Current Issue : July-September Volume : 2023 Issue Number : 3 Articles : 5 Articles
Graphene-based materials have been shown to have advantageous properties in biomedical and dental applications due to their high mechanical, physiochemical, antibacterial, and stem cell differentiating properties. Although graphene-based materials have displayed appropriate biocompatible properties when used in implant materials for orthopedic applications, little research has been performed to specifically test the biocompatibility of graphene for dental applications. The oral environment, compared to the body, varies greatly and must be considered when evaluating biocompatibility requirements for dental applications. This review will discuss in vitro and in vivo studies that assess graphene’s cytotoxicity, antibacterial properties, and cell differentiation ability to evaluate the overall biocompatibility of graphene-based materials for dental applications. Particle shape, size, and concentration were found to be major factors that affected overall biocompatibility of graphene....
Neonatal diseases are among the main causes of morbidity and a significant contributor to underfive mortality in the world. There is an increase in understanding of the pathophysiology of the diseases and the implementation of different strategies to minimize their burden. However, improvements in outcomes are not adequate. Limited success is due to different factors, including the similarity of symptoms, which can lead to misdiagnosis, and the inability to detect early for timely intervention. In resourcelimited countries like Ethiopia, the challenge is more severe. Low access to diagnosis and treatment due to the inadequacy of neonatal health professionals is one of the shortcomings. Due to the shortage of medical facilities, many neonatal health professionals are forced to decide the type of disease only based on interviews. They may not have a complete picture of all variables that have a contributing effect on neonatal disease from the interview. This can make the diagnosis inconclusive and may lead to a misdiagnosis. Machine learning has great potential for early prediction if relevant historical data is available. We have applied a classification stacking model for the following four main neonatal diseases: sepsis, birth asphyxia, necrotizing enter colitis (NEC), and respiratory distress syndrome. These diseases account for 75% of neonatal deaths. Thedataset has been obtained from the Asella Comprehensive Hospital. It has been collected between 2018 and 2021. The developed stacking model was compared to three related machine-learning models XGBoost (XGB), Random Forest (RF), and Support Vector Machine (SVM). The proposed stacking model outperformed the other models, with an accuracy of 97.04%. We believe that this will contribute to the early detection and accurate diagnosis of neonatal diseases, especially for resource-limited health facilities....
This study aimed to explore the independent risk factors for community-acquired pneumonia (CAP) complicated with acute respiratory distress syndrome (ARDS) and to predict and evaluate the risk of ARDS in CAP patients based on artificial neural network models (ANNs). We retrospectively analyzed eligible 989 CAP patients (632 men and 357 women) who met the criteria from the comprehensive intensive care unit (ICU) and the respiratory and critical care medicine department of Changzhou Second People’s Hospital, Jiangsu Provincial People’s Hospital, Nanjing Military Region General Hospital, and Wuxi Fifth People’s Hospital between February 2018 and February 2021. The best predictors to model the ANNs were selected from 51 variables measured within 24 h after admission. By using this model, patients were divided into a training group (n 701) and a testing group (n 288 patients). Results showed that in 989 CAP patients, 22 important variables were identified as risk factors. The sensitivity, specificity, and accuracy of the ANNs model training group were 88.9%, 90.1%, and 89.7%, respectively. When ANNs were used in the test group, their sensitivity, specificity, and accuracy were 85.0%, 87.3%, and 86.5%, respectively; when ANNs were used to predict ARDS, the area under the receiver operating characteristic (ROC) curve was 0.943 (95% confidence interval (0.918–0.968)). The nine most important independent variables affecting the ANNs models were lactate dehydrogenase (100%), activated partial thromboplastin time (84.6%), procalcitonin (83.8%), age (77.9%), maximum respiratory rate (76.0%), neutrophil (75.9%), source of admission (68.9%), concentration of total serum kalium (61.3%), and concentration of total serum bilirubin (50.4%) (all important >50%). The ANNs model and the logistic regression models were significantly different in predicting and evaluating ARDS in CAP patients. Thus, the ANNs model has a good predictive value in predicting and evaluating ARDS in CAP patients, and its performance is better than that of the logistic regression model in predicting the incidence of ARDS patients....
To determine the current evidence on artificial neural network (ANN) in prognostic studies of musculoskeletal diseases (MSD) and to assess the accuracy of ANN in predicting the prognosis of patients with MSD. The scoping review was reported under the Preferred Items for Systematic Reviews and the Meta-Analyses extension for Scope Reviews (PRISMA-ScR). Cochrane Library, Embase, Pubmed, and Web of science core collection were searched from inception to January 2023. Studies were eligible if they used ANN to make predictions about MSD prognosis. Variables, model prediction accuracy, and disease type used in the ANN model were extracted and charted, then presented as a table along with narrative synthesis. Eighteen Studies were included in this scoping review, with 16 different types of musculoskeletal diseases. The accuracy of the ANN model predictions ranged from 0.542 to 0.947. ANN models were more accurate compared to traditional logistic regression models. This scoping review suggests that ANN can predict the prognosis of musculoskeletal diseases, which has the potential to be applied to different types of MSD....
Background The ever-growing need for cheap, simple, fast, and accurate healthcare solutions spurred a lot of research activities which are aimed at the reliable deployment of artificial intelligence in the medical fields. However, this has proved to be a daunting task especially when looking to make automated diagnoses using biomedical image data. Biomedical image data have complex patterns which human experts find very hard to comprehend. Against this backdrop, we applied a representation or feature learning algorithm: Invariant Scattering Convolution Network or Wavelet scattering Network to retinal fundus images and studied the the efficacy of the automatically extracted features therefrom for glaucoma diagnosis/detection. The influence of wavelet scattering network parameter settings as well as 2-D channel image type on the detection correctness is also examined. Our work is a distinct departure from the usual method where wavelet transform is applied to pre-processed retinal fundus images and handcrafted features are extracted from the decomposition results. Here, the RIM-ONE DL image dataset was fed into a wavelet scattering network developed in the Matlab environment to achieve a stage-wise decomposition process called wavelet scattering of the retinal fundus images thereby, automatically learning features from the images. These features were then used to build simple and computationally cheap classification algorithms. Results Maximum detection correctness of 98% was achieved on the held-out test set. Detection correctness is highly sensitive to scattering network parameter setting and 2-D channel image type. Conclusion A superficial comparison of the classification results obtained from our work and those obtained using a convolutional neural network underscores the potentiality of the proposed method for glaucoma detection....
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