Abstract: Diabetic retinopathy (DR) remains a leading cause of irreversible vision loss in adult populations around the globe. Despite growing evidence of the effectiveness of routine assessments and early intervention, DR screening strategies are not widely implemented largely due to an inadequate availability of resources to cope with the growing burden of diabetes. Advances in technology in the field of DR screening are clearly warranted and the recent emergence of deep learning-based artificial intelligence (AI) grading of retinal pathology offers significant potential benefits including an increased efficiency, accessibility and affordability of screening programmes.

Abstract: Training in residency programs is highly competitive, it requires the formation of competent physicians that achieve the performance standards that were declared for their technical skills, attitudes and interpersonal abilities. The use of simulation and technology on the medical education has increased considerably. Particularly in ophthalmology the simulators used are: live models from animal or cadavers, mannequins, wet laboratories, simulated patients, part-task moles, laser or surgical models, and more recently, virtual reality (VR). VR places a person in a simulated environment that has a specific sense of self-location, where the participant interacts with the objects within the setting. Teaching with VR refers to the use of the available resources in technology and visualization of structures to improve the educational experience of medical students, residents and physicians in professional continuous development programs. Several authors highlight the benefits of assessing trainees with the tools, they argue that the key contribution of this model is in the formative assessment. Rather than evaluating and putting a score on student’s grades, VR provides a powerful experience for the acquisition of skills. A conclusion is the need to develop studies to document the effects that it has on knowledge, skills and behaviors, and to patient related outcomes.

Abstract: Artificial intelligence (AI) methods have become a focus of intense interest within the eye care community. This parallels a wider interest in AI, which has started impacting many facets of society. However, understanding across the community has not kept pace with technical developments. What is AI, and how does it relate to other terms like machine learning or deep learning? How is AI currently used within eye care, and how might it be used in the future? This review paper provides an overview of these concepts for eye care specialists. We explain core concepts in AI, describe how these methods have been applied in ophthalmology, and consider future directions and challenges. We walk through the steps needed to develop an AI system for eye disease, and discuss the challenges in validating and deploying such technology. We argue that among medical fields, ophthalmology may be uniquely positioned to benefit from the thoughtful deployment of AI to improve patient care.

Abstract: The objective of the paper is to provide a general view for automatic cup to disc ratio (CDR) assessment in fundus images. As for the cause of blindness, glaucoma ranks as the second in ocular diseases. Vision loss caused by glaucoma cannot be reversed, but the loss may be avoided if screened in the early stage of glaucoma. Thus, early screening of glaucoma is very requisite to preserve vision and maintain quality of life. Optic nerve head (ONH) assessment is a useful and practical technique among current glaucoma screening methods. Vertical CDR as one of the clinical indicators for ONH assessment, has been well-used by clinicians and professionals for the analysis and diagnosis of glaucoma. The key for automatic calculation of vertical CDR in fundus images is the segmentation of optic cup (OC) and optic disc (OD). We take a brief description of methodologies about the OC and disc optic segmentation and comprehensively presented these methods as two aspects: hand-craft feature and deep learning feature. Sliding window regression, super-pixel level, image reconstruction, super-pixel level low-rank representation (LRR), deep learning methodologies for segmentation of OD and OC have been shown. It is hoped that this paper can provide guidance and bring inspiration to other researchers. Every mentioned method has its advantages and limitations. Appropriate method should be selected or explored according to the actual situation. For automatic glaucoma screening, CDR is just the reflection for a small part of the disc, while utilizing comprehensive factors or multimodal images is the promising future direction to furthermore enhance the performance.

Abstract: The most prominent causes of loss of vision in individuals over 50 years include age-related macular degeneration (AMD), glaucoma, and diabetic retinopathy (DR). While it is important to screen for these diseases effectively, current eye care is not properly doing so for much of the population, resulting in unfortunate visual disability and high costs for patients. Innovative functional testing can be unified with other screening methods for a more robust and safer screening and prediction of disease. The goal in the creation of functional testing modalities is to develop highly sensitive screening tests that are easy to use, accessible to all users, and inexpensive. The tests herein are deployed on an iPad with easily understood and intuitive instructions for rapid, streamlined, and automatic administration. These testing modalities could become highly sensitive screenings for early detection of potentially blinding diseases. The applications from our collaborators at AMA Optics include a cone photostress recovery test for detection of AMD and diabetic macular edema (DME), brightness balance perception for optic nerve dysfunction and especially glaucoma, color vision testing which is a broad screening tool, and visual acuity test. Machine learning with the combined structural and functional data will optimize identification of disease and prediction of outcomes. Here, we review and assess various tests of visual function that are easily administered on a tablet for screening in primary care. These user-friendly and simple screening tests allow patients to be identified in the early stages of disease for referral to specialists, proper assessment and treatment.

Abstract: Navigation technology in ophthalmology, colloquially called “eye-tracking”, has been applied to various areas of eye care. This approach encompasses motion-based navigation technology in both ophthalmic imaging and treatment. For instance, modern imaging instruments use a real-time eye-tracking system, which helps to reduce motion artefacts and increase signal-to-noise ratio in imaging acquisition such as optical coherence tomography (OCT), microperimetry, and fluorescence and color imaging. Navigation in ophthalmic surgery has been firstly applied in laser vision corrective surgery and spread to involve navigated retinal photocoagulation, and positioning guidance of intraocular lenses (IOL) during cataract surgery. It has emerged as one of the most reliable representatives of technology as it continues to transform surgical interventions into safer, more standardized, and more predictable procedures with better outcomes. Eye-tracking is essential in refractive surgery with excimer laser ablation. Using this technology for cataract surgery in patients with high preoperative astigmatism has produced better therapeutic outcomes. Navigated retinal laser has proven to be safer and more accurate compared to the use of conventional slit lamp lasers. Eye-tracking has also been used in imaging diagnostics, where it is essential for proper alignment of captured zones of interest and accurate follow-up imaging. This technology is not routinely discussed in the ophthalmic literature even though it has been truly impactful in our clinical practice and represents a small revolution in ophthalmology.