**4. Discussion**

The goal of this literature review was to visualize the state of the art of artificial intelligence in detecting different dental situations, such as the detection of teeth, caries, filled teeth, endodontic treatments and dental implants.

Neural networks can have single or multiple layers, with nodes or neurons interconnected that allows signals to travel through the network. ANNs are typically divided into three layers of neurons, namely: input (receives the information), hidden (extracts patterns and performs the internal processing), and output (presents the final network output) [32,33]. Training is the process to optimize parameters [34]. Figure 2 details the architecture for teeth detection.

More and more industries are using artificial intelligence to make increasingly complex decisions, and many alternatives are available to them [32]. However, in view of our results, there is a paucity of guidance on selecting the appropriate methods tailored to the health-care industry.

**Figure 2.** System architecture for teeth detection.

The benefit of neural networks in medicine and dentistry is related to their ability to process large amounts of data for analyzing, diagnosing and disease monitoring. Deep learning has become a great ally in the field of medicine in general and is beginning to be one in dentistry. According to the year of publication of the studies included in this review, 2019 was the year in which the most articles were published.

The results provided by artificial intelligence have a great dependence on the data with which they learn and are trained, that is, on the input data and the image employed to detect each variable. All of the studies included in this review employed radiographs, mainly panoramic radiographies. In this sense, it would be interesting to apply neural networks and artificial intelligence in other types of radiological studies such as cone beam computed tomography (CBCT) or cephalometry, which allow clinicians to make a complete anatomical examination. Lee et al. evaluated the detection and diagnosis of different lesions employing CBCT and a deep convolutional neural network [35]. Before being possible to detect the variables analyzed in this review, teeth must be detected. Panoramic radiography is the most common technique in general dentistry, which captures the entire mouth in a single 2D image [36,37], and it is common to use artificial intelligence to detect the presence or absence of a tooth. The main advantages of these types of images are: the patient comfort compared with other techniques, such as intraoral images (bitewing and periapical); the low radiation exposure; and the ability to evaluate a larger area of the maxilla and mandible [37].

Panoramic radiographies are useful to evaluate endodontic treatments, periapical lesions and disorders in bones, among others [38]. This type of image has obtained the best results in tooth detection if we compare it with the study that used periapical images to detect this variable. In addition, the results obtained by the studies that detected teeth were superior to the rest of the variables analyzed, regardless of the network or type of image used.

Caries is one of the most common chronic diseases in the oral field, with a great impact on a patient's health [39]. Clinical examination is the main method for caries detection, with radiographic examination being a complementary diagnostic tool [40]. According to experience and scientific literature, intraoral bitewing images are the most effective in detecting caries lesions [41]. However, only one study included in this review employed bitewings to detect caries. Two studies used near-infrared transillumination images and one employed periapical images. The best results were obtained in the study where periapical images were used to detect caries.

A variety of CNN architectures were found in the studies included in this literature review. Convolutional networks are designed to process data that come in the form of multiple arrays and that are structured in a series of stages [42]. In recent decades, CNNs have been applied with success for the detection, segmentation and recognition of objects in images. In this review, convolutional networks applied to the detection of dental variables were used.

Faster regions with convolutional neural network features (Faster R-CNN) are composed of two modules. The first module is a deep fully convolutional network that suggests regions and the second module is the Fast R-CNN detector [43]. ResNets are residual networks, which is a CNN designed to allow for thousands of convolutional layers. Deep Neural Network for Object Detection (DetectNet) outputs the XY coordinates of a detected object. This kind of neural network has been applied in

different medical fields [19,44]. Keras is a library of open source neural networks written in Python. PyBrain is a machine-learning library for Python, whose objective is to provide flexible, easy-to-use and powerful algorithms for machine-learning tasks [45]. Mask R-CNN is an extension of Faster R-CNN, by adding a branch for predicting segmentation masks on each region of interest (ROI) [46]. AlexNet was introduced in 2012 and employs an eight-layer convolutional neural network as follows: five convolutional layers, two fully connected hidden layers, and one fully connected output layer [47].

In addition to the wide variety of neural network architectures, the studies included in this work also presented a great variety in terms of the number of images used. The manuscripts included in this review published in 2017 and 2018 are those that show a larger database compared with the articles published in 2019 and 2020. However, there is no relationship between the database used and the results obtained, nor between the database and the variables detected.

The possible and future clinical applications of artificial intelligence and neural networks is the prediction of a phenomenon. Probabilistic neural networks can be used in dentistry to predict fractures, as Johari et al. indicated, where a probabilistic neural network was designed to diagnose a fracture in endodontically treated teeth [48].

In view of the results shown in this review and the included studies, the authors suggest the use of neural networks that are capable of predicting possible diseases or possible treatment failures for future clinical applications in the field of dentistry.
