Medicina
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Review
Crossroads in Liver Transplantation: Is Artificial Intelligence the Key to Donor-Recipient Matching?
Liver transplantation outcomes have improved in recent years. However, with the emergence of expanded donor criteria, tools to better assist donor-recipient matching have become necessary. Most of the currently proposed scores based on conventional biostatistics are not good classifiers of a problem that is considered "unbalanced." In recent years, the implementation of artificial intelligence in medicine has experienced exponential growth. ⋯ The ability to handle a large number of variables with speed, objectivity, and multi-objective analysis is one of its advantages. Artificial neural networks and random forests have been the most widely used deep classifiers in this field. This review aims to give a brief overview of D-R matching and its evolution in recent years and how artificial intelligence may be able to provide a solution.
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Background and Objectives: The aim of the study was to evaluate vision-related quality of life (VR-QOL) and treatment satisfaction (TS) in patients with diabetic retinopathy treated with panretinal photocoagulation (PRP). Material and Methods: The panel study included 95 patients who underwent PRP for diabetic retinopathy. Eligible patients with no history of previous PRP were interviewer-administered the National Eye Institute Visual Function Questionnaire (NEI VFQ-25) and Retinopathy Treatment Satisfaction Questionnaire (RetTSQ) beforehandand one month after the last session of laser application. ⋯ Conclusion: The use of vision-related quality of life and treatment satisfaction questionnaires in conjunction with clinical examination, appears to provide a more comprehensive overview of an individual's daily well-being following PRP. Laser treatment for diabetic retinopathy leads to deterioration of some of the patients' perceived VR-QOL and TS. Health-care providers should inform patients about their treatment options and together decide which therapeutic method is best for them.
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Background and Objectives: The spinous foramen (FS) of the skull is an opening located in the greater wing of the sphenoid bone at the base of the skull, and it includes the middle meningeal vessels and the meningeal branch of the mandibular trigeminal nerve. The FS is commonly used as an anatomical landmark in neurosurgical procedures and neuroimaging of the middle cranial fossa because of its relationship with other cranial foramina and surrounding vascular and nervous structures. Thus, specific knowledge of its topography and possible anatomical variations is important regarding some surgical interventions and skull imaging. ⋯ The FS was smaller than the foramen ovale. A round and oval FS shape was the most common (42.1% and 32.8% of the samples, respectively), followed by drop-shaped (12.5%) and irregular-shaped (12.5%) foramina. Conclusions: In conclusion, FS variations among individuals are common and must be considered by surgeons during skull base interventions in order to avoid accidents and postoperative complications.
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Background and Objectives: Recently, many studies have focused on the early diagnosis of coronary artery disease (CAD), which is one of the leading causes of cardiac-associated death worldwide. The effectiveness of the most important features influencing disease diagnosis determines the performance of machine learning systems that can allow for timely and accurate treatment. We performed a Hybrid ML framework based on hard ensemble voting optimization (HEVO) to classify patients with CAD using the Z-Alizadeh Sani dataset. ⋯ Results: Five fold cross-validation experiments with the HEV classifier showed excellent prediction performance results with the 10 best balanced features obtained using SMOTE and feature selection. All evaluation metrics results reached > 98% with the HEV classifier, and the gradient-boosting model was the second best classification model with accuracy = 97% and F1-score = 98%. Conclusions: When compared to modern methods, the proposed method perform well in diagnosing coronary artery disease, and therefore, the proposed method can be used by medical personnel for supplementary therapy for timely, accurate, and efficient identification of CAD cases in suspected patients.
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Background and Objectives: A dental environment possesses a serious ergonomic health issue on the dental team members which in turn contributes to the development of work-related musculoskeletal disorders (WMSDs). The present research is aimed at evaluating the frequency of musculoskeletal disorders and their associated sociodemographic and work environment risk factors among dentists in the United Arab Emirates. Material and Methods: In this cross-sectional study, a pretested and validated questionnaire was sent via email as well as on different social media platforms to a total of 497 dentists. ⋯ Multivariate binary logistic regression for the number of regions affected by WMSDs revealed that not using an ergonomic dental chair (OR 2.70, 95% CI, 1.14-6.36) and high stress in the work environment (OR 1.31, 95% CI 1.02 to1.67) were associated with more body regions being affected by WMSDs. Conclusions: This study highlights the high prevalence rate of WMSDs among dentists in the UAE. Future research should be directed towards reducing stress in the work environment, increasing awareness regarding the importance of an ergonomic dental chair, and reducing gaps between private and governmental practices.