Purpose: Results of initial endoscopic biopsy of gastric lesions often differ from those of the final pathological diagnosis. We evaluated whether an artificial intelligence-based gastric lesion detection and diagnostic system, ENdoscopy as AI-powered Device Computer Aided Diagnosis for Gastroscopy (ENAD CAD-G), could reduce this discrepancy. Materials and Methods: We retrospectively collected 24,948 endoscopic images of early gastric cancers (EGCs), dysplasia, and benign lesions from 9,892 patients who underwent esophagogastroduodenoscopy between 2011 and 2021. The diagnostic performance of ENAD CAD-G was evaluated using the following real-world datasets: patients referred from community clinics with initial biopsy results of atypia (n=154), participants who underwent endoscopic resection for neoplasms (Internal video set, n=140), and participants who underwent endoscopy for screening or suspicion of gastric neoplasm referred from community clinics (External video set, n=296). Results: ENAD CAD-G classified the referred gastric lesions of atypia into EGC (accuracy, 82.47%; 95% confidence interval [CI], 76.46%–88.47%), dysplasia (88.31%; 83.24%– 93.39%), and benign lesions (83.12%; 77.20%–89.03%). In the Internal video set, ENAD CAD-G identified dysplasia and EGC with diagnostic accuracies of 88.57% (95% CI, 83.30%– 93.84%) and 91.43% (86.79%–96.07%), respectively, compared with an accuracy of 60.71% (52.62%–68.80%) for the initial biopsy results (P<0.001). In the External video set, ENAD CAD-G classified EGC, dysplasia, and benign lesions with diagnostic accuracies of 87.50% (83.73%–91.27%), 90.54% (87.21%–93.87%), and 88.85% (85.27%–92.44%), respectively. Conclusions ENAD CAD-G is superior to initial biopsy for the detection and diagnosis of gastric lesions that require endoscopic resection. ENAD CAD-G can assist community endoscopists in identifying gastric lesions that require endoscopic resection.

Previously, we reported the concept of a cloud-based telemedicine platform for patients with intracerebral hemorrhage (ICH) at local emergency rooms in rural and medically underserved areas in Gangwon state by combining artificial intelligence and remote consultation with a neurosurgeon. Developing a telemedicine ICH treatment protocol exclusively for doctors with less ICH expertise working in emergency rooms should be part of establishing this system. Difficulties arise in providing appropriate early treatment for ICH in rural and underserved areas before the patient is transferred to a nearby hub hospital with stroke specialists. This has been an unmet medical need for decade. The available reporting ICH guidelines are realistically possible in university hospitals with a well-equipped infrastructure. However, it is very difficult for doctors inexperienced with ICH treatment to appropriately select and deliver ICH treatment based on the guidelines. To address these issues, we developed an ICH telemedicine protocol. Neurosurgeons from four university hospitals in Gangwon state first wrote the guidelines, and professors with extensive ICH expertise across the country revised them. Guidelines and recommendations for ICH management were described as simply as possible to allow more doctors to use them easily. We hope that our effort in developing the telemedicine protocols will ultimately improve the quality of ICH treatment in local emergency rooms in rural and underserved areas in Gangwon state.

Purpose: This study aims to analyze the learning curve of hand-assisted laparoscopic living donor nephrectomy (HLDN) conducted by a trained gastrointestinal surgeon. Methods: A retrospective analysis was performed on the perioperative clinical data of 96 consecutive patients who underwent HLDN from May 2013 to March 2023. The learning curve was evaluated using the cumulative sum (CUSUM) test based on operation time and risk-adjusted CUSUM for postoperative complications. Patients were divided into three groups (novice, development, and competency phases) based on changes in operation time. Patient demographics and perioperative outcomes were compared between each group. Results: Among the patients, 35 were male, with a mean age of 48.9 ± 11.3 years and a mean body mass index (BMI) of 24.5 ± 3.2 kg/m 2 . The novice phase (phase 1) included the first 30 cases, with the development phase (phase 2) up to the 65th case. Operation times were significantly different across phases, averaging 263.2 ± 33.4, 211.1 ± 34.4, and 161.1 ± 31.3 minutes for phases 1, 2, and 3, respectively (P < 0.001). Blood loss decreased gradually across phases (phase 1, 264.7 ± 144.4 mL; phase 2, 239.7 ± 166.3 mL; phase 3, 198.8 ± 103.5 mL), though not statistically significant. BMI impacted operation time only in phase 1. Overall postoperative complications occurred in 13 cases (Clavien-Dindo grade I, 4 cases;grade II, 9 cases), with no significant differences across phases. Conclusion HLDN can be safely performed by a trained gastrointestinal surgeon, with approximately 30 cases needed to achieve proficiency.

Background: Atherogenic dyslipidemia, which is frequently associated with type 2 diabetes (T2D) and insulin resistance, contributes to the development of vascular complications. Statin therapy is the primary approach to dyslipidemia management in T2D, however, the role of non-statin therapy remains unclear. Ezetimibe reduces cholesterol burden by inhibiting intestinal cholesterol absorption. Fibrates lower triglyceride levels and increase high-density lipoprotein cholesterol (HDL-C) levels via peroxisome proliferator- activated receptor alpha agonism. Therefore, when combined, these drugs effectively lower non-HDL-C levels. Despite this, few clinical trials have specifically targeted non-HDL-C, and the efficacy of triple combination therapies, including statins, ezetimibe, and fibrates, has yet to be determined. Methods: This is a multicenter, prospective, randomized, open-label, active-comparator controlled trial involving 3,958 eligible participants with T2D, cardiovascular risk factors, and elevated non-HDL-C (≥100 mg/dL). Participants, already on moderate-intensity statins, will be randomly assigned to either Ezefeno (ezetimibe/fenofibrate) addition or statin dose-escalation. The primary end point is the development of a composite of major adverse cardiovascular and diabetic microvascular events over 48 months. Conclusion This trial aims to assess whether combining statins, ezetimibe, and fenofibrate is as effective as, or possibly superior to, statin monotherapy intensification in lowering cardiovascular and microvascular disease risk for patients with T2D. This could propose a novel therapeutic approach for managing dyslipidemia in T2D.