Abstract:Background
Early clinical cracked tooth can be a perplexing disorder to diagnose and manage. One of the key problems for the diagnosis of the cracked tooth is the detection of the location of the surface crack.
Methods
This paper proposes an image-based method for the detection of the micro-crack in the simulated cracked tooth. A homemade three-axis motion platform mounted with a telecentric lens was built as an image acquisition system to observe … Show more
“…In addition, automatic segmentations implemented by CNN had been achieved on MRI images of brain tumors, mammograms and radiographs of bone tissue [ 18 , 19 , 95 ]. More recently, Zhang et al [ 96 ] proposed a method based on digital image correlation to identify the crack path and quantitative characterization of crack opening displacement, whose results were in good accordance with the one obtained from micro-CT. Fig. 6 Block diagram to illustrate the framework of the methods for detection of cracked tooth …”
Section: Ai-based Image Analysis For Crack Detectionsupporting
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.
“…In addition, automatic segmentations implemented by CNN had been achieved on MRI images of brain tumors, mammograms and radiographs of bone tissue [ 18 , 19 , 95 ]. More recently, Zhang et al [ 96 ] proposed a method based on digital image correlation to identify the crack path and quantitative characterization of crack opening displacement, whose results were in good accordance with the one obtained from micro-CT. Fig. 6 Block diagram to illustrate the framework of the methods for detection of cracked tooth …”
Section: Ai-based Image Analysis For Crack Detectionsupporting
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.
“…The large temperature difference generated thermal stress, which further resulted in the microcracks in the sample. More details can be found in Reference [ 26 ].…”
Cracked tooth syndrome is a commonly encountered disease in dentistry, which is often accompanied by dramatic painful responses from occlusion and temperature stimulation. Current clinical diagnostic trials include traditional methods (such as occlusion test, probing, cold stimulation, etc.) and X-rays based medical imaging (periapical radiography (PR), cone-beam computed tomography (CBCT), etc.). However, these methods are strongly dependent on the experience of the clinicians, and some inconspicuous cracks are also extremely easy to be overlooked by visual observation, which will definitely affect the subsequent treatments. Inspired by the achievements of applying deep convolutional neural networks (CNNs) in crack detection in engineering, this article proposes an image-based crack detection method using a deep CNN classifier in combination with a sliding window algorithm. A CNN model is designed by modifying the size of the input layer and adding a fully connected layer with 2 units based on the ResNet50, and then, the proposed CNN is trained and validated with a self-prepared cracked tooth dataset including 20,000 images. By comparing validation accuracy under seven different learning rates,
10
−
5
is chosen as the best learning rate for the following testing process. The trained CNN is tested on 100 images with
1920
×
1080
-pixel resolutions, which achieves an average accuracy of 90.39%. The results show that the proposed method can effectively detect cracks in images under various conditions (stained, overexplosion, images affected by other diseases). The proposed method in this article provides doctors with a more intelligent diagnostic solution, and it is not only suitable for optical photographs but also for automated diagnosis of other medical imaging images.
“…The same preparation protocol for vitro simulated cracked teeth and its details can be found in Refs. [ 24 , 48 , 49 ]. It should be mentioned that the preparation of cracked teeth samples requires a significant effort because of costs and resources, with specific requirements for tooth type and integrity.…”
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