New Technologies for Outcome Measures in Retinal Disease: Review from the European Vision Institute Special Interest Focus Group

Two-photon excitation fluorescence in ophthalmology: safety and improved imaging for functional diagnostics

Two-photon excitation fluorescence (TPEF) is emerging as a powerful imaging technique with superior penetration power in scattering media, allowing for functional imaging of biological tissues at a subcellular level. TPEF is commonly used in cancer diagnostics, as it enables the direct observation of metabolism within living cells. The technique is now widely used in various medical fields, including ophthalmology. The eye is a complex and delicate organ with multiple layers of different cell types and tissues. Although this structure is ideal for visual perception, it generates aberrations in TPEF eye imaging. However, adaptive optics can now compensate for these aberrations, allowing for improved imaging of the eyes of animal models for human diseases. The eye is naturally built to filter out harmful wavelengths, but these wavelengths can be mimicked and thereby utilized in diagnostics via two-photon (2Ph) excitation. Recent advances in laser-source manufacturing have made it possible to minimize the exposure of in vivo measurements within safety, while achieving sufficient signals to detect for functional images, making TPEF a viable option for human application. This review explores recent advances in wavefront-distortion correction in animal models and the safety of use of TPEF on human subjects, both of which make TPEF a potentially powerful tool for ophthalmological diagnostics.

Looking Ahead: Visual and Anatomical Endpoints in Future Trials of Diabetic Macular Ischemia

Diabetic macular ischemia (DMI) is a common complication of diabetic retinopathy that can lead to progressive and irreversible visual loss. Despite substantial clinical burden, there are no treatments for DMI, no validated clinical trial endpoints, and few clinical trials focusing on DMI. Therefore, generating consensus on validated endpoints that can be used in DMI for the development of effective interventions is vital. In this review, we discuss potential endpoints appropriate for use in clinical trials of DMI, and consider the data required to establish acceptable and meaningful endpoints. A combination of anatomical, functional, and patient-reported outcome measures will provide the most complete picture of changes that occur during the progression of DMI. Potential endpoint measures include change in size of the foveal avascular zone measured by optical coherence tomography angiography and change over time in best-corrected visual acuity. However, these endpoints must be supported by further research. We also recommend studies to investigate the natural history and progression of DMI. In addition to improving understanding of how patient demographics and comorbidities such as diabetic macular edema affect clinical trial endpoints, these studies would help to build the consensus definition of DMI that is currently missing from clinical practice and research.

Foveal Therapy in Blue Cone Monochromacy: Predictions of Visual Potential From Artificial Intelligence

Novel therapeutic approaches for treating inherited retinal degenerations (IRDs) prompt a need to understand which patients with impaired vision have the anatomical potential to gain from participation in a clinical trial. We used supervised machine learning to predict foveal function from foveal structure in blue cone monochromacy (BCM), an X-linked congenital cone photoreceptor dysfunction secondary to mutations in the OPN1LW/OPN1MW gene cluster. BCM patients with either disease-associated large deletion or missense mutations were studied and results compared with those from subjects with other forms of IRD and various degrees of preserved central structure and function. A machine learning technique was used to associate foveal sensitivities and best-corrected visual acuities to foveal structure in IRD patients. Two random forest (RF) models trained on IRD data were applied to predict foveal function in BCM. A curve fitting method was also used and results compared with those of the RF models. The BCM and IRD patients had a comparable range of foveal structure. IRD patients had peak sensitivity at the fovea. Machine learning could successfully predict foveal sensitivity (FS) results from segmented or un-segmented optical coherence tomography (OCT) input. Application of machine learning predictions to BCM at the fovea showed differences between predicted and measured sensitivities, thereby defining treatment potential. The curve fitting method provided similar results. Given a measure of visual acuity (VA) and foveal outer nuclear layer thickness, the question of how many lines of acuity would represent the best efficacious result for each BCM patient could be answered. We propose that foveal vision improvement potential in BCM is predictable from retinal structure using machine learning and curve fitting approaches. This should allow estimates of maximal efficacy in patients being considered for clinical trials and also guide decisions about dosing.

New Technologies for Outcome Measures in Glaucoma: Review by the European Vision Institute Special Interest Focus Group

Glaucoma is the leading cause of irreversible blindness worldwide, with an increasing prevalence. The complexity of the disease has been a major challenge in moving the field forward with regard to both pathophysiological insight and treatment. In this context, discussing possible outcome measures in glaucoma trials is of utmost importance and clinical relevance. A recent meeting of the European Vision Institute (EVI) special interest focus group was held on "New Technologies for Outcome Measures in Retina and Glaucoma," addressing both functional and structural outcomes, as well as translational hot topics in glaucoma and retina research. In conjunction with the published literature, this review summarizes the meeting focusing on glaucoma.