Thyroid eye disease is a complex inflammatory disorder of the orbit that has gained tremendous interest over the past years, and numerous scientific efforts have been deployed to elucidate its pathophysiology for novel drug development. Our manuscript will delve into the molecular dysregulations involved in the pathogenesis of thyroid eye disease that led to its clinical manifestations. Abnormalities within the apoptotic pathway, inflammatory cascade, and autoimmune regulatory systems will be covered. We will further discuss the challenges involved in its diagnosis and management and provide a summary of the current diagnostic tools (i.e., molecular biomarkers, diagnostic scores) from the perspective of clinicians. Finally, our comprehensive literature review will provide a thorough summary of most recent preclinical and clinical studies around the topic of thyroid eye disease, with an emphasis on the manuscripts published within the last five years. We believe our manuscript will bring novelty within the field by bridging the fundamental sciences with the clinical aspect of this disease. This review will be a great tool for clinicians in better understanding the pathogenesis of thyroid eye disease while providing an outlook on future perspectives (i.e., liquid biopsies, artificial intelligence).

Clinical decision support systems (CDSS) are an emerging frontier in teleophthalmology, drawing on heuristic decision making to augment processes such as triage and referral. We describe the development and implementation of a novel cloud-based decision tree CDSS for on-call ophthalmology consults. The objective was to standardize the triage and referral process while providing a more accurate provisional diagnosis and urgency. Prospective comparative cohort study. On-call referrals to a Canadian community ophthalmology clinic. A web-based decision tree algorithm was developed using current guidelines and expert opinion. The algorithm collected tailored information on the patient’s ophthalmic concern, and outputted a provisional diagnosis and urgency before sending an electronic referral to the on-call ophthalmology clinic. Data were described using descriptive statistics. Spearman-rho correlations and Cohen’s kappa coefficient were used to characterize the observed relationships. Post hoc analysis was conducted using analysis of contingency tables and adjusted residuals. Diagnostic category, provisional diagnosis, and urgency for the referring provider, CDSS, and ophthalmologist. Ninety-six referrals were processed. Referring providers included medical doctors (76.0%, n = 73), optometrists (20.8%, n = 20), and nurse practitioners (3.1%, n = 3). The CDSS (κ = 0.5898; 95% confidence interval [CI], 0.4868–0.6928; P < 0.0001) performed equally well with 66.7% agreement in determining category when compared with referring providers (κ = 0.5880; 95% CI, 0.4798–0.6961; P < 0.0001). The CDSS (agreement = 53.1%; κ = 0.4999; 95% CI, 0.4021–0.5978; P < 0.0001) performed better than referring providers (agreement = 43.8%; κ = 0.4191; 95% CI, 0.3194–0.5188; P < 0.0001) in determining a diagnosis. The CDSS (ρ = 0.5014; 95% CI, 0.3092–0.6935; P < 0.0001) also performed better than referring providers (ρ = 0.4035; 95% CI, 0.2406–0.5665; P < 0.0001) in determining urgency. The CDSS assigned a lower level of urgency in 22 cases (22.9%) compared with referring providers in 6 cases (6.3%). To our knowledge, this is the first cloud-based CDSS in ophthalmology designed to augment the triage and referral process. The CDSS achieves a more accurate diagnosis and urgency, standardizes information collection, and overcomes antiquated paper-based consults. Future directions include developing a random forest model or integrating convolutional neural network-based machine learning to refine the speed and accuracy of triage and referral processes, with emphasis on increasing sensitivity of the CDSS.

Intraocular malignant tumors are rare; however, they can cause serious life-threatening complications. Uveal melanoma (UM) and retinoblastoma (RB) are the most common intraocular tumors in adults and children, respectively, and come with a great disease burden. For many years, several different treatment modalities for UM and RB have been proposed, with chemotherapy for RB cases and plaque radiation therapy for localized UM as first-line treatment options. Extraocular extension, recurrence, and metastasis constitute the major challenges of conventional treatments. To overcome these obstacles, immunotherapy, which encompasses different treatment options such as oncolytic viruses, antibody-mediated immune modulations, and targeted immunotherapy, has shown great potential as a novel therapeutic tool for cancer therapy. These anti-cancer treatment options provide numerous advantages such as selective cancer cell death and the promotion of an anti-tumor immune response, and they prove useful in preventing vision impairment due to macular and/or optic disc involvement. Numerous factors such as the vector choice, route of administration, dosing, and patient characteristics must be considered when engineering an oncolytic virus or other forms of immunotherapy vectors. This manuscript provides an in-depth review of the molecular design of oncolytic viruses (e.g., virus capsid proteins and encapsulation technologies, vectors for delivery, cell targeting) and immunotherapy. The most recent advances in preclinical- and clinical-phase studies are further summarized. The recent developments in virus-like drug conjugates (i.e., AU011), oncolytic viruses for metastatic UM, and targeted immunotherapies have shown great results in clinical trials for the future clinical application of these novel technologies in the treatment algorithm of certain intraocular tumors.

• Similarly structured virtual learning programs may address critical gaps in undergraduate medical education of ophthalmology. • Medical students prefer interactive virtual modalities over traditional didactic methods for learning ophthalmology. • Social isolation in learners and inequities in access to medical education can be mitigated with open-access virtual learning.

Diabetic retinopathy (DR) remains the leading cause of preventable blindness among working-age adults worldwide, driven by the growing prevalence of diabetes mellitus. The aim of this comprehensive literature review is to provide an insightful analysis of recent advances in the pathogenesis of DR, followed by a summary of emerging technologies for its diagnosis and treatment. Recent studies have explored the roles of cell death pathways, immune activation, and lipid peroxidation in the pathology of DR. However, at the core of DR pathology lies neovascularization driven by vascular endothelial growth factor (VEGF), and mitochondrial damage due to dysregulated oxidative stress. These dysregulated pathways manifest clinically as DR, with specific subtypes including non-proliferative DR, proliferative DR and diabetic macular edema, which can be diagnosed through various imaging modalities. Recently, novel advances have been made using liquid biopsy and artificial (AI)-based algorithms with the goal of transforming DR diagnostics. AI models show distinct promise with the capacity to provide automated interpretation of retinal imaging. Furthermore, conventional anti-VEGF injectable agents have revolutionized DR treatment in the past decades. Today, as the pathogenesis of DR becomes better understood, new pathways, such as the ROS-VEGF loop, are being elucidated in greater depth, enabling the development of targeted therapies. In addition, new innovations such as intravitreal implants are transforming the delivery of DR-specific medication. This paper will discuss the current understanding of the pathogenesis of DR, which is leading to new diagnostic and therapeutic tools that will transform clinical management of DR.