Patient-reported outcomes measures and patient preferences for minimally invasive glaucoma surgical devices.

BackgroundMany therapeutic options are available to glaucoma patients. One recent therapeutic option is minimally invasive glaucoma surgical (MIGS) devices. It is unclear how patients view different treatments and which patient-reported outcomes would be most relevant in patients with mild to moderate glaucoma. We developed a questionnaire for patients eligible for MIGS devices and a patient preference study to examine the value patients place on certain outcomes associated with glaucoma and its therapies.ObjectivesTo summarize the progress to date.MethodsQuestionnaire development: We drafted the questionnaire items based on input from one physician and four patient focus groups, and a review of the literature. We tested item clarity with six cognitive interviews. These items were further refined. Patient preference study: We identified important benefit and risk outcomes qualitatively using semi-structured, one-on-one interviews with patients who were eligible for MIGS devices. We then prioritized these outcomes quantitatively using best-worst scaling methods.ResultsQuestionnaire testing: Three concepts were deemed relevant for the questionnaire: functional limitations, symptoms, and psychosocial factors. We will evaluate the reliability and validity of the 52-item draft questionnaire in an upcoming field test. Patient preference study: We identified 13 outcomes that participants perceived as important. Outcomes with the largest relative importance weights were "adequate IOP control" and "drive a car during the day."ConclusionsPatients have the potential to steer clinical research towards outcomes that are important to them. Incorporating patients' perspectives into the MIGS device development and evaluation process may expedite innovation and availability of these devices

From data to deployment: the Collaborative Communities on Ophthalmic Imaging roadmap for artificial intelligence in age-related macular degeneration

IMPORTANCE: Healthcare systems worldwide are challenged to provide adequate care for the 200 million individuals with age-related macular degeneration (AMD). Artificial intelligence (AI) has the potential to make a significant positive impact on the diagnosis and management of patients with AMD. However, the development of effective AI devices for clinical care faces numerous considerations and challenges, a fact evidenced by a current absence of FDA-approved AI devices for AMD. OBJECTIVES: To delineate the state of AI for AMD including current data, standards, achievements, and challenges. EVIDENCE Members of the Collaborative Community on Ophthalmic Imaging working group for AI in AMD attended an inaugural meeting on September 7, 2020 to discuss the topic. Subsequently, they undertook a comprehensive review of the medical literature relevant to the topic. Members engaged in meetings and discussion through December 2021 to synthesize the information and arrive at consensus. FINDINGS: Existing infrastructure for robust AI development for AMD includes several large, labeled datasets of color fundus photography (CFP) and optical coherence tomography (OCT) images. However, image data often does not contain meta-data necessary for the development of reliable, valid, and generalizable models. Data sharing for AMD model development is made difficult by restrictions on data privacy and security, although potential solutions are under investigation. Computing resources may be adequate for current applications, but knowledge of machine learning (ML) development may be scarce in many clinical ophthalmology settings. Despite these challenges, researchers have produced promising AI models for AMD for screening, diagnosis, prediction, and monitoring. Future goals include defining benchmarks to facilitate regulatory authorization and subsequent real-world generalization. CONCLUSIONS: AND RELEVANCE: Delivering an FDA-authorized, AI-based device for clinical care in AMD involves numerous considerations including the identification of an appropriate clinical application, acquisition and curation of a large, high-quality data set, development of the AI architecture, training and validation of the model, and functional interactions between the model output and clinical end-user. The research efforts undertaken to date represent starting points for the medical devices that will eventually benefit providers, healthcare systems, and patients

The Definition of Glaucomatous Optic Neuropathy in Artificial Intelligence Research and Clinical Applications

OBJECTIVE: Although artificial intelligence (AI) models may offer innovative and powerful ways to use the wealth of data generated by diagnostic tools, there are important challenges related to their development and validation. Most notable is the lack of a perfect reference standard for glaucomatous optic neuropathy (GON). Because AI models are trained to predict presence of glaucoma or its progression, they generally rely on a reference standard that is used to train the model and assess its validity. If an improper reference standard is used, the model may be trained to detect or predict something that has little or no clinical value. This article summarizes the issues and discussions related to the definition of GON in AI applications as presented by the Glaucoma Workgroup from the Collaborative Community for Ophthalmic Imaging (CCOI) US Food and Drug Administration Virtual Workshop, on September 3 and 4, 2020, and on January 28, 2022. DESIGN: Review and conference proceedings. SUBJECTS: No human or animal subjects or data therefrom were used in the production of this article. METHODS: A summary of the Workshop was produced with input and approval from all participants. MAIN OUTCOME MEASURES: Consensus position of the CCOI Workgroup on the challenges in defining GON and possible solutions. RESULTS: The Workshop reviewed existing challenges that arise from the use of subjective definitions of GON and highlighted the need for a more objective approach to characterize GON that could facilitate replication and comparability of AI studies and allow for better clinical validation of proposed AI tools. Different tests and combination of parameters for defining a reference standard for GON have been proposed. Different reference standards may need to be considered depending on the scenario in which the AI models are going to be applied, such as community-based or opportunistic screening versus detection or monitoring of glaucoma in tertiary care. CONCLUSIONS: The development and validation of new AI-based diagnostic tests should be based on rigorous methodology with clear determination of how the reference standards for glaucomatous damage are constructed and the settings where the tests are going to be applied