Current Opinion in Ophthalmology最新文献

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: The Future Vision Forum discussed the current state of Human Centered Computing and the future of data collection, curation, and collation in ophthalmology. Although the uptake of electronic health record (EHR) systems and the digitization of healthcare data is encouraging, there are still barriers to implementing a specialty-wide clinical trial database. The article identifies several critical opportunities, including the need for standardization of image metadata and data, the establishment of a centralized trial database, incentives for clinicians and trial sponsors to participate, and resolving ethical concerns surrounding data ownership.

Findings: Recommendations to overcome these challenges include the standardization of image metadata using the Digital Imaging and Communications in Medicine (DICOM) guidelines, the establishment of a centralized trial database that uses federated learning (FL), and the use of FL to facilitate cross-institutional collaboration for rare diseases. Forum faculty suggests incentives will accelerate artificial intelligence, digital innovation projects, and data sharing agreements to empower patients to release their data.

Summary: A specialty-wide clinical trial database could provide invaluable insights into the natural history of disease, pathophysiology, why trials fail, and improve future clinical trial design. However, overcoming the barriers to implementation will require continued discussion, collaboration, and collective action from stakeholders across the ophthalmology community.

Purpose: Traditionally, ophthalmologists complete training and then choose a clinical care setting. The skills required to become an ophthalmologist can be applied to a variety of alternative career paths within and beyond healthcare. Not unexpectedly, therefore, there is a growing trend for ophthalmologists to explore alternative career paths in both healthcare and the life science industry more broadly. In this invited editorial, we summarize the more commonly considered 'alternative career paths,' and provide personal perspectives that have helped us and others when weighing such options.

Recent findings: Prior to pursuing an alternative career path, it is important to reflect on one's motivations and goals. A number of alternative careers paths are available, and the choice of when and what to pursue is both personal and personalizable. While it can be difficult to know a priori whether and to what extent a given path will be both enjoyable and rewarding, insights and advice from those who have walked that path before you can be invaluable. We review the more common paths of administrative leadership, entrepreneurship and innovation, product development, healthcare policy, nonprofit organizations, and investing, noting that these are just examples of the many options currently available.

Summary: Ophthalmologists should feel empowered to design a career that is both purposeful and personally meaningful, as this will result ultimately in the greatest happiness and fulfillment. There is a world of opportunity available to those who are willing to explore and create their own path.

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.

Purpose of review: To discuss the drawbacks and propose recommendations for integrating physician extenders in ophthalmologic practice.

Recent findings: In this article, the role of utilizing physician extenders in ophthalmology is discussed. A role for physician extenders has been suggested as more and more patients will require ophthalmologic care.

Summary: Guidance is needed on how to best integrate physician extenders into eye care. However, quality of care is of the highest importance, and unless there is reliable and consistent training of extenders, using physician extenders to administer invasive procedures (e.g., intravitreal injection) should be avoided due to safety concerns.

Purpose of review: The aim of this review is to define the "state-of-the-art" in artificial intelligence (AI)-enabled devices that support the management of retinal conditions and to provide Vision Academy recommendations on the topic.

Recent findings: Most of the AI models described in the literature have not been approved for disease management purposes by regulatory authorities. These new technologies are promising as they may be able to provide personalized treatments as well as a personalized risk score for various retinal diseases. However, several issues still need to be addressed, such as the lack of a common regulatory pathway and a lack of clarity regarding the applicability of AI-enabled medical devices in different populations.

Summary: It is likely that current clinical practice will need to change following the application of AI-enabled medical devices. These devices are likely to have an impact on the management of retinal disease. However, a consensus needs to be reached to ensure they are safe and effective for the overall population.

Purpose of review: The aim of this article is to provide an update on the latest applications of deep learning (DL) and classical machine learning (ML) techniques to the detection and prognostication of intraocular and ocular surface malignancies.

Recent findings: Most recent studies focused on using DL and classical ML techniques for prognostication purposes in patients with uveal melanoma (UM).

Summary: DL has emerged as the leading ML technique for prognostication in ocular oncological conditions, particularly in UM. However, the application of DL may be limited by the relatively rarity of these conditions.