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Automatic SCOring of Atopic Dermatitis using Deep Learning (ASCORAD)


Alfonso Medela, Taig Mac Carthy, S. Andy Aguilar Robles, Carlos M. Chiesa-Estomba, and Ramon Grimalt.


Atopic dermatitis (AD) is a multifaceted, chronic relapsing inflammatory skin disease that is commonly associated with other atopic manifestations such as allergic conjunctivitis, allergic rhinitis, and asthma. It is the most common skin disease in children, affecting approximately 15‒20% of children and 1‒3% of adults. The onset of the disease is most common by age 5 years, and early diagnosis and treatment are essential to avoid complications of AD and improve quality of life (QOL).

The European Task Force on Atopic Dermatitis developed the SCOring Atopic Dermatitis (SCORAD) index to create a consensus on assessment methods for AD. The system is representative and well-evaluated but shows, as with all other systems, intraobserver and interobserver disagreements. However, it is currently widely used in clinical practice to assess patient evolution and measure the effectiveness of treatments.

Creating a more objective and practical scoring system for AD assessment is key to improving evidence-based dermatology. In this study, we introduce the Automatic SCORAD (ASCORAD), an automatic version of the SCORAD that provides a quick, accurate, and fully automated scoring method.

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ASCORAD shows promise as an automatic scoring system that might enable a more objective and quicker evaluation. Indeed, a deep learning algorithm could simplify the assessment of AD, a very common skin disease that affects 15‒20% of children and 1‒3% of adults worldwide. An AI-automated approach may help to reduce such bias and therefore be a more precise and objective criterion for evaluation in pharmaceutical studies and routine clinical practice.

Our results show that deep learning may be noticed as a fast and objective alternative method for the automatic assessment of AD with great potential, already achieving results comparable with those of human expert assessment, while reducing interobserver variability and being more time-efficient.

ASCORAD could also be used in situations where face-to-face consultations are not possible, providing an automatic assessment of clinical signs and lesion surface. It could also be a potential tool to reduce the time and effort of training clinical assessors for clinical trials and in clinical practice.

ASCORAD in action

In short, we have proved that a convolutional neural network trained with the observer's average results can achieve an RMAE similar to that of one of the experts. Furthermore, our automatic method outputs a value in the range 0‒100 for each visual sign instead of the range 0‒3 as the usual SCORAD, broadening the spectrum of possible outputs and turning the discrete problem into more continuous.

We believe that our algorithm has the potential to reduce costs in dermatology by saving time while improving the documentation process of the evolution of the disease. This could be interesting for the application in pharmaceutical clinical trials, as well as in clinical practice.

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