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Publications, References, and Further Reading

This page compiles the key references from the alopecia areata literature and the related publications that support the alopecia severity endpoints provided by Legit.Health.

Primary publication​

ASALT: Automatic Severity of Alopecia Tool​

Legit.Health “ASALT: Automatic Severity of Alopecia Tool. AI-Powered SALT Scoring for Clinical Trials” In preparation. 2026. (In preparation)

  • Development of the ASALT scoring algorithm combining scalp segmentation with SALT-weighted regional aggregation
  • 4-quadrant scalp imaging protocol validated for clinical trial use
  • Deployed in a Phase 3 clinical trial for adverse event monitoring of alopecia
  • Severity classification aligned with standard SALT ranges (None, Limited, Moderate, Severe, Very Severe)

This publication is currently in preparation and will describe the ASALT scoring methodology, validation results, and clinical trial deployment experience.

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. For scalp imaging with four separate perspectives, DIQA is especially important to ensure consistent quality across all quadrant images. 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 deep learning architecture validated across multiple dermatological conditions

The same architectural principles and training methodology underpin the alopecia scoring system.

Additional publications​

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

Key references from the literature​

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

Olsen EA, Hordinsky MK, Price VH, et al. “Severity of Alopecia Tool (SALT): A Validated Clinical Tool for Assessing Alopecia Areata” J Am Acad Dermatol. 2004. doi:10.1016/j.jaad.2003.09.032

Establishes the SALT scoring system as the standard method for quantifying alopecia areata severity in clinical trials. Defines the quadrant-based scalp assessment methodology and severity grading.

U.S. Food and Drug Administration “Alopecia Areata: Developing Drugs for Treatment. Guidance for Industry.” FDA Guidance Document. 2022.

Establishes SALT as an acceptable efficacy measure for alopecia areata clinical trials. Recommends standardised photographic documentation and responder definitions based on SALT thresholds.

Olsen EA, et al. “Guidelines for clinical trials in alopecia areata: A consensus statement by the National Alopecia Areata Foundation” J Am Acad Dermatol. 2018. doi:10.1016/j.jaad.2017.09.028

Provides consensus recommendations for alopecia areata trial design, including endpoints, assessment timing, and photographic documentation standards.

Wyrwich KW, et al. “Measuring the severity of alopecia areata: A review of existing methods and a critical assessment of the SALT score” J Am Acad Dermatol. 2020.

Reviews strengths and limitations of SALT scoring, including inter-rater variability in visual percentage estimation. Supports the case for automated scoring approaches.

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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