However deep learning-based models require a large number of training samples in order to effectively train the model parameters. The proficiency of deep learning for object detection and classification is well documented. A system that automates the classification of a dental implant based on an X-ray image of a patient’s jaw may therefore be of great assistance to dental practitioners. Dentists may incur significant costs in scenarios where the wrong abutment or artificial tooth is ordered. The dentist can subsequently order a suitable abutment and artificial tooth to replace the existing ones. Based on this information, the connection type of the implant can be deduced. Dentists often consider an X-ray image of the implant in question in order to discern the make, model, and dimensions of the implant. In clinical practice where the dental records of a patient are not readily available, reliable categorisation of a dental implant previously inserted into the aforementioned patient’s jaw is often challenging. Within the context of implant dentistry, implants provide promising prosthetic restoration alternatives for patients. The well-documented success of deep learning in medical imaging has the potential for meeting dental implant recognition needs.ĭental implant recognition is crucial to multiple dental specialties, such as forensic identification and dental reconstruction of broken connections. A number of deep learning-based algorithms have also been investigated in various medical image analysis processes involving multiple organs, the brain, pancreas, breast cancer diagnosis and COVID-19 detection and diagnosis. Recent advances in machine learning, especially with regard to deep learning, are assisting to identify, classify, and quantify patterns in medical images, therefore helping to diagnose and treat different diseases.ĭeep learning-based algorithms in biomedical imaging have produced impressive diagnostic and predictive results in radiology and pathology research. Graphical abstractĭue to the powerful ability to learn abstract and complex features, deep learning algorithms have been employed as the underlying architecture to many computer vision applications such as object detection, image segmentation and image classification. Since the proposed systems utilise an ensemble of techniques that has not been employed for the purpose of dental implant classification/recognition on any previous occasion, the above-mentioned results are very encouraging. It is demonstrated that a segmentation/detection accuracy of 94.0% and a classification/recognition accuracy of 71.7% are attainable within the context of the proposed fully automated system. However, as part of the fully automated system, suitable ROIs are automatically detected. Within the context of the semi-automated system, suitable regions of interest (ROIs), which contain the dental implants, are manually specified. Semi-automated and fully automated systems are proposed for segmenting questioned dental implants from the background in actual X-ray images.
A fully convolutional network (FCN) is subsequently trained on the artificially generated X-ray images for the purpose of automatically identifying the connection type associated with a specific dental implant in an actual X-ray image. The proposed algorithm is based on the calculation of two-dimensional (2D) projections (from a number of different angles) of 3D volumetric representations of computer-aided design (CAD) surface models. A novel algorithm for generating artificial training samples from triangulated three-dimensional (3D) surface models within the context of dental implant recognition is proposed.
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