Integrating Artificial Intelligence and Neural Networks for Enhanced Diagnosis of Atopic Dermatitis
Year: 2025
Type: article
Abstract: Introduction: Atopic dermatitis, a chronic inflammatory skin condition, disproportionately affects young children. Statistics show that up to 90% of cases are diagnosed before the age of five. Despite its prevalence, the exact cause of this condition remains a mystery, frustrating both patients and healthcare professionals. This article delves into the exciting potential of Artificial Intelligence (AI) methods in diagnosing atopic dermatitis in this particularly vulnerable age group. We’ll explore how AI can be used to improve diagnostic accuracy and efficiency, potentially leading to earlier intervention and better management of the condition. Methods: Atopic dermatitis, particularly prevalent among children under the age of five, remains a condition without a clearly defined etiology. This study investigates the application of artificial intelligence-specifically an Artificial Neural Network (ANN) for the diagnosis of atopic dermatitis in early childhood. The model incorporates input parameters including age, gender, presence of asthma, skin pH levels, dermoscopic findings and Immunoglobulin E (IgE) levels to enhance diagnostic accuracy. Results: The research focuses on developing and evaluating an artificial neural network designed for the automated detection of Atopic Dermatitis (AD). Utilizing age, gender, asthma status, skin pH, dermoscopy and IgE levels, this approach has the potential to improve AD diagnosis accuracy, paving the way for similar models to further enhance future diagnostics. Conclusion: This paper demonstrates a successful Artificial Neural Network for distinguishing atopic dermatitis from healthy skin, potentially impacting millions with this common condition.
Author: Mahajabeen Madarkar
Language: English
Subfield: Dermatology
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4409817041