An eyecare foundation model for clinical assistance: a randomized controlled trial.

In the context of an increasing need for clinical assessments of foundation models, we developed EyeFM, a multimodal vision-language eyecare copilot, and conducted a multifaceted evaluation, including retrospective validations, multicountry efficacy validation as a clinical copilot and a double-masked randomized controlled trial (RCT). EyeFM was pretrained on 14.5 million ocular images from five imaging modalities paired with clinical texts from global, multiethnic datasets.

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.

Predictive Potential of Retina-Based Biological Age in Assessing Chronic Obstructive Pulmonary Disease Risk.

Background: Previously, based on retinal photographs, we developed a deep-learning algorithm to predict biological age (termed, RetiAGE) that was associated with future risks of morbidity and mortality. This study specifically aimed to evaluate the performance of RetiAGE in predicting future risks of chronic obstructive pulmonary disease (COPD).

Methods: RetiAGE scores were generated from retinal images in the UK Biobank and stratified into tertiles.

Associations of Metabolically Healthy Obesity and Retinal Age Gap.

Purpose: We investigated the association between metabolically healthy obesity (MHO) and retinal age gap and explored potential sex differences in this association.

Methods: This study included 30,335 participants from the UK Biobank. Body mass index (BMI) was classified into normal weight, overweight, and obesity.

Prognostic potentials of AI in ophthalmology: systemic disease forecasting via retinal imaging.

Background: Artificial intelligence (AI) that utilizes deep learning (DL) has potential for systemic disease prediction using retinal imaging. The retina's unique features enable non-invasive visualization of the central nervous system and microvascular circulation, aiding early detection and personalized treatment plans for personalized care. This review explores the value of retinal assessment, AI-based retinal biomarkers, and the importance of longitudinal prediction models in personalized care.

Validation of a deep-learning-based retinal biomarker (Reti-CVD) in the prediction of cardiovascular disease: data from UK Biobank.

Background: Currently in the United Kingdom, cardiovascular disease (CVD) risk assessment is based on the QRISK3 score, in which 10% 10-year CVD risk indicates clinical intervention. However, this benchmark has limited efficacy in clinical practice and the need for a more simple, non-invasive risk stratification tool is necessary. Retinal photography is becoming increasingly acceptable as a non-invasive imaging tool for CVD.

Detection of Systemic Diseases From Ocular Images Using Artificial Intelligence: A Systematic Review.

Purpose: Despite the huge investment in health care, there is still a lack of precise and easily accessible screening systems. With proven associations to many systemic diseases, the eye could potentially provide a credible perspective as a novel screening tool. This systematic review aims to summarize the current applications of ocular image-based artificial intelligence on the detection of systemic diseases and suggest future trends for systemic disease screening.

Clinical Relevance of Body Fluid Volume Status in Diabetic Patients With Macular Edema.

Objective: To investigate body fluid status in diabetic macular edema (DME) patients and the extent to which it is affected by renal function.

Methods: One hundred and thirty-two eyes from 132 patients with diabetes mellitus (DM) were prospectively collected in this cross-sectional, observational study. Thirty-five were DM patients without diabetic retinopathy (DR), 31 were DR patients without DME, and 66 were DME patients.

Retinal Microvasculature Changes in Patients With Coronary Total Occlusion on Optical Coherence Tomography Angiography.

Retinal microvasculature has been associated with coronary artery disease (CAD), but the exact contributory role in coronary total occlusion (CTO) is unclear. We aimed to investigate whether retinal vasculature is associated with CTO and could provide incremental value in the assessment of CTO. A total of 218 CAD patients including 102 CTO and 116 non-CTO were enrolled.

Improved Retinal Microcirculation After Cardiac Surgery in Patients With Congenital Heart Disease.

Microcirculatory changes in congenital heart disease (CHD) patients undergoing cardiac surgery are not fully understood. We aimed to investigate the changes of retinal microcirculation in CHD patients after cardiac surgery by optical coherence tomography angiography (OCTA) and explore the association between retinal microcirculation and surgical outcome. This prospective observational study consisted of 71 CHD patients aged ≥6 years undergoing cardiac surgery including 19 cyanotic CHD (CCHD) and 52 acyanotic CHD (ACHD).

Epigenetic inhibition assisted chemotherapeutic treatment of lung cancer based on artificial exosomes.

We adopted a novel strategy by combining histone deacetylase (HDAC) inhibitors with traditional chemotherapeutics to treat solid tumors. However, chemotherapeutics often have a narrow therapeutic index and need multiple administrations with undesired side effects that lead to the intolerance. To reduce the non-specificity of chemotherapeutics, targeted therapy was introduced to restrict such agents in the tumor with minimum effects on other tissues.

Hybrid artificial cell-mediated epigenetic inhibition in metastatic lung cancer.

Hypothesis: Histone deacetylase inhibitors (HDACIs), such as vorinostat (suberoylanilide hydroxamic acid, SAHA), has become a promising approach for the treatment of metastatic lung cancer. However, HDACIs usually showed a short circulation lifetime, low specificity, and low bioavailability, which limited their therapeutic effect in this field. We supposed that the use of biomimetic nanoparticles enabled to overcome the disadvantages of HDACIs, and improved the inhibition of metastatic lung cancer.

Development and Validation of Retinal Vasculature Nomogram in Suspected Angina Due to Coronary Artery Disease.

Aims: To develop and validate a nomogram using retinal vasculature features and clinical variables to predict coronary artery disease (CAD) in patients with suspected angina.

Methods: The prediction model consisting of 795 participants was developed in a training set of 508 participants with suspected angina due to CAD, and data were collected from January 2018 to June 2019. The held-out validation was conducted with 287 consecutive patients from July 2019 to November 2019.

Retinal microvasculature impairments in patients with coronary artery disease: An optical coherence tomography angiography study.

Purpose: To investigate the association between retinal microvasculature and the presence and severity of coronary artery disease (CAD) using optical coherence tomography angiography (OCTA).

Methods: The cross-sectional study was conducted in Guangdong Provincial People's Hospital, China. Retinal microvasculature parameters were measured by OCTA of the optic disc, including the vessel density (VD) and retinal nerve fibre thickness of the radial peripapillary capillary.

Automatic prediction of treatment outcomes in patients with diabetic macular edema using ensemble machine learning.

Background: This study aimed to predict the treatment outcomes in patients with diabetic macular edema (DME) after 3 monthly anti-vascular endothelial growth factor (VEGF) injections using machine learning (ML) based on pretreatment optical coherence tomography (OCT) images and clinical variables.

Methods: An ensemble ML system consisting of four deep learning (DL) models and five classical machine learning (CML) models was developed to predict the posttreatment central foveal thickness (CFT) and the best-corrected visual acuity (BCVA). A total of 363 OCT images and 7,587 clinical data records from 363 eyes were included in the training set (304 eyes) and external validation set (59 eyes).

High-Performance Dual Combination Therapy for Cancer Treatment with Hybrid Membrane-Camouflaged Mesoporous Silica Gold Nanorods.

Conventional chemotherapy usually induces significant side effects due to its inability to discriminate between cancer and normal cells. Moreover, the efficacy of cancer elimination is still unsatisfied. Here, we fabricated a nanocomposite enabling high-performance dual combination therapy (chemo/photothermal therapy).

DETECTION OF MORPHOLOGIC PATTERNS OF DIABETIC MACULAR EDEMA USING A DEEP LEARNING APPROACH BASED ON OPTICAL COHERENCE TOMOGRAPHY IMAGES.

Purpose: To develop a deep learning (DL) model to detect morphologic patterns of diabetic macular edema (DME) based on optical coherence tomography (OCT) images.

Methods: In the training set, 12,365 OCT images were extracted from a public data set and an ophthalmic center. A total of 656 OCT images were extracted from another ophthalmic center for external validation.

Retinal microvasculature impairment in patients with congenital heart disease investigated by optical coherence tomography angiography.

Importance: A high prevalence of retinal abnormalities have been reported in congenital heart disease (CHD), but quantitative analysis of retinal vasculature is scarce. Optical coherence tomography angiography (OCTA) is a noninvasive method to quantitatively assess the retinal microvasculature.

Background: To investigate the retinal microvasculature changes in CHD patients by using OCTA.

Fully Automatic Arteriovenous Segmentation in Retinal Images via Topology-Aware Generative Adversarial Networks.

Retinal image contains rich information on the blood vessel and is highly related to vascular diseases. Fully automatic and accurate identification of arteries and veins from the complex background of retinal images is essential for analyzing eye-relevant diseases, and monitoring progressive eye diseases. However, popular methods, including deep learning-based models, performed unsatisfactorily in preserving the connectivity of both the arteries and veins.

Retinal Neurovascular Impairment in Patients with Essential Hypertension: An Optical Coherence Tomography Angiography Study.

Purpose: To investigate retinal neurovascular structural changes in patients with essential hypertension.

Methods: This observational cross-sectional study consisted of 199 right eyes from 169 nondiabetic essential hypertensive patients, divided into groups as follows: group A, 113 patients with hypertensive retinopathy (HTNR); group B, 56 patients without HTNR; and a control group of 30 healthy subjects. Peripapillary retinal nerve fiber layer (RNFL), radial peripapillary segmented (RPC), ganglion cell-inner plexiform layer (GC-IPL), and superficial (SVP) and deep (DVP) vascular plexus density at the macula (6 × 6 mm2) were measured by optical coherence tomography angiography (OCTA).