Publications by authors named "Yiu-Lun Wong"
Article Synopsis
- The study develops and tests deep learning models to assess the quality of 3D macular scans from two optical coherence tomography devices, Cirrus and Spectralis.
- Researchers collected and analyzed 3D scans from over 3,800 patients, and utilized a specialized deep learning network to classify scans as gradable or ungradable.
- The models demonstrated high accuracy in internal validation and external testing, indicating they could effectively filter out low-quality scans and be integrated with disease detection systems for automated eye disease diagnosis.
Background: Cardiovascular disease is a leading cause of global death. Prospective population-based studies have found that changes in retinal microvasculature are associated with the development of coronary artery disease. Recently, artificial intelligence deep learning (DL) algorithms have been developed for the fully automated assessment of retinal vessel calibres.
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- Diabetic macular edema (DME) is a major cause of vision loss in diabetics, and this study assesses how effectively artificial intelligence (AI) can detect DME through fundus photography (FP) and optical coherence tomography (OCT) images.
- The analysis included 53 studies, revealing that AI models based on FP achieved an area under the curve (AUROC) of 0.964 with 92.6% sensitivity, while OCT-based models performed even better with an AUROC of 0.985 and 95.9% sensitivity.
- The study highlights the need for better external validation and larger, more diverse training datasets to enhance AI performance in detecting DME, pointing to factors like deep learning and sample
Background: The acceleration of population aging calls for simple and effective interventions catered for older people. Gerontechnology, the combination of gerontology and technology, can promote quality of life in older adults. However, public health-related events incorporating information communication technology (ICT) for older people have seldom been evaluated.
View Article and Find Full Text PDFProcess Evaluation and Experience Sharing on Utilizing Information Communication Technologies and Digital Games in a Large Community Family Health Event: Hong Kong Jockey Club SMART Family-Link Project.
Front Public Health
May 2021
Information communication technologies (ICT) are increasingly used in health promotion, but integration is challenging and involves complex processes. Large community health promotion events are often held but the experiences and processes have rarely been evaluated and published. No reports have described and systematically evaluated an ICT-supported health promotion event using digital games.
View Article and Find Full Text PDFThis paper reports an integrated solution, called BALSA, for the secondary analysis of next generation sequencing data; it exploits the computational power of GPU and an intricate memory management to give a fast and accurate analysis. From raw reads to variants (including SNPs and Indels), BALSA, using just a single computing node with a commodity GPU board, takes 5.5 h to process 50-fold whole genome sequencing (∼750 million 100 bp paired-end reads), or just 25 min for 210-fold whole exome sequencing.
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