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• W4282927995 abstract "Despite numerous clinical trials and pre-clinical developments, the diagnosis of cracked tooth, especially in the early stages, remains a challenge. Cracked tooth syndrome is often accompanied by dramatic painful responses from occlusion and temperature stimulation, which has become one of the leading causes for tooth loss in adults. Current clinical diagnostical approaches for cracked tooth have been widely investigated based on X-rays, optical light, ultrasound wave, etc. Advances in artificial intelligence (AI) development have unlocked the possibility of detecting the crack in a more intellectual and automotive way. This may lead to the possibility of further enhancement of the diagnostic accuracy for cracked tooth disease. In this review, various medical imaging technologies for diagnosing cracked tooth are overviewed. In particular, the imaging modality, effect and the advantages of each diagnostic technique are discussed. What's more, AI-based crack detection and classification methods, especially the convolutional neural network (CNN)-based algorithms, including image classification (AlexNet), object detection (YOLO, Faster-RCNN), semantic segmentation (U-Net, Segnet) are comprehensively reviewed. Finally, the future perspectives and challenges in the diagnosis of the cracked tooth are lighted." @default.
• W4282927995 created "2022-06-16" @default.
• W4282927995 creator A5018811069 @default.
• W4282927995 creator A5021298004 @default.
• W4282927995 creator A5028389688 @default.
• W4282927995 creator A5051893588 @default.
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• W4282927995 creator A5076069924 @default.
• W4282927995 creator A5079831591 @default.
• W4282927995 creator A5091413959 @default.
• W4282927995 date "2022-06-15" @default.
• W4282927995 modified "2023-10-16" @default.
• W4282927995 title "A perspective on the diagnosis of cracked tooth: imaging modalities evolve to AI-based analysis" @default.
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