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Updated: May 12, 2026
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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
Published on: July 11, 2025
Exploring AI as a Diagnostic Tool in Medical Imaging for Dermatopathological Diseases
Pravallika Kakada1, Pratibha Ramani1, Venkatesan Rajinikanth2
1From the Department of Oral Pathology and Microbiology, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India.
Indian Journal of Dermatology
|May 11, 2026
Summary
This study developed an AI tool using deep learning to detect pemphigus from histology images, achieving over 92% accuracy. This technology aids in faster diagnosis and treatment planning for oral health conditions.
Area of Science:
- Oral Medicine
- Dermatology
- Artificial Intelligence
- Medical Imaging
Background:
- Oral health is integral to overall well-being.
- Pemphigus, an autoimmune condition, causes painful blisters in the mouth and on the skin.
- Accurate and timely diagnosis of pemphigus is crucial for effective management.
Purpose of the Study:
- To develop an artificial intelligence (AI) tool for detecting pemphigus.
- To enhance diagnostic accuracy using deep learning on clinical and histological images.
- To improve early detection and treatment planning for pemphigus.
Main Methods:
- A supervised learning approach was used.
- A pre-trained MobileNet deep learning model was employed for image classification.
- Histology images were processed, including cropping, resizing, and labeling into healthy and pemphigus categories.
Main Results:
- The AI model achieved over 92% accuracy in differentiating normal mucosa from pemphigus lesions.
- The developed tool can function as an initial diagnostic aid.
- Faster treatment planning is facilitated by the AI's diagnostic capabilities.
Conclusions:
- AI-based diagnostic tools show significant promise for reducing the clinical diagnostic burden.
- This AI tool can assist healthcare professionals in identifying pemphigus.
- Future research will explore real-time disease detection applications.
