Deep learning-based anatomical position recognition for gastroscopic examination.

Background: The gastroscopic examination is a preferred method for the detection of upper gastrointestinal lesions. However, gastroscopic examination has high requirements for doctors, especially for the strict position and quantity of the archived images. These requirements are challenging for the education and training of junior doctors.

Evaluation of deep learning methods for early gastric cancer detection using gastroscopic images.

Background: A timely diagnosis of early gastric cancer (EGC) can greatly reduce the death rate of patients. However, the manual detection of EGC is a costly and low-accuracy task. The artificial intelligence (AI) method based on deep learning is considered as a potential method to detect EGC.

Influence of Sedation on the Detection Rate of Early Cancer and Precancerous Lesions During Diagnostic Upper Gastrointestinal Endoscopies: A Multicenter Retrospective Study.

Introduction: The influence of sedation on the endoscopic detection rate of upper gastrointestinal (UGI) early cancer (EC) and precancerous lesions, including high-grade intraepithelial neoplasia (HGIN) and low-grade intraepithelial neoplasia, has not been assessed. The aim of this research is to assess whether the use of sedation can help improve the detection rate of UGI EC and precancerous lesions. The second objective is to evaluate its potential influencing factors.

Incidence and Risk Factors for Liver Abscess After Thermal Ablation of Liver Neoplasm.

Background: Radiofrequency ablation (RFA) and microwave ablation (MWA) are the most frequently used thermal ablation methods for the treatment of liver cancer. Liver abscess is a common and severe complication of thermal ablation treatment.

Objectives: The objective of this study was to determine the incidence and risk factors of liver abscess formation after thermal ablation of liver cancer.