Publications by authors named "Vivien W M Tsui"
Article Synopsis
- Hepatocellular carcinoma (HCC) has a high mortality rate, and current diagnostic methods like LI-RADS often lead to indeterminate results, complicating accurate diagnosis.
- Researchers developed four deep learning models using CT scans, finding that the Spatio-Temporal 3D Convolution Network (ST3DCN) performed best, significantly outperforming standard radiological interpretation in identifying HCC.
- The ST3DCN model demonstrated strong diagnostic accuracy in both internal validation (AUCs up to 0.919) and external testing (AUC of 0.901), indicating its potential as an effective tool for HCC diagnosis.
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.
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.
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.
View Article and Find Full Text PDFThe 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.
View Article and Find Full Text PDFBackground 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.
Article Synopsis
- A study found that sometimes doctors can miss up to 26% of growths called adenomas during a procedure called colonoscopy, where they check the colon for problems.
- Researchers tested a special AI technology that helps doctors find these missed growths by reviewing videos of colon exams, and it could spot around 79% of the missed adenomas in one test.
- The AI was used in real procedures, finding missed adenomas in about 27% of patients, suggesting that using AI could help doctors be more careful and catch more of these growths.
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.
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