Abstract
The application of Artificial Intelligence in dental healthcare has a very promising role due to the abundance of imagery and non-imagery-based clinical data. Expert analysis of dental radiographs can provide crucial information for 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 quality. The Tufts Dental Database, a new X-ray panoramic radiography image dataset, has been presented in this paper. This dataset consists of 1000 panoramic dental radiography images with expert labeling of abnormalities and teeth. The classification of radiography images was performed based on five different levels: anatomical location, peripheral characteristics, radiodensity, effects on the surrounding structure, and the abnormality category. This first-of-its-kind multimodal dataset also includes the radiologist's expertise captured in the form of eye-tracking and think-aloud protocol. The contributions of this work are 1) publicly available dataset that can help researchers to incorporate human expertise into AI and achieve more robust and accurate abnormality detection; 2) a benchmark performance analysis for various state-of-the-art systems for dental radiograph image enhancement and image segmentation using deep learning; 3) an in-depth review of various panoramic dental image datasets, along with segmentation and detection systems. The release of this dataset aims to propel the development of AI-powered automated abnormality detection and classification in dental panoramic radiographs, enhance tooth segmentation algorithms, and the ability to distill the radiologist's expertise into AI.
Highlights
THE need for precise imaging, and diagnostic tools have led to an exponential increase in computed tomography (CT) and X-ray imaging-based technologies to detect, diagnose, and treat various dental diseases in the last few decades
Utilizing classical Machine Learning (ML) techniques such as mathematical morphology [2], active contour [3], and levelset [4] have been used for teeth segmentation
The Tufts Dental Database (TDD) contains 9000 images and 3 JSON files describing each radiograph. These images belong to 4 main subsets: panoramic radiographs, the labeled masks highlighting the abnormalities, the eye-tracking gaze plots, and the labeled teeth mask
Summary
THE need for precise imaging, and diagnostic tools have led to an exponential increase in computed tomography (CT) and X-ray imaging-based technologies to detect, diagnose, and treat various dental diseases in the last few decades. Multiple types of imagery for the same anatomical region of the same individual are taken over multiple time points, and the corresponding non-imagery-based clinical data is present Dental conditions such as caries, apical lesions, and PBL are relatively prevalent, making it easy to build the dataset to train and optimize neural networks. Faster operation time, more diagnostic capabilities, less radiation exposure, better patient acceptance, and fewer infection control procedures [24] make it ideal for dentistry Intraoral radiographs such as periapical and bitewing X-rays can image localized regions in the mouth but require multiple scans to capture different areas.
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