The Advantages of Implant Therapy in Management of Edentulous jaws - Case Report

Potpuna proteza često nema zadovoljavajuću stabilnost u usnoj šupljini. Osobito nakon više godina nošenja proteze nastaju promjene u međučeljusnim odnosima te posljedično gubitak alveolarne kosti i poremećaj u stabilizaciji proteze. Postoji više načina implantološko-protetske rehabilitacije potpune bezubosti. U radu prikazujemo uporabu 2 usadka u bezuboj čeljusti te protetsku rehabilitaciju s tzv. kuglama. Prednosti ovakva načina implantološko-protetske rehabilitacije jesu u razmjerno lakom postavljanju usadka, postizanju zadovoljavajuće stabilnosti proteze te financijski razmjerno povoljnom rješavanju problema stabilnosti u odnosu prema drugim implantološkim metodama.Long term use of a conventional denture typically results in advanced alveolar bone loss, following a decrease of intermaxillary space and lack of stability. There a few ways in implant prosthodontics treatment of completely edentulous jaws. In this case report we show the use of two implants in completely endentulous patients and prosthetic rehabilitation with snap attachment. The advantages of implant prostodontics are relatively easily placement into the bone, stable implant assisted overlay denture, and relatively acceptable price

Foramen Mandibulae as an Indicator of Successful Conduction Anesthesia

Comparative measurements were made of 144 orthopantomographs in 50 patients with successful and 94 patients with unsuccessful inferior alveolar nerve block anesthesia. The results show that the bony lingula is prominent in 28.5% of all patients, or in 56.0% of those with unsuccessful anesthesia. The variables mandibular notch vs. mandibular foramen (MN-MF) and the anterior ramus ridge vs. mandibular foramen (ARR-MF) show greater distances in the group of patients with successful anesthesia, while the variables of posterior ramus ridge vs. mandibular foramen (PRR-MF) and mandibular angle vs. mandibular foramen (MA-MF) were greater in the group of patients with unsuccessful anesthesia (p > 0.05). It is concluded that the variability in position of the mandibular foramen among others may be responsible for an occasional failure of inferior alveolar nerve block

Epidemiological Analysis of Oral Surgery Procedures

The epidemiological study was conducted to assess oral health of patients referred to the Department of Oral Surgery at Clinical Hospital Center in Rijeka. The distribution of particular diagnoses and surgical interventions in relation to frequency of occurrence was tested. The total of 1,268 patients aged from 5 to 89 years, both sexes, were included in the study. All the patients were treated under local anesthesia. The most common reason for referral to oral surgery was chronic periapical lesion (33.3%), followed by retained root (26.7%), impacted tooth (12.7%), and radicular cyst (8.3%). The majority of patients, residents of Rijeka city area, were treated for the diagnosis of adult periodontitis, while the radicular cysts and hypertrophy of the upper frenulum were more frequent referral diagnoses in patients coming from the areas around Rijeka. Extractions were performed more frequently in patients from Rijeka, while cystectomies with apicectomies and frenulectomies in other patients

Estimation of covid-19 epidemiology curve of the united states using genetic programming algorithm

© 2021 by the authors. Licensee MDPI, Basel, Switzerland. Estimation of the epidemiology curve for the COVID-19 pandemic can be a very computationally challenging task. Thus far, there have been some implementations of artificial intelligence (AI) methods applied to develop epidemiology curve for a specific country. However, most applied AI methods generated models that are almost impossible to translate into a mathematical equation. In this paper, the AI method called genetic programming (GP) algorithm is utilized to develop a symbolic expression (mathematical equation) which can be used for the estimation of the epidemiology curve for the entire U.S. with high accuracy. The GP algorithm is utilized on the publicly available dataset that contains the number of confirmed, deceased and recovered patients for each U.S. state to obtain the symbolic expression for the estimation of the number of the aforementioned patient groups. The dataset consists of the latitude and longitude of the central location for each state and the number of patients in each of the goal groups for each day in the period of 22nd January 2020–3rd December 2020. The obtained symbolic expressions for each state are summed up to obtain symbolic expressions for estimation of each of the patient groups (confirmed, deceased and recovered). These symbolic expressions are combined to obtain the symbolic expression for the estimation of the epidemiology curve for the entire U.S. The obtained symbolic expressions for the estimation of the number of confirmed, deceased and recovered patients for each state achieved R2 score in the ranges 0.9406–0.9992, 0.9404–0.9998 and 0.9797–0.99955, respectively. These equations are summed up to formulate symbolic expressions for the estimation of the number of confirmed, deceased and recovered patients for the entire U.S. with achieved R2 score of 0.9992, 0.9997 and 0.9996, respectively. Using these symbolic expressions, the equation for the estimation of the epidemiology curve for the entire U.S. is formulated which achieved R2 score of 0.9933. Investigation showed that GP algorithm can produce symbolic expressions for the estimation of the number of confirmed, recovered and deceased patients as well as the epidemiology curve not only for the states but for the entire U.S. with very high accuracy

Automatic evaluation of the lung condition of COVID-19 patients using X-ray images and convolutional neural networks

© 2021 by the authors. Licensee MDPI, Basel, Switzerland. COVID-19 represents one of the greatest challenges in modern history. Its impact is most noticeable in the health care system, mostly due to the accelerated and increased influx of patients with a more severe clinical picture. These facts are increasing the pressure on health systems. For this reason, the aim is to automate the process of diagnosis and treatment. The research presented in this article conducted an examination of the possibility of classifying the clinical picture of a patient using X-ray images and convolutional neural networks. The research was conducted on the dataset of 185 images that consists of four classes. Due to a lower amount of images, a data augmentation procedure was performed. In order to define the CNN architecture with highest classification performances, multiple CNNs were designed. Results show that the best classification performances can be achieved if ResNet152 is used. This CNN has achieved AUCmacro and AUCmicro up to 0.94, suggesting the possibility of applying CNN to the classification of the clinical picture of COVID-19 patients using an X-ray image of the lungs. When higher layers are frozen during the training procedure, higher AUCmacro and AUCmicro values are achieved. If ResNet152 is utilized, AUCmacro and AUCmicro values up to 0.96 are achieved if all layers except the last 12 are frozen during the training procedure

Application of artificial intelligence-based regression methods in the problem of covid-19 spread prediction: A systematic review

COVID-19 is one of the greatest challenges humanity has faced recently, forcing a change in the daily lives of billions of people worldwide. Therefore, many efforts have been made by researchers across the globe in the attempt of determining the models of COVID-19 spread. The objectives of this review are to analyze some of the open-access datasets mostly used in research in the field of COVID-19 regression modeling as well as present current literature based on Artificial Intelligence (AI) methods for regression tasks, like disease spread. Moreover, we discuss the applicability of Machine Learning (ML) and Evolutionary Computing (EC) methods that have focused on regressing epidemiology curves of COVID-19, and provide an overview of the usefulness of existing models in specific areas. An electronic literature search of the various databases was conducted to develop a comprehensive review of the latest AI-based approaches for modeling the spread of COVID-19. Finally, a conclusion is drawn from the observation of reviewed papers that AI-based algorithms have a clear application in COVID-19 epidemiological spread modeling and may be a crucial tool in the combat against coming pandemics