Search

Problems related to ventral hernia repairs (VHR) are very common, and evaluating them using computational methods can assist in selecting the most appropriate treatment. This study is based upon data from 3339 patients from different European countries observed during the last 12 years (2012–2023), which were collected by specialists in hernia surgery. Most patients underwent standard surgical procedures, with a growing trend towards laparoscopic surgery. This paper focuses on statistically evaluating the treatment methods in relation to patient age, body mass index (BMI), and the type of repair. Appropriate mathematical methods are employed to extract and classify the selected features, with emphasis on computational and machine-learning techniques. The paper presents surgical hernia treatment statistics related to patient age, BMI, and repair methods. The main conclusions point to mean groin hernia repair (GHR) complications of 19% for patients in the database. The accuracy of separating GHR mesh surgery with and without postoperative complications reached 74.4% using a two-layer neural network classification. Robotic surgeries represent 22.9% of all the evaluated hernia repairs. The proposed methodology suggests both an interdisciplinary approach and the utilization of computational intelligence in hernia surgery, potentially applicable in a clinical setting. [ABSTRACT FROM AUTHOR]

Nitric oxide (NO) is already recognized as an important signaling molecule in the brain. It diffuses easily and the nervous cell's membrane is permeable to NO. The information transmission is three-dimensional, which is different from synaptic transmission. NO operates in two different ways: Close and specific at the synapses of neurons, and as a volumetric transmitter sending signals to various targets, regardless of their anatomy, connectivity or function, when multiple nearby sources act simultaneously. These modes of operation seem to be the basis by which NO is involved in many central mechanisms of the brain, such as learning, memory formation, brain development and synaptogenesis. This work focuses on the effect of NO dynamics on the environment through which it diffuses, using automata networks. We study their implications in the formation of complex functional structures in the volume transmission (VT), which are necessary for the synchronous functional recruitment of neuronal populations. We qualitatively and quantitatively analyze the proposed model regarding these characteristics through the concepts of entropy and mutual information. The proposed deterministic model allows the incorporation of fuzzy dynamics. With that, a generalized model based on fuzzy automata networks can be provided. This allows the generation and diffusion processes of NO to be arbitrarily produced and maintained over time. This model can accommodate arbitrary processes in decision-making mechanisms and can be part of a complete formal VT framework in the brain and artificial neural networks. [ABSTRACT FROM AUTHOR]

The integration of computational intelligence and augmented reality has become increasingly prevalent in dental practices. Three-dimensional (3D) printing techniques have now become routine in orthodontics, prosthetics, and maxillofacial surgery. The objective of this study is to assess the effectiveness of stereolithography (SLA) printed models compared to traditional plaster casts over a three-year period. The experimental dataset consisted of 36 orthodontic patients, each with SLA printed models and plaster casts for both the upper and lower jaws, resulting in a total of 72 scans in the form of Standard Template Library (STL) files and 72 traditional impressions. The upper dental arch models were constructed using an SLA 3D printer, employing a blue 405 nm laser beam to solidify a liquid polymer. The classical plaster casts were prepared in a dental laboratory. The models were retained for long-term orthodontic therapy control. To evaluate the differences between the dental models, a laboratory scanner was used to generate virtual casts. The meshes obtained were adjusted and pre-aligned using the best-fit algorithm. Subsequently, registration of the models was performed using the iterative closest point (ICP) algorithm. Distances between the point clouds and meshes for each point of the printed model were calculated by determining the nearest triangle on the reference mesh (cast). Additionally, the model surfaces were assessed using a scanning electron microscope and a stereomicroscope. The results from 22 experimental datasets indicated a high level of agreement between the plaster casts and virtual surfaces, with a mean absolute difference of 0.018 mm and a standard deviation of 0.17 mm. These values were obtained by averaging 231,178 data points for each comparison. Overall, this study demonstrates the comparability and accuracy of SLA printed models in relation to traditional plaster casts, supporting their potential as reliable alternatives in dental practice. [ABSTRACT FROM AUTHOR]

Gait disorders accompany a number of neurological and musculoskeletal disorders that significantly reduce the quality of life. Motion sensors enable high-quality modelling of gait stereotypes. However, they produce large volumes of data, the evaluation of which is a challenge. In this publication, we compare different data reduction methods and classification of reduced data for use in clinical practice. The best accuracy achieved between a group of healthy individuals and patients with ataxic gait extracted from the records of 43 participants (23 ataxic, 20 healthy), forming 418 segments of straight gait pattern, is 98% by random forest classifier preprocessed by t-distributed stochastic neighbour embedding. [ABSTRACT FROM AUTHOR]