Radiomics-Based Artificial Intelligence and Machine Learning Approach for the Diagnosis and Prognosis of Idiopathic Pulmonary Fibrosis: A Systematic Review.

Idiopathic pulmonary fibrosis (IPF) is a devastating interstitial lung disease (ILD) characterized by progressive fibrosis and poor survival outcomes. Accurate diagnosis and prognosis remain challenging due to overlapping features with other ILDs and variability in imaging interpretation. This systematic review evaluates the current evidence on artificial intelligence (AI) and machine learning (ML) applications for the diagnosis and prognosis of IPF using computed tomography (CT) imaging.

Evaluating the Clinical Outcomes of Empagliflozin in Heart Failure Patients With Preserved Ejection Fraction: A Systematic Review.

Heart failure with preserved ejection fraction (HFpEF) represents a significant clinical challenge due to its complex pathophysiology and limited therapeutic options. This systematic review evaluates the efficacy and safety of empagliflozin, a sodium-glucose co-transporter 2 (SGLT2) inhibitor, in patients with HFpEF. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we analyzed 11 studies, predominantly from the EMPEROR-Preserved trial and its sub-analyses, investigating empagliflozin in HFpEF patients.

Ivabradine in the treatment of congenital junctional ectopic tachycardia: A systematic review.

Background: Congenital junctional ectopic tachycardia (CJET) is a rare but life-threatening arrhythmia in neonates and infants, often refractory to conventional antiarrhythmic therapy. Ivabradine, a selective inhibitor of hyperpolarization-activated cyclic nucleotide-gated channels, has emerged as a promising drug for CJET management.

Aim: To evaluate the efficacy and safety of ivabradine in the management of CJET.

Emerging Therapies for Non-Alcoholic Steatohepatitis (NASH): A Comprehensive Review of Pharmacological and Non-Pharmacological Approaches.

Non-alcoholic steatohepatitis (NASH) has emerged as a significant global health concern, closely linked to the obesity epidemic and metabolic syndrome. This review explores emerging therapies for NASH that go beyond traditional lifestyle modifications. The complex pathophysiology of NASH, involving insulin resistance, lipotoxicity, oxidative stress, and chronic inflammation, offers multiple targets for therapeutic intervention.

Artificial Intelligence (AI)-Enhanced Detection of Diabetic Retinopathy From Fundus Images: The Current Landscape and Future Directions.

Diabetic retinopathy (DR) remains a leading cause of vision loss worldwide, with early detection critical for preventing irreversible damage. This review explores the current landscape and future directions of artificial intelligence (AI)-enhanced detection of DR from fundus images. Recent advances in deep learning and computer vision have enabled AI systems to analyze retinal images with expert-level accuracy, potentially transforming DR screening.

Metformin and Atrial Fibrillation: A Systematic Review of Their Association.

Atrial fibrillation (AF) is a common cardiac arrhythmia with a significant impact on patient outcomes and healthcare systems. Given the rising incidence of AF with age and its association with conditions, such as diabetes, there is growing interest in exploring pharmacological interventions that might mitigate AF risk. Metformin, a widely prescribed antihyperglycemic agent for type 2 diabetes mellitus (T2DM), has demonstrated various cardiovascular benefits, including anti-inflammatory and antioxidative properties, leading to speculations about its potential role in AF prevention.

Assessing the Renal Outcomes of Semaglutide in Diabetic Kidney Disease: A Systematic Review.

Diabetic kidney disease (DKD) is a prevalent microvascular complication of diabetes, posing a significant health burden. Semaglutide, a glucagon-like peptide-1 receptor agonist, has shown promise in mitigating renal outcomes in DKD. This systematic review aimed to evaluate the renal effects of semaglutide in individuals with DKD.

Artificial intelligence and machine learning in peritoneal dialysis: a systematic review of clinical outcomes and predictive modeling.

Background: The integration of artificial intelligence (AI) and machine learning (ML) in peritoneal dialysis (PD) presents transformative opportunities for optimizing treatment outcomes and informing clinical decision-making. This study aims to provide a comprehensive overview of the applications of AI/ML techniques in PD, focusing on their potential to predict clinical outcomes and enhance patient care.

Materials And Methods: This systematic review was conducted according to PRISMA guidelines (2020), searching key databases for articles on AI and ML applications in PD.

Predicting the Progression of Chronic Kidney Disease: A Systematic Review of Artificial Intelligence and Machine Learning Approaches.

Chronic kidney disease (CKD) is a progressive condition characterized by gradual loss of kidney function, necessitating timely monitoring and interventions. This systematic review comprehensively evaluates the application of artificial intelligence (AI) and machine learning (ML) techniques for predicting CKD progression. A rigorous literature search identified 13 relevant studies employing diverse AI/ML algorithms, including logistic regression, support vector machines, random forests, neural networks, and deep learning approaches.