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Artificial Intelligence has emerged as a breakthrough in many fields including medicine and dentistry where new approaches can be employed to solve challenging decision making processes faced in the dental field. Artificial intelligence can be used as a decision support mechanism to solve the increasing population and consequently the increasing dental treatment needs. It also assists dentists in diagnosis and treatment planning stages that require expert opinion. This mini-review covers some of the recent studies in this area and envisions future directions on the use of machine learning in dental problems.

Recently, radiation therapy with protons has received much attention. In 2020, nearly 250,000 patients in 110 centers around the world preferred radiation therapy with protons for treatment. One of them, proton-based stereotactic radiosurgery, is a treatment option after surgical interventions in Arteriovenous Malformation (AVM). The main goal of this research is to compute the amount of linear energy transfer (LET) delivered to the target because the highly preferred liquid embolic agents weaken the proton beam. LET, recoils, lateral straggle and crystalline vanacies accumulated by three different embolization materials: Ethylene vinyl alcohol (On), Cyanoacrylate (Ca) and polyvinyl alcohol (Pa) were analysed with an aim to calculate the ionization, rebound, phonon and lateral straggle of the brain at different depths for a 2 cm diameter AVM using Monte Carlo-based simulation. The obtained data were evaluated and the results compared against water. The closest Bragg peak value to water was found in Ca with a 4.1% difference. The closest to water in beam range was On with 0.9% difference. The highest recoils value was observed in PVA embolization material. The lowest lateral straggle value at all beam energy levels was in PVA embolization material.

Today, with rapid advances in technology, computer-based studies and Artificial Intelligence (AI) approaches are finding their place in every field, especially in the medical sector, where they attract great attention. The Temporomandibular Joint (TMJ) stands as the most intricate joint within the human body, and diseases related to this joint are quite common. In this paper, we reviewed studies that utilize AI-based algorithms and computer-aided programs for investigating TMJ and TMJ-related diseases. We conducted a literature search on Google Scholar, Web of Science, and PubMed without any time constraints and exclusively selected English articles. Moreover, we examined the references to papers directly related to the topic matter. As a consequence of the survey, a total of 66 articles within the defined scope were assessed. These selected papers were distributed across various areas, with 11 focusing on segmentation, 3 on Juvenile Idiopathic Arthritis (JIA), 10 on TMJ Osteoarthritis (OA), 21 on Temporomandibular Joint Disorders (TMD), 6 on decision support systems, 10 reviews, and 5 on sound studies. The observed trend indicates a growing interest in artificial intelligence algorithms, suggesting that the number of studies in this field will likely continue to expand in the future.

This paper proposes a novel data classification framework, combining sparse auto-encoders (SAEs) and a post-processing system consisting of a linear system model relying on Particle Swarm Optimization (PSO) algorithm. All the sensitive and high-level features are extracted by using the first auto-encoder which is wired to the second auto-encoder, followed by a Softmax function layer to classify the extracted features obtained from the second layer. The two auto-encoders and the Softmax classifier are stacked in order to be trained in a supervised approach using the well-known backpropagation algorithm to enhance the performance of the neural network. Afterwards, the linear model transforms the calculated output of the deep stacked sparse auto-encoder to a value close to the anticipated output. This simple transformation increases the overall data classification performance of the stacked sparse auto-encoder architecture. The PSO algorithm allows the estimation of the parameters of the linear model in a metaheuristic policy. The proposed framework is validated by using three public datasets, which present promising results when compared with the current literature. Furthermore, the framework can be applied to any data classification problem by considering minor updates such as altering some parameters including input features, hidden neurons and output classes.

This paper addresses a new hybrid feature extractor algorithm, which in essence integrates a Fast-Hessian detector into the SIFT (Scale Invariant Feature Transform) algorithm. Feature extractors mainly consist of two essential parts: feature detector and descriptor extractor. This study proposes to integrate (Speeded-Up Robust Features) SURF’s hessian detector into the SIFT algorithm so as to boost the total number of true matched pairs. This is a critical requirement in image processing and widely used in various corresponding fields from image stitching to object recognition. The proposed hybrid algorithm has been tested under different experimental conditions and results are quite encouraging in terms of obtaining higher matched pairs and precision score.

This survey addresses the existing state of knowledge related to vision-based mobile robots, especially including their background and history, current trends, and mapless navigation. This paper not only discusses studies relevant to vision-based mobile robot systems but also critically evaluates the methodologies which have been developed and that directly affect such systems.

This paper proposes a new obstacle avoidance method using a single monocular vision camera as the only sensor which is called as Hybrid Architecture . This architecture integrates a high performance appearance-based obstacle detection method into an optical flow-based navigation system. The hybrid architecture was designed and implemented to run both methods simultaneously and is able to combine the results of each method using a novel arbitration mechanism. The proposed strategy successfully fused two different vision-based obstacle avoidance methods using this arbitration mechanism in order to permit a safer obstacle avoidance system. Accordingly, to establish the adequacy of the design of the obstacle avoidance system, a series of experiments were conducted. The results demonstrate the characteristics of the proposed architecture, and the results prove that its performance is somewhat better than the conventional optical flow-based architecture. Especially, the robot employing Hybrid Architecture avoids lateral obstacles in a more smooth and robust manner than when using the conventional optical flow-based technique.