Background: Coronary heart disease (CHD) is the leading cause of death worldwide, constituting a growing health and social burden. People with cardiometabolic disorders are more likely to develop CHD. Retinal image analysis is a novel and noninvasive method to assess microvascular function. We aim to investigate whether retinal images can be used for CHD risk estimation for people with cardiometabolic disorders. Methods: We have conducted a case–control study at Shenzhen Traditional Chinese Medicine Hospital, where 188 CHD patients and 128 controls with cardiometabolic disorders were recruited. Retinal images were captured within two weeks of admission. The retinal characteristics were estimated by the automatic retinal imaging analysis (ARIA) algorithm. Risk estimation models were established for CHD patients using machine learning approaches. We divided CHD patients into a diabetes group and a non-diabetes group for sensitivity analysis. A ten-fold cross-validation method was used to validate the results. Results: The sensitivity and specificity were 81.3% and 88.3%, respectively, with an accuracy of 85.4% for CHD risk estimation. The risk estimation model for CHD with diabetes performed better than the model for CHD without diabetes. Conclusions: The ARIA algorithm can be used as a risk assessment tool for CHD for people with cardiometabolic disorders.

Objectives: Train an automatic retinal image analysis (ARIA) method to screen glaucomatous optic neuropathy (GON) on non-mydriatic retinal images labelled with the additional results of optical coherence tomography (OCT) and assess different models for the GON classification.Methods: All the images were obtained from the hospital for training and 10-fold cross-validation. Two methods were used to improve the classification performance: (1) using images labelled with the additional results of OCT as the reference standard and (2) generating models using retinal features from the entire images, the region of interest (ROI) of the optic disc, and the ROI of the macula, and the combination of all the features.Results: Overall, we collected 1338 images with paired OCT scans. In 10-fold validation, ARIA achieved sensitivities of 92.2 %, 92.7% and 85.7%, specificities of 88.8%, 86.7% and 80.2% and accuracies of 90.6%, 89.9% and 83.1% using the retinal features from the entire images, the ROI of the optic disc and the ROI of the macula, respectively. We found the model combining all the features has the best classification performance and obtained a sensitivity of 92.5%, a specificity of 92.1% and an accuracy of 92.4%, which is significantly different from other models (p<0.001).Conclusion: We used two methods to improve the classification performance and found the best model to detect glaucoma on colour fundus retinal images. It can become a cost-effective and relatively more accurate glaucoma screening tool than conventional methods.

This study evaluates if there is an association between lifestyle changes and the risk of small vessel disease (SVD) as measured by cerebral white matter hyperintensities (WMH) estimated by the automatic retinal image analysis (ARIA) method. We recruited 274 individuals into a community cohort study. Subjects were assessed at baseline and annually with the Health-Promoting Lifestyle Profile II Questionnaire (HPLP-II) and underwent a simple physical assessment. Retinal images were taken using a non-mydriatic digital fundus camera to evaluate the level of WMH estimated by ARIA (ARIA-WMH) to measure the risk of small vessel disease. We calculated the changes from baseline to one year for the six domains of HPLP-II and analysed the relationship with the ARIA-WMH change. A total of 193 (70%) participants completed both the HPLP-II and ARIA-WMH assessments. The mean age was 59.1 ± 9.4 years, and 76.2% (147) were women. HPLP-II was moderate (Baseline, 138.96 ± 20.93; One-year, 141.97 ± 21.85). We observed a significant difference in ARIA-WMH change between diabetes and non-diabetes subjects (0.03 vs. −0.008, respectively, p = 0.03). A multivariate analysis model showed a significant interaction between the health responsibility (HR) domain and diabetes (p = 0.005). For non-diabetes subgroups, those with improvement in the HR domain had significantly decreased in ARIA-WMH than those without HR improvement (−0.04 vs. 0.02, respectively, p = 0.003). The physical activity domain was negatively related to the change in ARIA-WMH (p = 0.02). In conclusion, this study confirms that there is a significant association between lifestyle changes and ARIA-WMH. Furthermore, increasing health responsibility for non-diabetes subjects reduces the risk of having severe white matter hyperintensities.

Purpose: This study aims to develop a method for detecting referable (intermediate and advanced) age-related macular degeneration (AMD) and neovascular AMD, as well as providing an automatic segmentation of choroidal neovascularisation (CNV) on colour fundus retinal images. We also demonstrated that brain health risk scores estimated by AI-based Retinal Image Analysis (ARIA), such as white matter hyperintensities and depression, are significantly associated with AMD and neovascular AMD. Methods: A primary dataset of 1480 retinal images was collected from Zhongshan Hospital of Fudan University for training and 10-fold cross-validation. Additionally, two validation subdataset comprising 238 images (retinal images and wide-field images) were used. Using fluorescein angiography-based labels, we applied the InceptionResNetV2 deep network with the ARIA method to detect AMD, and a transfer ResNet50_Unet was used to segment CNV. The risks of cerebral white matter hyperintensities and depression were estimated using an AI-based Retinal Image Analysis approach. Results: In a 10-fold cross-validation, we achieved sensitivities of 97.4% and 98.1%, specificities of 96.8% and 96.1%, and accuracies of 97.0% and 96.4% in detecting referable AMD and neovascular AMD, respectively. In the external validation, we achieved accuracies of 92.9% and 93.7% and AUCs of 0.967 and 0.967, respectively. The performances on two validation sub-datasets show no statistically significant difference in detecting referable AMD (p = 0.704) and neovascular AMD (p = 0.213). In the segmentation of CNV, we achieved a global accuracy of 93.03%, a mean accuracy of 91.83%, a mean intersection over union (IoU) of 68.7%, a weighted IoU of 89.63%, and a mean boundary F1 (BF) of 67.77%. Conclusions: The proposed method shows promising results as a highly efficient and cost-effective screening tool for detecting neovascular and referable AMD on both retinal and wide-field images, and providing critical insights into CNV. Its implementation could be particularly valuable in resource-limited settings, enabling timely referrals, enhancing patient care, and supporting decision-making across AMD classifications. In addition, we demonstrated that AMD and neovascular AMD are significantly associated with increased risks of WMH and depression.

Objective: To investigate the effect of Janus kinase (JAK) inhibition by baricitinib on erosion repair in rheumatoid arthritis (RA) patients with active disease using high-resolution peripheral quantitative computer tomography (HR-pQCT). Hypothesis: JAK inhibitor can lead to repair of existing erosion in RA patients with active disease. Design and Subjects: This is a 24-week, randomized, placebo-controlled, double-blind study. We plan to enroll 60 adult patients with active RA (disease activity score 28-C-reactive protein [DAS28-CRP] ¿ 3.2) and at least one bone erosion on HR-pQCT. They will be randomized 1:1 to receive JAK inhibitor (baricitinib 4 mg once daily) or placebo for 24 weeks. Medications will be adjusted according to a standard protocol aiming to achieve low disease activity. Biologic or other targeted synthetic disease-modifying antirheumatic drugs are not permitted. Methodology: HR-pQCT of the 2-4 metacarpophalangeal joints will be done at baseline and 24 weeks. Inflammatory cytokine profile and bone–cartilage interface biomarkers will also be checked at baseline and 24 weeks. Clinical response will be monitored using DAS28-CRP. Main Outcome Measures and Analysis: The primary outcome is the proportion of patients with erosion volume regression on HR-pQCT, comparing the two groups by chi-square test. Secondary outcomes include dimensions of erosion, marginal osteosclerosis, and joint space width on HR-pQCT; RA disease activity; and serum biomarkers. The above-mentioned parameters will be compared before and after treatment by Wilcoxon signed-rank, with their changes compared between the two treatment arms using the Mann–Whitney [Formula: see text] test. Independent factors predicting response to JAK inhibitors will be analyzed by multivariate logistic regression.

Background: Schizophrenia is a debilitating disorder that exacts a heavy burden on sufferers and carers alike. Despite improvements in neuroimaging techniques in the past decades, schizophrenia has lacked a clear definition at the neuroanatomical level. The presence of diagnostic markers may lead to breakthroughs with our current phenomenological approach to schizophrenia, assisting with diagnosis and monitoring of the disease. Prior study findings suggest structural retinal changes in schizophrenia, including retinal-layer thinning and microvascular changes. Aim: We aim to evaluate whether retinal imaging, in combination with machine-learning approaches, can facilitate diagnostic classification of schizophrenia. Furthermore, we characterise retinal structural features in individuals with schizophrenia compared with healthy controls, and examine whether these retinal measures are associated with disease status, clinical severity, functional outcomes, and other key clinical variables. Method: This study recruited 64 individuals with schizophrenia and 64 healthy controls. Participants in the schizophrenia group completed standardized clinical rating scales to assess symptom severity and functional status. Additional clinical variables, including demographic characteristics, duration of untreated psychosis, and antipsychotic medication dosage, were recorded. All participants underwent fundus photography, and retinal images were processed using the Automated Retinal-Imaging Analysis (ARIA) system developed at the Chinese University of Hong Kong (CUHK). Extracted retinal parameters were used to train machine-learning models to classify schizophrenia cases versus controls. Using a cross-sectional design, we compared retinal characteristics between groups while adjusting for relevant confounders, and evaluated associations between retinal measures and clinical variables within the schizophrenia cohort. Results: Our machine-learning model successfully distinguished individuals with schizophrenia from healthy controls, achieving high sensitivity (96.9%) and specificity (92.2%). Consistent with prior literature, we observed reductions in some measures of retinal thickness in the schizophrenia group, along with some novel alterations in venular microvascular structure. No associations between retinal measurements and symptom severity or illness-related variables were statistically significant. Conclusions: The strong discriminatory performance of our machine-learning (ML) model underscores the potential value of ML-based retinal analysis as an adjunct to existing diagnostic and risk-stratification approaches for schizophrenia. Our findings suggest retinal-layer alterations in schizophrenia and novel microvascular changes that have not been examined in earlier work. Together, these results highlight promising directions for future large-scale investigations into retinal biomarkers in schizophrenia.