Current Progress and Challenges of Using Artificial Intelligence in Clinical Dentistry-A Narrative Review.

The concept of machines learning and acting like humans is what is meant by the phrase "artificial intelligence" (AI). Several branches of dentistry are increasingly relying on artificial intelligence (AI) tools. The literature usually focuses on AI models. These AI models have been used to detect and diagnose a wide range of conditions, including, but not limited to, dental caries, vertical root fractures, apical lesions, diseases of the salivary glands, maxillary sinusitis, maxillofacial cysts, cervical lymph node metastasis, osteoporosis, cancerous lesions, alveolar bone loss, the need for orthodontic extractions or treatments, cephalometric analysis, age and gender determination, and more. The primary contemporary applications of AI in the dental field are in undergraduate teaching and research. Before these methods can be used in everyday dentistry, however, the underlying technology and user interfaces need to be refined.

A Contemporary Review of Clinical Factors Involved in Speech-Perspectives from a Prosthodontist Point of View.

Background and Objectives: Learning to speak properly requires a fully formed brain, good eyesight, and a functioning auditory system. Defective phonation is the outcome of a failure in the development of any of the systems or components involved in speech production. Dentures with strong phonetic skills can be fabricated with the help of a dentist who has a firm grasp of speech production and phonetic characteristics. Every dentist strives to perfect their craft by perfecting the balance between the technical, cosmetic, and acoustic aspects of dentistry, or "phonetics". The ideal prosthesis for a patient is one that not only sounds good but also functions well mechanically and aesthetically. Words are spoken by using articulators that alter their size and form. Conclusions: Therefore, a prosthesis should be made in such a way that it does not interfere with the ability to communicate. As a result, a prosthodontist has to have a solid grasp of how speech is made and the numerous parts that go into it.

Does the Loss of Teeth Have an Impact on Geriatric Patients' Cognitive Status?

Significant changes in the microstructure of the brain cause dementia and other mental declines associated with aging and disease. Although research has established a connection between oral health and dementia, the underlying pathologic mechanisms are still unknown. AIM: Our aim was to evaluate dentures' impact on the cognitive state of geriatric patients. MATERIAL AND METHODS: A total of 108 individuals seeking treatment at the Faculty of Dental Medicine in Iasi, Romania, participated in the study, which ran from May 2022 to October 2022. Cognitive dysfunction was assessed using the Mini-Mental State Examination. The acquired data were analyzed with IBM SPSS 26.0, and the p-value was set at 0.05. RESULTS: The average value of the MMSE score was 21.81 ± 3.872. Differences between groups of wearer/non-wearer subjects were statistically significant for most of the questions in the questionnaire. Linear regression analysis showed that individuals with a high MMSE score have prosthodontic treatment. A decrease in the MMSE score is associated with a decrease in masticatory efficiency (B = 1.513, p = 0.268). CONCLUSIONS: This study provides further evidence that tooth loss is associated with worse cognitive performance. It is thus conceivable that the necessary effects can be achieved by increasing the efforts dedicated to preventing tooth loss in the adult population.

A Comparative Analysis of Dental Measurements in Physical and Digital Orthodontic Case Study Models.

Background and Objectives: Study models are essential tools used in the dental teaching process. The aim of the present study was to compare the values obtained by manual and digital orthodontic measurements on physical and digital case study models. Materials and Methods: The physical experimental models were obtained by traditional pouring (improved stone-type IV gypsum products) and by additive manufacturing (resins). The digital experimental models were created by scanning the physical ones, using a white light-emitting diode (LED) source and an L-shaped dental scanner­Swing DOF (DOF, Seoul, Korea). The physical study models were first measured using a digital caliper, and then, they were scanned and evaluated using the DentalCad 3.0 Galway software (exocad GmbH, Darmstadt, Germany). The Pont, Linder−Harth, and Bolton indices, which are used in orthodontics for training students, were derived using the available data. Results: When comparing the linear measurement mean ranks taken on physical study models to those of digital models, no statistically significant differences (p > 0.05) were found. A similar result was also shown when the dentoalveolar growth indicators were analyzed. Conclusions: It can be concluded that dental study models made by direct light processing (DLP) and pouring type IV class gypsum are both acceptable for orthodontic teaching purposes.