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the equipment is acquired, it may require regular maintenance and consumables. Additionally, the obsolescence of the equipment must be included as technology evolves over time and may need to be replaced with new financial investment. Finally, to represent a good inversion, the digitalization of dental care workflows should provide quantifiable advantages in terms of quality, time consumption, and personnel cost reduction. As ROI calculation can be complex, professional advice on these economic aspects should be taken.
1.3.8 Advantages of Digital Dentistry for Clinics
Before digital dentistry, it was common for the client to visit the dentist for multiple consultations to diagnose and plan dental treatments, while procedures could last long periods of time. However, dental practices are now able to diagnose, plan, and treat their patients much faster and with fewer clinical steps and less chair time using digital technology.
Besides the potential financial benefits, the implementation of digital solutions can offer indirect benefits. The patient experience tends to be better and more comfortable when using digital treatments, with a sense of quality and a high level of technological innovation. Aspects of treatments such as planning can be easier for the patient to understand. 3D digital simulations and impressions can improve patient engagement and approval, especially when esthetics are involved.
With the adoption of digitalized procedures, laboratory and clinical procedures tend to cooperate better with administrative records, treatment planning, and fabrication through standardized workflows.
1.3.9 Workflow in a Digitalized Dental Clinic
The term workflow refers to a sequence of predefined tasks executed by various people within the work environment. The establishment of workflows creates mindsets throughout the organization that ensure a standardized exchange of data along the dental workflow from planning to therapy [10]. Each dental clinic may determine its own internal workflows, and how they would interact when some outsourced help is needed. Some procedures can eventually be planned to be exclusively digital, and there can be a mix of analogue and digital components (Figure 1.3).
Furthermore, dental clinics commonly have several professionals who can conduct treatments with different levels of expertise. In this context, treatment workflows lead to standardization of outcomes and skill levels among the dental team. This enables a more consistent and controllable production outcome.
Figure 1.3 Digital workflow involving imaging and planning centers. All treatment plans are ultimately to be approved by the dental clinician responsible for the case.
1.4 Current Knowledge and Perspectives in Artificial Intelligence in Dentistry
Artificial intelligence (AI) is a term commonly used to represent the concept of artificially created human‐like intelligence [11]. AI has revolutionized several fields of scientific research and development, with improvements ranging from facial recognition on our smartphones to genome analysis. AI has been developed in different sectors of society to solve various tasks by utilizing data more optimally, including health areas like medicine and dentistry. Data science is an umbrella term that refers to the interdisciplinary area dedicated to the analysis of structured and unstructured data, which aims at the application of statistical methods for data analytics. The work of data scientists includes using advanced techniques such as machine learning. Machine learning is a subfield of AI, in which algorithms are applied to learn patterns intrinsic to data structures, which allow data predictions (supervised machine learning) and data reduction and data handling (unsupervised machine learning) [12]. One of the factors associated with the adoption of this methodology would be that AI is especially suitable for overcoming the variability in subjective individual examination and for increasing the effectiveness of diagnosis [13].
Unsupervised machine learning is used for reducing the dimensionality of such datasets, increasing interpretability. Clustering uses metric variables as input to group data, enabling reduction of data into smaller groups, with categorical variables as outputs. Principal component analysis (PCA) also enables data reduction by grouping variables that correlate with each other. Multiple correspondence analysis (MCA) is used for data reduction and data handling of categorical variables that associate with each other. Unsupervised machine learning is very suitable for handling a large amount of data and for data mining, acquired through dental patients' records for example [14].
Supervised machine learning, defined by its use of labeled datasets, is used for prediction. Accuracy will therefore depend on the size and quality of the database, as well as the labeling and training method. Supervised machine learning uses linear regression, logistic regression, ensemble models, and neural networks (NNs). The main component of supervised machine learning is labeling the dataset to train the algorithms and to accurately classify data or predict results (Figure 1.4).
Neural networks are particularly useful for complex data structures, such as imaging data, as these models are capable of representing an image and its hierarchical resources, such as edges, corners, shapes, and macroscopic patterns [15, 16]. During the training process, the corresponding data and labels are transmitted repeatedly by the NNs. The computational power of these NNs depends on the quality and quantity of training data, which allow these networks to update the weights assigned to each variable of the model in question, and this training can be supervised or not. NNs can also be associated in several layers. The term deep learning is a reference to deep (multilayered) NN architectures [12].
Figure 1.4 LabelImg AI software tool being used to label the dataset.
Recently, convolutional neural networks (CNNs) have been developed and applied in many aspects of the health field, including dentistry, with the performance of various tasks like image classification and object detection [11, 12]. CNNs use a clever trick to reduce the amount of training data needed to detect objects under different conditions. The trick is basically to use the same input weights for multiple artificial neurons – so that all those neurons are activated by the same pattern – but with different input pixels. Every convolutional layer responds to stimuli only in a restricted region of the image's field of interest, known as the receptive field. This structure differs from conventional image classification algorithms and other deep learning algorithms, as the CNN can learn the type of filter created manually in conventional algorithms [15, 16].
When training image datasets are entered into a machine learning system, the learning procedures are automatically repeated, without the need for a manual definition of the image characteristics. In this way, machine learning methods (with or without deep learning) can adaptively learn image characteristics and simultaneously perform image classification [16]. Thus, the results from models that use machine learning differ greatly from systems that operate with conventional programming. From the moment that a NN is trained, weights are assigned to the characteristics of the sample data, and the results are intrinsically linked to the sample used for training this model. Therefore, more favorable results can only be found with AI training with a new, larger, and more accurate sample.
Methods based on machine learning depend on the quantity and quality of information that can be learned (based on a set of training data). A smaller sample size can reduce the potential for identification accuracy in test images, thus decreasing the sensitivity and specificity of the test. Furthermore, a large sample can be created by using data augmentation, which is a feature used to increase the sample of images by using image rotation