Priapism Following Coronary Angiography: A Case Report and Clinical Insights for Diagnosis and Management.

BACKGROUND Priapism is a persistent penile erection that lasts longer than 4 hours. The most common causes of priapism are ischemia and the effects of drugs, including PDE-5 inhibitors, such as sildenafil and anticoagulants. Here, we describe the presentation and management of a 50-year-old man with priapism following coronary artery angiography for the investigation of ischemic heart disease (IHD).

A Proactive Attack Detection for Heating, Ventilation, and Air Conditioning (HVAC) System Using Explainable Extreme Gradient Boosting Model (XGBoost).

The advent of Industry 4.0 has revolutionized the life enormously. There is a growing trend towards the Internet of Things (IoT), which has made life easier on the one hand and improved services on the other.

Multiple Sclerosis Diagnosis Using Machine Learning and Deep Learning: Challenges and Opportunities.

Multiple Sclerosis (MS) is a disease that impacts the central nervous system (CNS), which can lead to brain, spinal cord, and optic nerve problems. A total of 2.8 million are estimated to suffer from MS.

Optimal Path Routing Protocol for Warning Messages Dissemination for Highway VANET.

In vehicular ad hoc networks (VANETs), helpful information dissemination establishes the foundation of communication. One of the significant difficulties in developing a successful dissemination system for VANETs is avoiding traffic fatalities. Another essential success metric is the transfer of reliable and secure warning messages through the shortest path, particularly on highways with high mobility.

Amniotic Fluid Classification and Artificial Intelligence: Challenges and Opportunities.

A fetal ultrasound (US) is a technique to examine a baby's maturity and development. US examinations have varying purposes throughout pregnancy. Consequently, in the second and third trimester, US tests are performed for the assessment of Amniotic Fluid Volume (AFV), a key indicator of fetal health.

Computational Intelligence-Based Model for Exploring Individual Perception on SARS-CoV-2 Vaccine in Saudi Arabia.

Countries around the world are facing so many challenges to slow down the spread of the current SARS-CoV-2 virus. Vaccination is an effective way to combat this virus and prevent its spreading among individuals. Currently, there are more than 50 SARS-CoV-2 vaccine candidates in trials; only a few of them are already in use.

Using a Deep Learning Model to Explore the Impact of Clinical Data on COVID-19 Diagnosis Using Chest X-ray.

The coronavirus pandemic (COVID-19) is disrupting the entire world; its rapid global spread threatens to affect millions of people. Accurate and timely diagnosis of COVID-19 is essential to control the spread and alleviate risk. Due to the promising results achieved by integrating machine learning (ML), particularly deep learning (DL), in automating the multiple disease diagnosis process.

Cough Sound Detection and Diagnosis Using Artificial Intelligence Techniques: Challenges and Opportunities.

Coughing is a common symptom of several respiratory diseases. The sound and type of cough are useful features to consider when diagnosing a disease. Respiratory infections pose a significant risk to human lives worldwide as well as a significant economic downturn, particularly in countries with limited therapeutic resources.

Computational Intelligence-Based Model for Mortality Rate Prediction in COVID-19 Patients.

The COVID-19 outbreak is currently one of the biggest challenges facing countries around the world. Millions of people have lost their lives due to COVID-19. Therefore, the accurate early detection and identification of severe COVID-19 cases can reduce the mortality rate and the likelihood of further complications.

E-Triage Systems for COVID-19 Outbreak: Review and Recommendations.

With population growth and aging, the emergence of new diseases and immunodeficiency, the demand for emergency departments (EDs) increases, making overcrowding in these departments a global problem. Due to the disease severity and transmission rate of COVID-19, it is necessary to provide an accurate and automated triage system to classify and isolate the suspected cases. Different triage methods for COVID-19 patients have been proposed as disease symptoms vary by country.

Applications of Big Data Analytics to Control COVID-19 Pandemic.

The COVID-19 epidemic has caused a large number of human losses and havoc in the economic, social, societal, and health systems around the world. Controlling such epidemic requires understanding its characteristics and behavior, which can be identified by collecting and analyzing the related big data. Big data analytics tools play a vital role in building knowledge required in making decisions and precautionary measures.

A Deep Learning-Based Approach for the Detection of Early Signs of Gingivitis in Orthodontic Patients Using Faster Region-Based Convolutional Neural Networks.

Computer-based technologies play a central role in the dentistry field, as they present many methods for diagnosing and detecting various diseases, such as periodontitis. The current study aimed to develop and evaluate the state-of-the-art object detection and recognition techniques and deep learning algorithms for the automatic detection of periodontal disease in orthodontic patients using intraoral images. In this study, a total of 134 intraoral images were divided into a training dataset ( = 107 [80%]) and a test dataset ( = 27 [20%]).

Predicting Postoperative Length of Stay for Isolated Coronary Artery Bypass Graft Patients Using Machine Learning.

Purpose: Predictive analytics (PA) is a new trending approach in the field of healthcare that uses machine learning to build a prediction model using supervised learning algorithms. Isolated coronary artery bypass grafting (iCABG), an open-heart surgery, is commonly performed in the treatment of coronary heart disease.

Aim: The aim of this study was to develop and evaluate a model to predict postoperative length of stay (PLoS) for iCABG patients using supervised machine learning techniques, and to identify the features with the highest contribution to the model.

FDA Approved Biologics: Can Etanercept and Ustekinumab be Considered a First-Line Systemic Therapy for Pediatric/Adolescents in Moderate to Severe Psoriasis? A Systematic Review.

Psoriasis is a chronic immune-mediated skin disorder. Due to lack of clarity in its pathogenesis, a cure with existing treatment is a big challenge. Biologics, a revolutionary treatment, are potent immunomodulators that explicitly target the culprit cells of the immune system to achieve the maximum level of Psoriasis Area and Severity Index (PASI) score (75 to 90) and clear or almost clear skin in moderate to severe psoriasis.

Coronavirus Disease 2019 (COVID-19) in Children: Vulnerable or Spared? A Systematic Review.

The ongoing pandemic of coronavirus disease 2019 (COVID-19) has affected people from all cultures, religions, gender, and age groups around the world. In the last few months, several studies have been conducted on various aspects of COVID-19. Our goal was to see if the pediatric population is vulnerable to this infection.