the clinical utility of images for remote consultations and clinical trials” Journal of the American Academy of Dermatology. 2023. doi:10.1016/j.jaad.2022.11.002

J Am Acad Dermatol. 2023;88(4):927-928

  • Pearson correlation ≥0.70 with expert image quality assessment
  • Real-time evaluation of focus, lighting, framing, and resolution
  • Applicability to both clinical practice and clinical trial settings

DIQA is the real-time image quality assessment algorithm that serves as a quality gate in the clinical trial workflow. Published in the Journal of the American Academy of Dermatology.

The Legit.Health AI architecture (deep learning models trained on expert-annotated datasets producing severity scores aligned with established clinical scales) has been validated across multiple dermatological conditions:

AIHS4: Automatic IHS4 for Hidradenitis Suppurativa​

Legit.Health “Automatic International Hidradenitis Suppurativa Severity Score System (AIHS4): A Novel Tool to Assess the Severity of Hidradenitis Suppurativa Using Artificial Intelligence” Published. 2025.

  • Inter-observer ICC ≥ 0.727 (95% CI: 0.66–0.79) for objective severity assessment
  • State-of-the-art comparison: ICC of 0.47 without the device vs. 0.727 with the device
  • Same object detection + scoring methodology as acne

The same architectural principles underpin the acne scoring system.

Additional publications​

Validation of the AI methodology has also been completed for APASI (psoriasis), ASCORAD (atopic dermatitis), and AUAS (urticaria). For full details, see the clinical validation section.

Conference presentations​

AEDV (Spanish Academy of Dermatology and Venereology) 2025, Paris

Poster: ALADIN scoring methodology for acne severity assessment

Preliminary results of the acne lesion detection model, spatial density computation, and early correlation data with expert IGA ratings.

Key references from the literature​

The following publications from the dermatology and clinical trial literature provide context for the scoring methodology and endpoint positioning:

Hayashi N, et al. “Establishment of grading criteria for acne severity” J Dermatol. 2008. doi:10.1111/j.1346-8138.2008.00462.x

Establishes the Hayashi Criterion grading system based on the number of inflammatory lesions per half-face. Forms the basis for the standard 2-perspective imaging protocol.

U.S. Food and Drug Administration “Acne Vulgaris: Establishing Effectiveness of Drugs Intended for Treatment. Guidance for Industry.” FDA Guidance Document. 2005.

Establishes the standard for acne clinical trial endpoints in the United States. Recommends both lesion counts and IGA as co-primary endpoints.

Tan JKL, et al. “A Comprehensive Review of the Acne Grading Scale in 2023” Ann Dermatol. 2024. PMC10995619

Documents the variability across existing acne grading scales and highlights that 'numerous IGA scales exist with variations in specific categorical definitions'.

Systematic review “Artificial Intelligence in the Assessment and Grading of Acne Vulgaris: A Systematic Review” J Pers Med. 2025. PMC12194645

Reviews AI applications in acne diagnosis and severity grading (2017–2025). Highlights the potential of AI-driven methods to improve objectivity, reproducibility, and clinical efficiency.

Clinical validation portfolio​

Legit.Health maintains an active clinical validation program across all supported conditions. The complete evidence portfolio is available in the clinical validation section, which includes:

  • Published peer-reviewed papers with embedded PDFs
  • Performance claims with acceptance criteria and achieved values
  • Clinical validation study summaries with study codes
  • Links to regulatory documentation

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