Abstract:
Due to the abundance of imagery and non-imagery-based clinical data, dental AI is promising. Dental radiograph analysis by a specialist can aid clinical diagnosis and treatment. In recent years, Convolutional Neural Networks have achieved the highest accuracy in various benchmarks, including analyzing dental X-ray images to improve clinical care. This paper introduces the Tufts Dental Database, a panoramic radiography image dataset. This dataset includes 1000 panoramic dental radiography images with expert abnormality and tooth labeling. Radiographs were classified by anatomical location, peripheral characteristics, radiodensity, effects on the surrounding structure, and abnormality category. This groundbreaking multimodal dataset includes the radiologist’s eye-tracking and think-aloud protocol. This work contributes a publicly available dataset that can help researchers incorporate human expertise into AI and achieve more robust and accurate abnormality detection; a benchmark performance analysis for various state-of-the-art systems for dental radiograph image enhancement and image segmentation using deep learning; and an in-depth review of various panoramic dental image datasets and segmentation and detection systems. This dataset will accelerate AI-powered automated abnormality detection and classification in dental panoramic radiographs, tooth segmentation algorithms, and radiologist expertise distillation.
Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.
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