A deep learning system for detecting silent brain infarction and predicting stroke risk.

Current brain imaging to detect silent brain infarctions (SBIs) is not feasible for the general population. Here, to overcome this challenge, we developed a retinal image-based deep learning system, DeepRETStroke, to detect SBI and refine stroke risk. We use 895,640 retinal photographs to pretrain the DeepRETStroke system, which encodes a domain-specific foundation model for representing eye-brain connections.

Vision transformer-based stratification of pre/diabetic and pre/hypertensive patients from retinal photographs for 3PM applications.

Objective: Diabetes and hypertension pose significant health risks, especially when poorly managed. Retinal evaluation though fundus photography can provide non-invasive assessment of these diseases, yet prior studies focused on disease presence, overlooking control statuses. This study evaluated vision transformer (ViT)-based models for assessing the presence and control statuses of diabetes and hypertension from retinal images.

A deep-learning retinal aging biomarker for cognitive decline and incident dementia.

Introduction: The utility of retinal photography-derived aging biomarkers for predicting cognitive decline remains under-explored.

Methods: A memory-clinic cohort in Singapore was followed-up for 5 years. RetiPhenoAge, a retinal aging biomarker, was derived from retinal photographs using deep-learning.

Comparative Analysis of Vision Transformers and Conventional Convolutional Neural Networks in Detecting Referable Diabetic Retinopathy.

Objective: Vision transformers (ViTs) have shown promising performance in various classification tasks previously dominated by convolutional neural networks (CNNs). However, the performance of ViTs in referable diabetic retinopathy (DR) detection is relatively underexplored. In this study, using retinal photographs, we evaluated the comparative performances of ViTs and CNNs on detection of referable DR.

A deep learning system for predicting time to progression of diabetic retinopathy.

Diabetic retinopathy (DR) is the leading cause of preventable blindness worldwide. The risk of DR progression is highly variable among different individuals, making it difficult to predict risk and personalize screening intervals. We developed and validated a deep learning system (DeepDR Plus) to predict time to DR progression within 5 years solely from fundus images.

A deep network DeepOpacityNet for detection of cataracts from color fundus photographs.

Background: Cataract diagnosis typically requires in-person evaluation by an ophthalmologist. However, color fundus photography (CFP) is widely performed outside ophthalmology clinics, which could be exploited to increase the accessibility of cataract screening by automated detection.

Methods: DeepOpacityNet was developed to detect cataracts from CFP and highlight the most relevant CFP features associated with cataracts.

Use of artificial intelligence in forecasting glaucoma progression.

Artificial intelligence (AI) has been widely used in ophthalmology for disease detection and monitoring progression. For glaucoma research, AI has been used to understand progression patterns and forecast disease trajectory based on analysis of clinical and imaging data. Techniques such as machine learning, natural language processing, and deep learning have been employed for this purpose.

Predictive, preventive, and personalized management of retinal fluid via computer-aided detection app for optical coherence tomography scans.

Aims: Computer-aided detection systems for retinal fluid could be beneficial for disease monitoring and management by chronic age-related macular degeneration (AMD) and diabetic retinopathy (DR) patients, to assist in disease prevention via early detection before the disease progresses to a "wet AMD" pathology or diabetic macular edema (DME), requiring treatment. We propose a proof-of-concept AI-based app to help predict fluid via a "fluid score", prevent fluid progression, and provide personalized, serial monitoring, in the context of predictive, preventive, and personalized medicine (PPPM) for patients at risk of retinal fluid complications.

Methods: The app comprises a convolutional neural network-Vision Transformer (CNN-ViT)-based segmentation deep learning (DL) network, trained on a small dataset of 100 training images (augmented to 992 images) from the Singapore Epidemiology of Eye Diseases (SEED) study, together with a CNN-based classification network trained on 8497 images, that can detect fluid vs.

Gender Prediction for a Multiethnic Population via Deep Learning Across Different Retinal Fundus Photograph Fields: Retrospective Cross-sectional Study.

Background: Deep learning algorithms have been built for the detection of systemic and eye diseases based on fundus photographs. The retina possesses features that can be affected by gender differences, and the extent to which these features are captured via photography differs depending on the retinal image field.

Objective: We aimed to compare deep learning algorithms' performance in predicting gender based on different fields of fundus photographs (optic disc-centered, macula-centered, and peripheral fields).

Effect of microneedle geometry and supporting substrate on microneedle array penetration into skin.

Microneedles are being fast recognized as a useful alternative to injections in delivering drugs, vaccines, and cosmetics transdermally. Owing to skin's inherent elastic properties, microneedles require an optimal geometry for skin penetration. In vitro studies, using rat skin to characterize microneedle penetration in vivo, require substrates with suitable mechanical properties to mimic human skin's subcutaneous tissues.