Endocuff With or Without Artificial Intelligence-Assisted Colonoscopy in Detection of Colorectal Adenoma: A Randomized Colonoscopy Trial.

Introduction: Both artificial intelligence (AI) and distal attachment devices have been shown to improve adenoma detection rate and reduce miss rate during colonoscopy. We studied the combined effect of Endocuff and AI on enhancing detection rates of various colonic lesions.

Methods: This was a 3-arm prospective randomized colonoscopy study involving patients aged 40 years or older.

Artificial intelligence-assisted real-time monitoring of effective withdrawal time during colonoscopy: a novel quality marker of colonoscopy.

Background And Aims: The importance of withdrawal time during colonoscopy cannot be overstated in mitigating the risk of missed lesions and postcolonoscopy colorectal cancer. We evaluated a novel colonoscopy quality metric called the effective withdrawal time (EWT), which is an artificial intelligence (AI)-derived quantitative measure of quality withdrawal time, and its association with various colonic lesion detection rates as compared with standard withdrawal time (SWT).

Methods: Three hundred fifty video recordings of colonoscopy withdrawal (from the cecum to the anus) were assessed by the new AI model.

Blue-light imaging or narrow-band imaging for proximal colonic lesions: a prospective randomized tandem colonoscopy study.

Background And Aims: Blue-light imaging (BLI) is a new image-enhanced endoscopy with a wavelength filter similar to narrow-band imaging (NBI). We compared the 2 with white-light imaging (WLI) on proximal colonic lesion detection and miss rates.

Methods: In this 3-arm prospective randomized study with tandem examination of the proximal colon, we enrolled patients aged ≥40 years.

Computer-assisted detection versus conventional colonoscopy for proximal colonic lesions: a multicenter, randomized, tandem-colonoscopy study.

Background And Aims: Computer-assisted detection (CADe) is a promising technologic advance that enhances adenoma detection during colonoscopy. However, the role of CADe in reducing missed colonic lesions is uncertain. The aim of this study was to determine the miss rates of proximal colonic lesions by CADe and conventional colonoscopy.

Machine learning models in the prediction of 1-year mortality in patients with advanced hepatocellular cancer on immunotherapy: a proof-of-concept study.

Introduction: Immunotherapy is a new promising treatment for patients with advanced hepatocellular carcinoma (HCC), but is costly and potentially associated with considerable side effects. This study aimed to evaluate the role of machine learning (ML) models in predicting the 1-year cancer-related mortality in advanced HCC patients treated with immunotherapy.

Method: 395 HCC patients who had received immunotherapy (including nivolumab, pembrolizumab or ipilimumab) between 2014 and 2019 in Hong Kong were included.

Feasibility study of a single-use balloon-assisted robotic colonoscope in healthy volunteers.

Despite its widespread adoption, colonoscope still has its limitations. Advancement is often limited by the looping of colon. The isolation of SARS-CoV-2 in stool raises concern for the risk of disease transmission.

Impact of first wave of COVID-19 on outcomes of hospitalization for upper gastrointestinal bleeding in Hong Kong: a population-based study.

 The COVID-19 pandemic has caused a major disruption in the healthcare system. This study determined the impact of the first wave of COVID-19 on the number and outcome of patients hospitalized for upper gastrointestinal bleeding (UGIB) in Hong Kong.  Records of all patients hospitalized for UGIB in Hong Kong public hospitals between October 2018 and June 2020 were retrieved.

Is artificial intelligence the final answer to missed polyps in colonoscopy?

Lesions missed by colonoscopy are one of the main reasons for post-colonoscopy colorectal cancer, which is usually associated with a worse prognosis. Because the adenoma miss rate could be as high as 26%, it has been noted that endoscopists with higher adenoma detection rates are usually associated with lower adenoma miss rates. Artificial intelligence (AI), particularly the deep learning model, is a promising innovation in colonoscopy.

Accuracy of artificial intelligence-assisted detection of upper GI lesions: a systematic review and meta-analysis.

Background And Aims: Artificial intelligence (AI)-assisted detection is increasingly used in upper endoscopy. We performed a meta-analysis to determine the diagnostic accuracy of AI on detection of gastric and esophageal neoplastic lesions and Helicobacter pylori (HP) status.

Methods: We searched Embase, PubMed, Medline, Web of Science, and Cochrane databases for studies on AI detection of gastric or esophageal neoplastic lesions and HP status.

Accuracy of artificial intelligence on histology prediction and detection of colorectal polyps: a systematic review and meta-analysis.

Background And Aims: We performed a meta-analysis of all published studies to determine the diagnostic accuracy of artificial intelligence (AI) on histology prediction and detection of colorectal polyps.

Method: We searched Embase, PubMed, Medline, Web of Science, and Cochrane library databases to identify studies using AI for colorectal polyp histology prediction and detection. The quality of included studies was measured by the Quality Assessment of Diagnostic Accuracy Studies tool.

Feedback from artificial intelligence improved the learning of junior endoscopists on histology prediction of gastric lesions.

Artificial intelligence (AI)-assisted image classification has been shown to have high accuracy on endoscopic diagnosis. We evaluated the potential effects of use of an AI-assisted image classifier on training of junior endoscopists for histological prediction of gastric lesions. An AI image classifier was built on a convolutional neural network with five convolutional layers and three fully connected layers A Resnet backbone was trained by 2,000 non-magnified endoscopic gastric images.

Linked color imaging versus narrow-band imaging for colorectal polyp detection: a prospective randomized tandem colonoscopy study.

Background And Aims: Linked color imaging (LCI) is a newly available image-enhanced endoscopy (IEE) system that emphasizes the red mucosal color. No study has yet compared LCI with other available IEE systems. Our aim was to investigate polyp detection rates using LCI compared with narrow-band imaging (NBI).

Territory-wide population-based study of chronic hepatitis C infection and implications for hepatitis elimination in Hong Kong.

Background: To study the epidemiology of chronic hepatitis C virus infection in Hong Kong and to estimate the service gap for achieving the WHO hepatitis elimination targets of attaining a diagnosis rate of 90%, treatment rate of 80% and 65% reduction in mortality rate by 2030.

Methods: From January 2005 to March 2017, patients who were tested positive for anti-HCV were retrospectively retrieved from all public hospitals in Hong Kong. The epidemiological data of 15 participating hospitals were analysed.

Novel structural co-expression analysis linking the NPM1-associated ribosomal biogenesis network to chronic myelogenous leukemia.

Co-expression analysis reveals useful dysregulation patterns of gene cooperativeness for understanding cancer biology and identifying new targets for treatment. We developed a structural strategy to identify co-expressed gene networks that are important for chronic myelogenous leukemia (CML). This strategy compared the distributions of expressional correlations between CML and normal states, and it identified a data-driven threshold to classify strongly co-expressed networks that had the best coherence with CML.