Current Opinion in Ophthalmology最新文献

Purpose of review: To review the neuro-ophthalmic manifestations of Ehlers-Danlos syndrome (EDS).

Recent findings: Ehlers-Danlos syndrome (EDS) is a rare genetic disorder with an estimated prevalence of 1 in 5000 individuals, but its true prevalence may be underestimated because of variable clinical presentations and limited awareness among healthcare professionals. The neuro-ophthalmic features of EDS may be difficult to recognize in context but new molecular genetic testing is now available for identification of specific subtypes of EDS.

Summary: Ophthalmic manifestations of EDS include loss of vision and double vision (strabismus), high myopia, retinal detachment, and blue sclera. The vascular subtype of EDS can present as a carotid-cavernous fistula, intracranial aneurysm, or arterial dissection.

Purpose of review: The current review will discuss the pathophysiology, work-up and clinical relevance of the ocular phenotype in Williams-Beuren syndrome in detail.

Recent findings: Few case reports, case series and retrospective studies reported the ophthalmic features in Williams-Beuren syndrome, focusing on specific aspects of the ocular involvement. Recently, novel retinal findings have been described in association with the disease.

Summary: Numerous ocular features have been described in Williams-Beuren syndrome. Some of them, such as the stellate pattern of the iris or the retinal arteriolar tortuosity may be helpful for the diagnosis but have no significant clinical implications; others, such as strabismus and refractive errors require early treatment to reduce the risk of irreversible visual impairment. Finally, some features, such as a broad foveal pit and thinner retina still have unknown significance and require further longitudinal and multimodal studies.

Purpose of review: Diversity, equity and inclusion (DEI) initiatives in ophthalmology have received increased attention in recent years. This review will highlight disparities, barriers to workforce diversity, as well as current and future efforts to improve DEI in ophthalmology.

Recent findings: Racial, ethnic, socioeconomic and sex disparities exist in vision health and across many ophthalmology subspecialties. The pervasive disparities result from factors such as a lack of access to eye care. In addition, ophthalmology is one of the least diverse specialties at the resident and faculty level. The lack of diversity has also been documented in ophthalmology clinical trials, wherein participant demographics do not reflect the diversity of the U.S. population.

Summary: Addressing social determinants of health including racism and discrimination is necessary to promote equity in vision health. Diversifying the workforce and expanding the representation of marginalized groups in clinical research are also paramount. Supporting existing programmes and creating new ones focusing on improving workforce diversity and reducing eye care disparities are essential to ensure equity in vision health for all Americans.

Purpose of review: The current article provides an overview of the present approaches to algorithm validation, which are variable and largely self-determined, as well as solutions to address inadequacies.

Recent findings: In the last decade alone, numerous machine learning applications have been proposed for ophthalmic diagnosis or disease monitoring. Remarkably, of these, less than 15 have received regulatory approval for implementation into clinical practice. Although there exists a vast pool of structured and relatively clean datasets from which to develop and test algorithms in the computational 'laboratory', real-world validation remains key to allow for safe, equitable, and clinically reliable implementation. Bottlenecks in the validation process stem from a striking paucity of regulatory guidance surrounding safety and performance thresholds, lack of oversight on critical postdeployment monitoring and context-specific recalibration, and inherent complexities of heterogeneous disease states and clinical environments. Implementation of secure, third-party, unbiased, pre and postdeployment validation offers the potential to address existing shortfalls in the validation process.

Summary: Given the criticality of validation to the algorithm pipeline, there is an urgent need for developers, machine learning researchers, and end-user clinicians to devise a consensus approach, allowing for the rapid introduction of safe, equitable, and clinically valid machine learning implementations.

Purpose of review: Smart eyewear is a head-worn wearable device that is evolving as the next phase of ubiquitous wearables. Although their applications in healthcare are being explored, they have the potential to revolutionize teleophthalmology care. This review highlights their applications in ophthalmology care and discusses future scope.

Recent findings: Smart eyewear equips advanced sensors, optical displays, and processing capabilities in a wearable form factor. Rapid technological developments and the integration of artificial intelligence are expanding their reach from consumer space to healthcare applications. This review systematically presents their applications in treating and managing eye-related conditions. This includes remote assessments, real-time monitoring, telehealth consultations, and the facilitation of personalized interventions. They also serve as low-vision assistive devices to help visually impaired, and can aid physicians with operational and surgical tasks.

Summary: Wearables such as smart eyewear collects rich, continuous, objective, individual-specific data, which is difficult to obtain in a clinical setting. By leveraging sophisticated data processing and artificial intelligence based algorithms, these data can identify at-risk patients, recognize behavioral patterns, and make timely interventions. They promise cost-effective and personalized treatment for vision impairments in an effort to mitigate the global burden of eye-related conditions and aging.

Purpose of review: Home monitoring in ophthalmology is appropriate for disease stages requiring frequent monitoring or rapid intervention, for example, neovascular age-related macular degeneration (AMD) and glaucoma, where the balance between frequent hospital attendance versus risk of late detection is a constant challenge. Artificial intelligence approaches are well suited to address some challenges of home monitoring.

Recent findings: Ophthalmic data collected at home have included functional (e.g. perimetry), biometric (e.g. intraocular pressure), and imaging [e.g. optical coherence tomography (OCT)] data. Potential advantages include early detection/intervention, convenience, cost, and visual outcomes. Artificial intelligence can assist with home monitoring workflows by handling large data volumes from frequent testing, compensating for test quality, and extracting useful metrics from complex data. Important use cases include machine learning applied to hyperacuity self-testing for detecting neovascular AMD and deep learning applied to OCT data for quantifying retinal fluid.

Summary: Home monitoring of health conditions is useful for chronic diseases requiring rapid intervention or frequent data sampling to decrease risk of irreversible vision loss. Artificial intelligence may facilitate accurate, frequent, large-scale home monitoring, if algorithms are integrated safely into workflows. Clinical trials and economic evaluations are important to demonstrate the value of artificial intelligence-based home monitoring, towards improved visual outcomes.