Publications by authors named "Iat Fan Lai"
Article Synopsis
- EE-Explorer is an AI system designed to triage eye emergencies and assist with primary diagnosis using metadata and images taken from smartphones.
- It was developed through a validation study involving data from over 4,000 patients and was tested across various hospitals to assess its accuracy in classifying urgency levels and diagnosing conditions.
- The results demonstrated high accuracy, outperforming human triage nurses, and indicated strong performance in both triage and diagnostic capabilities, suggesting its potential to improve access to care for patients with eye emergencies.
Application of Artificial Intelligence in Precision Medicine for Diabetic Macular Edema.
Asia Pac J Ophthalmol (Phila)
February 2024
Diabetic macular edema (DME) is the primary cause of central vision impairment in patients with diabetes and the leading cause of preventable blindness in working-age people. With the advent of optical coherence tomography and antivascular endothelial growth factor (anti-VEGF) therapy, the diagnosis, evaluation, and treatment of DME were greatly revolutionized in the last decade. However, there is tremendous heterogeneity among DME patients, and 30%-50% of DME patients do not respond well to anti-VEGF agents.
View Article and Find Full Text PDFThe storage of facial images in medical records poses privacy risks due to the sensitive nature of the personal biometric information that can be extracted from such images. To minimize these risks, we developed a new technology, called the digital mask (DM), which is based on three-dimensional reconstruction and deep-learning algorithms to irreversibly erase identifiable features, while retaining disease-relevant features needed for diagnosis. In a prospective clinical study to evaluate the technology for diagnosis of ocular conditions, we found very high diagnostic consistency between the use of original and reconstructed facial videos (κ ≥ 0.
View Article and Find Full Text PDFArticle Synopsis
- Deep-learning models can enhance early detection of chronic diseases like chronic kidney disease and type 2 diabetes using retinal images and basic health data.
- The models showed strong performance, with accuracy scores ranging from 0.85 to 0.93, based on over 115,000 retinal images from nearly 58,000 patients.
- They can also predict key health metrics and assess disease progression risks, proving effective even with images taken by smartphones in diverse populations.
Anterior segment eye diseases account for a significant proportion of presentations to eye clinics worldwide, including diseases associated with corneal pathologies, anterior chamber abnormalities (e.g. blood or inflammation), and lens diseases.
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