Exploring AI as a Diagnostic Tool in Medical Imaging for Dermatopathological Diseases.

BACKGROUND

Oral health is critical to overall well-being, with abnormalities leading to various health issues. Pemphigus is an autoimmune disease characterized by painful blisters on the skin and mucous membranes, necessitating accurate diagnosis and timely treatment.

OBJECTIVE

This study aims to develop an artificial intelligence (AI) tool utilizing deep learning techniques for the detection of pemphigus from clinical and histological images, enhancing diagnostic accuracy.

METHODS

A supervised learning approach was employed, utilizing a pre-trained MobileNet deep learning model to classify histology images into healthy and pemphigus categories. The dataset was prepared through cropping, resizing, and labeling of images.

RESULTS

The MobileNet model achieved an accuracy of over 92% in distinguishing between normal mucosa and pemphigus lesions. The proposed AI tool can serve as an initial diagnostic check, facilitating faster treatment planning.

CONCLUSION

The implementation of an AI-based diagnostic tool for pemphigus detection demonstrates significant potential in reducing the diagnostic burden in clinical settings. Future work will focus on real-time disease detection applications.