**1. Introduction**

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]. Medical practice in general, and dentistry in particular, generates massive data from sources such as high-resolution medical imaging, biosensors with continuous output and electronic medical records [4]. The use of computer programs can help dental professionals in making decisions related to prevention, diagnosis or treatment planning, among others [5].

At present, one of the artificial intelligence methods employed in clinical fields is called deep learning [6]. Artificial intelligence is the term used to describe the algorithms designed for problem solving and reasoning [7]. 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 been developed extremely quickly during the last decade [9], mainly as a choice for analyzing medical images. CNNs have been successfully employed in medicine, primarily in cancer, for the automated assessment of breast cancer in mammograms, skin cancer in clinical skin screenings, or diabetic retinopathy in eye examinations [10].

CNNs have been recently applied in dentistry to detect periodontal bone loss [11,12], caries on bitewing radiographs [13], apical lesions [14], or for medical image classification [12]. These kinds of neural networks can be used to detect structures, such as teeth or caries, to classify them and to segment them [15]. Neural networks need to be trained and optimized, and for that an image database is necessary.

There are several image techniques in the dentistry field depending on their use. Periapical images are employed to capture intact teeth, including front and posterior, as well as their surrounding bone; therefore, periapical images are very helpful to visualize the potential caries, periodontal bone loss and periapical diseases [16]. Bitewing images can only visualize the crowns of posterior teeth with simple layouts and considerably less overlaps [17]. Panoramic radiographies are very common in dentistry, because they allow for the screening of a broad anatomical region and at the same time, require a relatively low radiation dose [18].

The objective of this review of the literature was to visualize the state of the art of artificial intelligence in various dental applications, such as the detection of teeth, caries, filled teeth, or endodontic treatment, among others.
