Background Adequate pain control after cardiac surgery is essential. Paravertebral block is a simple technique and avoids the potential complications of epidural catheters. The objective of this study is to compare the effect of ultrasound-guided bilateral thoracic paravertebral block with thoracic epidural block on pain control after cardiac surgery. Materials and Methods Between March 2016 and 2017, 145 patients who had cardiac surgery through median sternotomy were randomized by stratified blocked randomization into two groups. Group I (n = 70 patients) had bilateral ultrasound-guided thoracic paravertebral block and Group II (n = 75 patients) had thoracic epidural analgesia. The primary end point was the postoperative visual analogue scale (VAS). The duration of mechanical ventilation, intensive care unit (ICU), and hospital stay were the secondary end points. The study design is a randomized parallel superiority clinical trial. Results Both groups had similar preoperative and operative characteristics. No significant difference in VAS measured immediately after endotracheal extubation then after 12, 24, and 48 hours between groups (p = 0.45). Pain score significantly declined with the repeated measures (p < 0.001) and the decline was not related to the treatment group. Postoperative pain was significantly related to diabetes mellitus (p = 0.039). Six patients in group I (8.5%) required an additional dose of morphine versus three patients (4%) in group II (p = 0.30). Patients in group I had significantly shorter ICU stay (p = 0.005) and lower incidence of urinary retention (p = 0.04) and vomiting (p = 0.018). No difference was found in operative complications between groups. Conclusion This randomized parallel controlled trial demonstrates that ultrasound-guided paravertebral block is safe and effective method for relieving post-cardiac surgery sternotomy pain compared with thoracic epidural analgesia but not superior to it.

Purpose: This review aims to explore the clinical and research applications of artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), in understanding, predicting, and managing obesity. It assesses the use of AI tools to identify obesity-related risk factors, predict outcomes, personalize treatments, and improve healthcare interventions for obesity. Methods: A comprehensive literature search was conducted using PubMed and Google Scholar, with keywords including “artificial intelligence”, “machine learning”, “deep learning”, “obesity”, “obesity management”, and related terms. Studies focusing on AI’s role in obesity research, management, and therapeutic interventions were reviewed, including observational studies, systematic reviews, and clinical applications. Results: This review identifies numerous AI-driven models, such as ML and DL, used in obesity prediction, patient stratification, and personalized management strategies. Applications of AI in obesity research include risk prediction, early detection, and individualization of treatment plans. AI has facilitated the development of predictive models utilizing various data sources, such as genetic, epigenetic, and clinical data. However, AI models vary in effectiveness, influenced by dataset type, research goals, and model interpretability. Performance metrics such as accuracy, precision, recall, and F1-score were evaluated to optimize model selection. Conclusions: AI offers promising advancements in obesity management, enabling more personalized and efficient care. While technology presents considerable potential, challenges such as data quality, ethical considerations, and technical requirements remain. Addressing these will be essential to fully harness AI’s potential in obesity research and treatment, supporting a shift toward precision healthcare.