Assessment of Helicobacter pylori infection by deep learning based on endoscopic videos in real time.

Background And Aims: Endoscopic assessment of Helicobacter pylori infection is a simple and effective method. Here, we aimed to develop a deep learning-based system named Intelligent Detection Endoscopic Assistant-Helicobacter pylori (IDEA-HP) to assess H. pylori infection by using endoscopic videos in real time.

Correlation of the detection rate of upper GI cancer with artificial intelligence score: results from a multicenter trial (with video).

Background And Aims: The quality of EGD is a prerequisite for a high detection rate of upper GI lesions, especially early gastric cancer. Our previous study showed that an artificial intelligence system, named intelligent detection endoscopic assistant (IDEA), could help to monitor blind spots and provide an operation score during EGD. Here, we verified the effectiveness of IDEA to help evaluate the quality of EGD in a large-scale multicenter trial.

A novel model for predicting fatty liver disease by means of an artificial neural network.

Background: The artificial neural network (ANN) emerged recently as a potent diagnostic tool, especially for complicated systemic diseases. This study aimed to establish a diagnostic model for the recognition of fatty liver disease (FLD) by virtue of the ANN.

Methods: A total of 7,396 pairs of gender- and age-matched subjects who underwent health check-ups at the First Affiliated Hospital, College of Medicine, Zhejiang University (Hangzhou, China) were enrolled to establish the ANN model.

Intelligent detection endoscopic assistant: An artificial intelligence-based system for monitoring blind spots during esophagogastroduodenoscopy in real-time.

Background: Observation of the entire stomach during esophagogastroduodenoscopy (EGD) is important; however, there is a lack of effective evaluation tools.

Aims: To develop an artificial intelligence (AI)-assisted EGD system able to automatically monitor blind spots in real-time.

Methods: An AI-based system, called the Intelligent Detection Endoscopic Assistant (IDEA), was developed using a deep convolutional neural network (DCNN) and long short-term memory (LSTM).

Successful treatment of multi-drug resistant tuberculosis by double lung transplantation.

A 24-year-old man suffering from end-stage, multi-drug, resistant tuberculosis and right heart failure underwent bilateral, single-lung transplantation with extracorporeal membrane oxygenator support. He was successfully weaned and discharged from the hospital 30 days later. Sputum cultures have been negative for tuberculosis since discharge, and for almost 2 years thereafter.