Advanced Application of Artificial-Neural-Networks (ANNs) fo
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Drug resistance to biologics in psoriasis therapy can occur – it may be acquired during a treatment or else present from the beginning. To date, no biomarkers are known that may reliably guide clinicians in predicting responsiveness to biologics. Biologics may pose a substantial economic burden. Secukinumab efficiently targets IL-17 in the treatment of psoriasis.

A Study was conducted to assess the “fast responder” patient profile, predicting it from the preliminary complete blood count (CBC) and clinical examination. From November 2016 to May 2017, a multicenter prospective open label pilot study was performed in three Italian reference centers enrolling bio-naive plaque psoriasis patients, undergoing the initiation phase secukinumab treatment (300mg subcutaneous at week 0,1,2,3,4).

Researchers define fast responders as patients having achieved at least PASI 75 at the end of secukinumab induction phase. Clinical and CBC data at week 0 and at week 4 were analyzed with linear statistics, principal component analysis, and artificial neural networks (ANNs), also known as deep learning.

Two different ANNs were employed:
*Auto Contractive Map (Auto-CM), an unsupervised ANNs, to study how this variables cluster and
*Supervised ANNs, Training with Input Selection and Testing (TWIST), to build the predictive model.

23 plaque psoriasis patients were enrolled:
-19 patients were responders and 4 were non-responders.
30 attributes were examined by Auto-CM, creating a semantic map for three main profiles: responders, non-responders and an intermediate profile.
-The algorithm yielded 5 of the 30 attributes to describe the 3 profiles. This allowed to set up the predictive model. It displayed after training testing protocol an overall accuracy of 91.88%.

Conclusively, the present study is possibly the first approach employing ANNs to predict drug efficacy in dermatology; a wider use of ANNs may be conducive to useful both theoretical and clinical insight.