Abstract
Neural networks are increasingly being used in the field of dentistry. The aim of this literature review was to visualize the state of the art of artificial intelligence in dental applications, such as the detection of teeth, caries, filled teeth, crown, prosthesis, dental implants and endodontic treatment. A search was conducted in PubMed, the Institute of Electrical and Electronics Engineers (IEEE) Xplore and arXiv.org. Data extraction was performed independently by two reviewers. Eighteen studies were included. The variable teeth was the most analyzed (n = 9), followed by caries (n = 7). No studies detecting dental implants and filled teeth were found. Only two studies investigated endodontic applications. Panoramic radiographies were the most common image employed (n = 5), followed by periapical images (n = 3). Near-infrared light transillumination images were employed in two studies and bitewing and computed tomography (CT) were employed in one study. The included articles used a wide variety of neuronal networks to detect the described variables. In addition, the database used also had a great heterogeneity in the number of images. A standardized methodology should be used in order to increase the compatibility and robustness between studies because of the heterogeneity in the image database, type, neural architecture and results.
Highlights
Medical imaging techniques, such as computed tomography (CT) or X-ray among others, have been used in recent decades for the detection, diagnosis and treatment of different diseases [1].A new and emerging field in dentistry is dental informatics, because of the possibility it offers to improve treatment and diagnosis [2], in addition to saving time and reducing stress and fatigue during daily practice [3]
Convolutional neural networks (CNNs) are commonly used in applications relying on deep learning, which have
All of the electronic search strategies resulted in 387 potential manuscripts
Summary
A new and emerging field in dentistry is dental informatics, because of the possibility it offers to improve treatment and diagnosis [2], in addition to saving time and reducing stress and fatigue during daily practice [3]. One of the artificial intelligence methods employed in clinical fields is called deep learning [6]. The success of deep learning is mainly due to the progress in the computer capacity, the huge amount of data available and the development of algorithms [1]. This method has been proven and is used effectively in image-based diagnosis in several fields [8]. Convolutional neural networks (CNNs) are commonly used in applications relying on deep learning, which have
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