Publications and References
Primary publication
"Automatic SCOring of Atopic Dermatitis using deep learning: A pilot study" Medela, A., Mac Carthy, T., Aguilar Robles, S. A., Chiesa-Estomba, C. M., Grimalt, R. JID Innovations, Volume 2, Issue 3, 100107 (2022) DOI: 10.1016/j.xjidi.2022.100107 | PMID: 35990535
This pilot study demonstrated the accuracy and reliability of automated SCORAD scoring for atopic dermatitis using deep learning algorithms, establishing the foundational evidence for the ASCORAD system.
Image quality validation
"Dermatology Image Quality Assessment (DIQA): Artificial intelligence to ensure the clinical utility of images for remote consultations and clinical trials" Hernández Montilla, I., Mac Carthy, T., Aguilar, A., Medela, A. Journal of the American Academy of Dermatology, Volume 88, Issue 4, pp. 927–928 (2023) DOI: 10.1016/j.jaad.2022.11.002 | PMID: 36526082
DIQA is the image quality assessment algorithm that acts as a quality gate in the clinical trial workflow, ensuring consistent image quality across investigator sites.
Related validation studies
The same AI architecture and methodology used for AD scoring has been validated across multiple conditions:
| Study | Condition | Endpoint | Status |
|---|---|---|---|
| ALADIN (Sabater et al., 2026) | Acne | IGA, lesion count | Published |
| APASI | Psoriasis | PASI components | Published |
| Automated SALT | Alopecia | SALT scoring | Deployed in Phase 3 |
| AIHS4 (2023) | Hidradenitis suppurativa | IHS4 scoring | Published |
| aEASI_HVN | Atopic dermatitis | EASI scoring | Ongoing |
Key references from the literature
SCORAD methodology
- Severity scoring of atopic dermatitis: the SCORAD index. Dermatology, 186(1), 23–31 (1993). — The original SCORAD development paper.
- Oranje, A. P. et al. Practical issues on interpretation of scoring atopic dermatitis: the SCORAD index, objective SCORAD and the three-item severity score. British Journal of Dermatology, 157(4), 645–648 (2007).
EASI methodology
- Hanifin, J. M. et al. The eczema area and severity index (EASI): assessment of reliability in atopic dermatitis. Experimental Dermatology, 10(1), 11–18 (2001).
AI in dermatology clinical trials
- The body of literature on AI-powered severity scoring in dermatology clinical trials is growing. Cross-condition validation across acne, psoriasis, alopecia, AD, and HS demonstrates the generalisability of the deep learning approach to clinical severity assessment.