ABSTRACT
The retrocaval ureter is an uncommon anomaly where the ureter passes behind the inferior vena cava. Open surgery had been the gold standard for treatment. We are presenting a case of the retrocaval ureter with ureteral calculi, which was effectively managed by open surgery. A 27-year-old male presented with a nine-month history of flank pain. He had no history of chronic illnesses. Physical examinations and laboratory findings were within normal. A computed tomography (CT) scan was done to confirm the diagnosis of retrocaval ureter with ureteral stones. The subcostal incision was made. Then, the proximal and lower ureter was transected at the point where it went retrocaval. The stones were extracted; then, watertight anastomosis was done. Ultrasound used for the follow-up of the patient for six months showed no hydronephrosis. Retrocaval ureteral may have no symptoms or be linked to nonspecific symptoms. The diagnosis of the retrocaval ureter is frequently delayed. Surgical management is utilized in the majority of cases.
ABSTRACT
Diabetes mellitus, a condition characterized by dysregulation of blood glucose levels, poses significant health challenges globally. This meta-analysis and systematic review aimed to evaluate the effectiveness of artificial intelligence (AI) in managing diabetes, underpinned by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The review scrutinized articles published between January 2019 and February 2024, sourced from six electronic databases: Web of Science, Google Scholar, PubMed, Cochrane Library, EMBASE, and MEDLINE, using keywords such as "Artificial intelligence use in medicine, Diabetes management, Health technology, Machine learning, Diabetic patients, AI applications, and Health informatics." The analysis revealed a notable variance in the prevalence of diabetes symptoms between patients managed with AI models and those receiving standard treatments or other machine learning models, with a risk ratio (RR) of 0.98 (95% CI: 0.88-1.08, I2 = 0%). Sub-group analyses, focusing on symptom detection and management, consistently showed outcomes favoring AI interventions, with RRs of 0.97 (95% CI: 0.87-1.08, I2 = 0%) for symptom detection and 0.97 (95% CI: 0.56-1.57, I2 = 0%) for management, respectively. The findings underscore the potential of AI in enhancing diabetes care, particularly in early disease detection and personalized lifestyle recommendations, addressing the significant health risks associated with diabetes, including increased morbidity and mortality. This study highlights the promising role of AI in revolutionizing diabetes management, advocating for its expanded use in healthcare settings to improve patient outcomes and optimize treatment efficacy.
