Contrast-Enhanced AI Framework for Early Detection and Classification of Jaw Cysts in OPG Images

Accurate detection and classification of jaw cysts in the maxillofacial region remain challenging due to their asymptomatic nature and low contrast in orthopantomogram (OPG) images. This study presents an AI-assisted diagnostic approach that incorporates histogram equalization techniques to enhance cyst visibility in radiographic scans. Shape and intensity-based features were extracted from 10 clinically validated OPG cases using ImageJ. The processed images were segmented and evaluated against expert annotations, achieving a segmentation precision of 91.2% and an F1-score of 0.88. Enhanced contrast significantly improved the delineation of cyst boundaries, facilitating differentiation between radicular and follicular cysts. The proposed method demonstrates the potential for aiding early diagnosis and clinical decision-making by providing measurable, reproducible image features in a computational workflow. This approach could support automated diagnostic tools for improved treatment planning in dental and maxillofacial care. Unlike complex deep learning models, our lightweight framework combines histogram equalization with shape-intensity descriptors, making it practical for low-resource clinical settings.