Atopic Dermatitis Severity Endpoints for Clinical Trials
The AI scoring provided by Legit.Health delivers automated severity assessment for atopic dermatitis (AD) clinical trials, supporting both SCORAD and EASI—two established efficacy measures in AD drug development.
Why automated AD scoring for clinical trials?
Manual SCORAD and EASI require a dermatologist to estimate affected body surface area (BSA), score intensity signs on each affected area, and (for SCORAD) record two subjective symptoms from the patient. Inter-rater variability in BSA estimation and intensity scoring is the primary source of noise in AD endpoints.
The AI automates the objective components of both scoring systems:
- BSA estimation: Pixel-level segmentation of affected skin across all photographed body areas
- Intensity scoring: Six objective signs for SCORAD / four signs for EASI — erythema, oedema/papulation, oozing/crusts, excoriations, lichenification, dryness — each scored 0–3 per the relevant methodology
- DIQA quality gate: Image quality assessed before scoring; substandard images flagged for recapture
For SCORAD, subjective components (pruritus NRS 0–10 and sleep disturbance NRS 0–10) are collected via patient-reported outcome instruments and combined with the AI objective score to produce the total SCORAD.
Endpoint capabilities
The AI provides four distinct endpoints, each configurable per study protocol:
| Endpoint | Definition | AI output |
|---|---|---|
| Objective SCORAD | A / 5 + 7B / 2 (extent + six signs) | 0–83 continuous scale |
| Total SCORAD | Objective + subjective (pruritus + sleep) | 0–103 composite |
| EASI | Eczema Area and Severity Index | 0–72 composite |
| IGA-AD | Investigator Global Assessment for AD | 0–4 ordinal |
Both SCORAD and EASI are accepted primary efficacy measures in AD clinical trials. SCORAD includes a subjective component (pruritus + sleep), while EASI is purely objective. The Legit.Health platform supports both. The aEASI validation study is ongoing.
How the AI works
The scoring pipeline processes each set of body area photographs in two stages:
Stage 1: BSA segmentation
A deep learning segmentation model identifies affected skin at the pixel level in each photograph. The segmentation output determines the percentage of the visible body area affected by eczema.
The following examples use synthetic dermatology imagery for illustration; they are not patient records.
Illustrative input image
Illustrative affected-area mask
Illustrative affected-area overlay
Stage 2: Intensity sign scoring
For each affected area, six clinical signs are scored independently on the 0–3 SCORAD scale:
| Sign | AI approach |
|---|---|
| Erythema | Colour analysis and boundary detection |
| Oedema/papulation | Texture and surface topology analysis |
| Oozing/crusts | Specific visual feature detection |
| Excoriations | Scratch mark pattern recognition |
| Lichenification | Thickened skin texture classification |
| Dryness | Surface appearance analysis |
Illustrative erythema example
Scratch mark pattern recognition
Thickened skin texture
The BSA extent and per-sign scores are combined into the objective SCORAD component (maximum 83). Adding the patient-reported pruritus and sleep-disturbance scores produces the total SCORAD (maximum 103).
Further reading
Scoring Methodology
Deep technical detail on the automated SCORAD and aEASI scoring pipelines—BSA segmentation, intensity sign scoring, SCORAD and EASI formulas, severity thresholds, and comparison with manual assessment.
Imaging Protocol
Image capture protocols for atopic dermatitis clinical trials, covering body area photography, DIQA quality control, patient preparation, and site standardisation.
Clinical Evidence
Performance validation of automated SCORAD scoring for atopic dermatitis, covering the peer-reviewed pilot study, ongoing clinical validation, and the regulatory evidence pathway.
Trial Workflow
End-to-end clinical trial workflow for atopic dermatitis severity endpoints, covering protocol design, patient enrollment, image capture, AI scoring, severity tracking, data export, and EDC integration.
Sample Outputs
Concrete examples of what sponsors, CROs, and investigators receive from atopic dermatitis severity assessments—per-visit SCORAD reports with segmentation masks, sign-level scoring, longitudinal tracking, and structured EDC data exports.
Publications
Peer-reviewed publications, key references from the literature, and the clinical validation portfolio for automated SCORAD scoring in atopic dermatitis.
Limitations
Transparent documentation of what the AI atopic dermatitis severity scoring can and cannot do, including scope boundaries, validation gaps, and how each limitation is managed.