Machine Learning Model for an App‐Based Tool to Assist With the Diagnosis of Canine Atopic Dermatitis

Abstract

ABSTRACT Background Canine atopic dermatitis (cAD) is a chronic condition requiring life‐long management. Accurate diagnosis can be challenging, with no reliable diagnostic test. Objectives This study aimed to generate a simple diagnostic model for cAD. Materials and Methods Machine learning by Random Forest was applied to metadata from a prospective dataset of clinical cases definitively diagnosed with cAD alone or another dermatosis. The dataset underwent a division of 67% for training and 33% for testing, with the model being trained via stratified K‐fold cross‐validation on the former portion, while performance assessment was conducted on the latter portion. Nine referral clinicians across four European countries contributed 645 cases. Results Modelling confirmed the value of the four tested metadata on a dog's history and reduced the initial 15 lesion locations tested to three. Metadata for the final model were: predisposed breed (any from a list of 31), predominantly indoor life, dermatitis onset age between 6 months and 3 years, dermatitis chronic, recurrent or a permanent background. Lesion locations were axilla, inguinal and other. Diagnostic prediction was 95% sensitive and 84% specific. Conclusions and Clinical Relevance This model is a relevant prototype for an app‐based tool to support general practitioners in the diagnosis of cAD alongside existing tests. It has high sensitivity and specificity based on four questions and three lesion locations obtained from a standard history and clinical examination.