Anatomical recognition artificial intelligence for identifying the recurrent laryngeal nerve during endoscopic thyroid surgery: A single-center feasibility study.

Background: We investigate the feasibility of using artificial intelligence (AI) to identify the recurrent laryngeal nerve (RLN) during endoscopic thyroid surgery and evaluated its accuracy.

Methods: In this retrospective study, we develop an AI model using a dataset of endoscopic thyroid surgery videos, including hemithyroidectomy procedures performed between April 2019 and September 2023 at the National Cancer Center Hospital East, Chiba, Japan. Semantic segmentation deep learning methods were applied to analyze the endoscopic thyroid surgery videos.

Risk of supranormal left ventricular ejection fraction in patients with aortic stenosis.

Background: Cardiovascular events are increasing in patients with supranormal left ventricular ejection fraction (snLVEF). However, the effect of snLVEF in patients with aortic stenosis (AS) remains unclear, especially in patients with moderate AS.

Hypothesis: This study aimed to evaluate the prognosis of mortality and heart failure (HF) in patients with LVEF ≥ 50% and moderate or severe AS.

Clinical value of the fibrosis-4 index in predicting mortality in patients with right ventricular pacing.

Background: The fibrosis-4 (FIB-4) index has attracted attention as a predictive factor for cardiovascular events and mortality in patients with heart disease. However, its clinical value in patients with implanted pacemakers remains unclear.

Methods: This study included patients who underwent pacemaker implantation.

Intraoperative artificial intelligence system identifying liver vessels in laparoscopic liver resection: a retrospective experimental study.

Background: The precise recognition of liver vessels during liver parenchymal dissection is the crucial technique for laparoscopic liver resection (LLR). This retrospective feasibility study aimed to develop artificial intelligence (AI) models to recognize liver vessels in LLR, and to evaluate their accuracy and real-time performance.

Methods: Images from LLR videos were extracted, and the hepatic veins and Glissonean pedicles were labeled separately.

Systemic immune-inflammation index as a predictor of prognosis after carotid artery stenting compared with C-reactive protein.

Background: Immune-inflammatory processes are highly associated with the progression of atherosclerosis. The systemic immune-inflammation index (SII) is a potential predictor for clinical outcomes in patients with stroke and ischemic heart disease. Therefore, this study aimed to investigate whether SII can accurately predict the short- and long-term prognoses in patients who underwent carotid artery stenting (CAS) compared to that with C-reactive protein (CRP).