ArticleIndian journal of dermatology
Exploring AI as a Diagnostic Tool in Medical Imaging for Dermatopathological Diseases.
Article in Indian journal of dermatology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
4 authors.
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Abstract
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.
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