A Health-Related Quality of Life Measure for Patients Who Undergo Minimally Invasive Glaucoma Surgery.

PURPOSE: To develop a standardized patient-reported outcome measure to assess the impact of glaucoma and treatment, including minimally invasive glaucoma surgery (MIGS), on health-related quality of life (HRQOL). DESIGN: Observational study before and after concomitant cataract and FDA-approved implantable MIGS device surgery to provide information on the measure's performance in assessing HRQOL. METHODS SETTING: Survey administration was done by electronic patient-reported outcomes (ePRO) application to patients at multiple sites on a computer, iPad, or similar device. PATIENT POPULATION: One hundred eighty-four adults completed a baseline survey, 124 completed a survey 3 months after surgery, and 106 completed the 1-month test-retest reliability survey. The age range was 37-89, and the average age was 72. Most of the respondents were female (57%), non-Hispanic White (81%), and had a college degree (56%). MAIN OUTCOME MEASURES: The Glaucoma Outcomes Survey (GOS) includes 42 questions assessing functional limitations (27 items), vision-related symptoms (7 items), psychosocial issues (7 items) and satisfaction with microinvasive glaucoma surgery (1 item). The three multiple-item scales were scored on a 0-100 range, with a higher score indicating worse health. RESULTS: Internal consistency reliability estimates ranged from 0.75 (vision-related symptoms) to 0.93 (functional limitations) and one-month test-retest intraclass correlations ranged from 0.65 (PROMIS global mental health) to 0.92 (functional limitations). Product-moment correlations among the GOS scales ranged from 0.56 to 0.60. Improvement in visual acuity in the study eye from baseline to the 3-month follow-up was significantly related to improvements in GOS functional limitations (r =0.18, p =0.0485), vision-related symptoms (r = 0.19, p = 0.0386), and psychosocial concerns (r = 0.18, p =0.0503). The highest proportion of responders to treatment was seen for the GOS functional limitations scale (48%), followed by GOS psychosocial issues (21%) and GOS vision-related symptoms (17%). CONCLUSIONS: This study provides initial support for using the GOS instrument in ophthalmic procedures such as MIGS. Further evaluation of the GOS in other samples, including different patient subgroups and clinical settings, will be valuable. The instrument may be useful for evaluations of other treatments for glaucoma.

Considerations for addressing bias in artificial intelligence for health equity.

Health equity is a primary goal of healthcare stakeholders: patients and their advocacy groups, clinicians, other providers and their professional societies, bioethicists, payors and value based care organizations, regulatory agencies, legislators, and creators of artificial intelligence/machine learning (AI/ML)-enabled medical devices. Lack of equitable access to diagnosis and treatment may be improved through new digital health technologies, especially AI/ML, but these may also exacerbate disparities, depending on how bias is addressed. We propose an expanded Total Product Lifecycle (TPLC) framework for healthcare AI/ML, describing the sources and impacts of undesirable bias in AI/ML systems in each phase, how these can be analyzed using appropriate metrics, and how they can be potentially mitigated. The goal of these "Considerations" is to educate stakeholders on how potential AI/ML bias may impact healthcare outcomes and how to identify and mitigate inequities; to initiate a discussion between stakeholders on these issues, in order to ensure health equity along the expanded AI/ML TPLC framework, and ultimately, better health outcomes for all.

FDA's role in expediting innovation of bioelectronic implants for vision restoration.

Bioelectronic implants for vision restoration are medical devices regulated in the United States by the Food and Drug Administration (FDA). This paper provides an overview of regulatory pathways and related FDA programs for bioelectronic implants for vision restoration, and identifies some of the gaps in the regulatory science of these devices. The FDA recognizes that additional discussion regarding development in this space is needed to further develop bioelectronic implants and ensure that safe and effective technologies are made available to patients with profound vision loss. FDA regularly participates in the Eye and the Chip World Research Congress meetings and continues to engage with important external stakeholders, including through public workshops such as the recent co-sponsored Expediting Innovation of Bioelectronic Implants for Vision Restoration. By participating in forums for discussion of these devices with all stakeholders, especially patients, FDA seeks to encourage advancement of these devices.

Development of a Patient-Reported Outcome Measure to Assess Symptoms Associated with Cataract Surgery and Intraocular Lens Implants.

PURPOSE: To develop a standardized patient-reported outcome measure of visual perceptions and symptoms for implanted premium and monofocal intraocular lenses (IOLs). DESIGN: Observational study before and after IOL implants to assess the measure and symptom experience. PARTICIPANTS: Adults scheduled for binocular implantation of the same IOL type completed the survey at baseline prior to surgery (n = 716) and postoperatively (n = 554). Most respondents were female (64%), White (81%), 61 or older (89%), and had some college or more education (62%). METHODS: Administration was by web survey with mail follow-up and phone reminders. MAIN OUTCOME MEASURES: Frequency, severity, and level of symptom bother in the last 7 days for 14 symptoms: (1) glare, (2) hazy vision, (3) blurry vision, (4) starbursts, (5) halos, (6) snowballs, (7) floaters, (8) double images, (9) rings and spider webs, (10) distortion, (11) light flashes with eyes closed, (12) light flashes with eyes open, (13) shimmering images, and (14) dark shadows. RESULTS: The median correlation among having 14 symptoms at baseline was only 0.19. Mean uncorrected binocular visual acuity improved from a preoperative value of 0.47 logarithm of the minimum angle of resolution (logMAR; Snellen 20/59) to a postoperative value of 0.12 (20/26) and best-corrected binocular visual acuity improved from 0.23 logMAR (20/34) preoperative to 0.05 logMAR (20/22) postoperative. The most bothersome symptoms were reduced after surgery: preoperative/postoperative glare (84%/36%), blurry vision (68%/22%), starbursts (66%/28%), hazy vision (63%/18%), snowballs (55%/17%), and halos (52%/22%). All symptoms decreased significantly (P < 0.0001) from before to after surgery except for dark crescent-shaped shadows (4%/4%). The percentage of symptoms rated as quite a bit or extremely bothersome declined from before to after surgery except for dark crescent-shaped shadows (29%/32%): blurry vision (54%/15%), snowballs (52%/14%), glare (49%/15%), and halos (46%/14%). Having monofocal IOL implants was associated with significantly more reduction in halos, starbursts, glare, and rings and spider webs, but less improvement in self-reported general vision. CONCLUSIONS: This study provides support for the 37-item Assessment of IntraOcular Lens Implant Symptoms (AIOLIS) instrument for use to assess symptoms and general perceptions of vision in clinical studies and clinical care. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found after the references.

Symptoms and Satisfaction Levels Associated with Intraocular Lens Implants in the Monofocal and Premium IOL Patient-Reported Outcome Measure Study.

PURPOSE: To develop a questionnaire with standardized questions and images about visual symptoms and satisfaction administered before and after cataract surgery with monofocal and various (premium) intraocular lenses (IOLs). DESIGN: A prospective, observational study of cataract surgery patients completing a self-administered questionnaire preoperatively and postoperatively at 4 to 6 months. PARTICIPANTS: Five hundred fifty-four patients with plans to undergo implantation of the same IOL in both eyes on separate occasions in 20 ophthalmology practices. METHODS: An 86-item questionnaire with standardized images assessed the following 14 symptoms: glare, blurry vision, starbursts, hazy vision, snowballs, halos, floaters, double images, rings and spider webs, light flashes with eyes closed, distortion, light flashes with eyes open, shimmering images, and dark crescent-shaped shadows. MAIN OUTCOME MEASURES: Symptom severity and level of symptom bother, satisfaction with vision, quality of vision, and ability to see without corrective lenses or eyeglasses. RESULTS: Except for dark crescent-shaped shadows, the report of visual symptoms significantly decreased postoperatively. Best uncorrected binocular visual acuity improved from 0.47 (20/59 Snellen visual acuity values) ± 0.35 logarithm of the minimum angle of resolution (logMAR) preoperatively to 0.12 (20/26 Snellen visual acuity values) ± 0.12 logMAR postoperatively. Patients' ratings of intermediate vision as good to excellent improved significantly from 12% preoperatively to 71% postoperatively, and patients' ratings of distance vision improved from 8% preoperatively to 85% postoperatively. After surgery, 84% reported that they were somewhat, very, or completely satisfied with their vision. Most patients (88%) reported that they could see pretty well, very well, or perfectly well without corrective lenses after surgery. CONCLUSIONS: The Assessment of IntraOcular Lens Implant Symptoms questionnaire can be used across a wide variety of IOLs to evaluate visual symptoms and satisfaction with a growing segment of the market, premium IOLs, that target intermediate and near vision, in addition to distance vision. Compared to patients receiving monofocal IOLs, patients receiving premium IOLs appear to be more challenging to satisfy because of their requirements for distance, intermediate, and near vision, and their desire to be free of eyeglasses postoperatively. This instrument provides a structured, uniform tool for regulators, researchers, and ophthalmologists in everyday practice to gain insights into patients' experiences. FINANCIAL DISCLOSURE(S): The author(s) have no proprietary or commercial interest in any materials discussed in this article.

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. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found after the references.

Artificial Intelligence for Glaucoma: Creating and Implementing Artificial Intelligence for Disease Detection and Progression.

On September 3, 2020, the Collaborative Community on Ophthalmic Imaging conducted its first 2-day virtual workshop on the role of artificial intelligence (AI) and related machine learning techniques in the diagnosis and treatment of various ophthalmic conditions. In a session entitled "Artificial Intelligence for Glaucoma," a panel of glaucoma specialists, researchers, industry experts, and patients convened to share current research on the application of AI to commonly used diagnostic modalities, including fundus photography, OCT imaging, standard automated perimetry, and gonioscopy. The conference participants focused on the use of AI as a tool for disease prediction, highlighted its ability to address inequalities, and presented the limitations of and challenges to its clinical application. The panelists' discussion addressed AI and health equities from clinical, societal, and regulatory perspectives.

Artificial Intelligence for Retinopathy of Prematurity: Validation of a Vascular Severity Scale against International Expert Diagnosis.

PURPOSE: To validate a vascular severity score as an appropriate output for artificial intelligence (AI) Software as a Medical Device (SaMD) for retinopathy of prematurity (ROP) through comparison with ordinal disease severity labels for stage and plus disease assigned by the International Classification of Retinopathy of Prematurity, Third Edition (ICROP3), committee. DESIGN: Validation study of an AI-based ROP vascular severity score. PARTICIPANTS: A total of 34 ROP experts from the ICROP3 committee. METHODS: Two separate datasets of 30 fundus photographs each for stage (0-5) and plus disease (plus, preplus, neither) were labeled by members of the ICROP3 committee using an open-source platform. Averaging these results produced a continuous label for plus (1-9) and stage (1-3) for each image. Experts were also asked to compare each image to each other in terms of relative severity for plus disease. Each image was also labeled with a vascular severity score from the Imaging and Informatics in ROP deep learning system, which was compared with each grader's diagnostic labels for correlation, as well as the ophthalmoscopic diagnosis of stage. MAIN OUTCOME MEASURES: Weighted kappa and Pearson correlation coefficients (CCs) were calculated between each pair of grader classification labels for stage and plus disease. The Elo algorithm was also used to convert pairwise comparisons for each expert into an ordered set of images from least to most severe. RESULTS: The mean weighted kappa and CC for all interobserver pairs for plus disease image comparison were 0.67 and 0.88, respectively. The vascular severity score was found to be highly correlated with both the average plus disease classification (CC = 0.90, P < 0.001) and the ophthalmoscopic diagnosis of stage (P < 0.001 by analysis of variance) among all experts. CONCLUSIONS: The ROP vascular severity score correlates well with the International Classification of Retinopathy of Prematurity committee member's labels for plus disease and stage, which had significant intergrader variability. Generation of a consensus for a validated scoring system for ROP SaMD can facilitate global innovation and regulatory authorization of these technologies.

From Data to Deployment: The Collaborative Community on Ophthalmic Imaging Roadmap for Artificial Intelligence in Age-Related Macular Degeneration.

OBJECTIVE: Health care 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 Food and Drug Administration (FDA)-approved AI devices for AMD. PURPOSE: To delineate the state of AI for AMD, including current data, standards, achievements, and challenges. METHODS: 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 a consensus. RESULTS: Existing infrastructure for robust AI development for AMD includes several large, labeled data sets of color fundus photography and OCT images; however, image data often do not contain the metadata 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 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 clinical setting generalization. CONCLUSIONS: 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 development 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 eventually will benefit providers, health care systems, and patients.

Foundational Considerations for Artificial Intelligence Using Ophthalmic Images.

IMPORTANCE: The development of artificial intelligence (AI) and other machine diagnostic systems, also known as software as a medical device, and its recent introduction into clinical practice requires a deeply rooted foundation in bioethics for consideration by regulatory agencies and other stakeholders around the globe. OBJECTIVES: To initiate a dialogue on the issues to consider when developing a bioethically sound foundation for AI in medicine, based on images of eye structures, for discussion with all stakeholders. EVIDENCE REVIEW: The scope of the issues and summaries of the discussions under consideration by the Foundational Principles of Ophthalmic Imaging and Algorithmic Interpretation Working Group, as first presented during the Collaborative Community on Ophthalmic Imaging inaugural meeting on September 7, 2020, and afterward in the working group. FINDINGS: Artificial intelligence has the potential to improve health care access and patient outcome fundamentally while decreasing disparities, lowering cost, and enhancing the care team. Nevertheless, substantial concerns exist. Bioethicists, AI algorithm experts, as well as the Food and Drug Administration and other regulatory agencies, industry, patient advocacy groups, clinicians and their professional societies, other provider groups, and payors (i.e., stakeholders) working together in collaborative communities to resolve the fundamental ethical issues of nonmaleficence, autonomy, and equity are essential to attain this potential. Resolution impacts all levels of the design, validation, and implementation of AI in medicine. Design, validation, and implementation of AI warrant meticulous attention. CONCLUSIONS AND RELEVANCE: The development of a bioethically sound foundation may be possible if it is based in the fundamental ethical principles of nonmaleficence, autonomy, and equity for considerations for the design, validation, and implementation for AI systems. Achieving such a foundation will be helpful for continuing successful introduction into medicine before consideration by regulatory agencies. Important improvements in accessibility and quality of health care, decrease in health disparities, and lower cost thereby can be achieved. These considerations should be discussed with all stakeholders and expanded on as a useful initiation of this dialogue.

White Paper on Ophthalmic Imaging for Choroidal Nevus Identification and Transformation into Melanoma.

Purpose: To discuss the evolution of noninvasive diagnostic methods in the identification of choroidal nevus and determination of risk factors for malignant transformation as well as introduce the novel role that artificial intelligence (AI) can play in the diagnostic process. Methods: White paper. Results: Longstanding diagnostic methods to stratify benign choroidal nevus from choroidal melanoma and to further determine the risk for nevus transformation into melanoma have been dependent on recognition of key clinical features by ophthalmic examination. These risk factors have been derived from multiple large cohort research studies over the past several decades and have garnered widespread use throughout the world. More recent publications have applied ocular diagnostic testing (fundus photography, ultrasound examination, autofluorescence, and optical coherence tomography) to identify risk factors for the malignant transformation of choroidal nevus based on multimodal imaging features. The widespread usage of ophthalmic imaging systems to identify and follow choroidal nevus, in conjunction with the characterization of malignant transformation risk factors via diagnostic imaging, presents a novel path to apply AI. Conclusions: AI applied to existing ophthalmic imaging systems could be used for both identification of choroidal nevus and as a tool to aid in earlier detection of transformation to malignant melanoma. Translational Relevance: Advances in AI models applied to ophthalmic imaging systems have the potential to improve patient care, because earlier detection and treatment of melanoma has been proven to improve long-term clinical outcomes.

Vision-Targeted Health-Related Quality-of-Life Survey for Evaluating Minimally Invasive Glaucoma Surgery.

PURPOSE: To develop a vision-targeted health-related quality-of-life instrument for patients with glaucoma who are candidates for minimally invasive glaucoma surgery (MIGS). DESIGN: Development of a health-related quality-of-life instrument. PARTICIPANTS: Twelve practicing ophthalmologists and 41 glaucoma patients. METHODS: A questionnaire was constructed to assess functional limitations, vision-related symptoms, aesthetics, psychosocial issues, and surgical satisfaction for MIGS candidates. Questions were drafted after a review of the literature and subsequently refined based upon input from 1 physician and 4 patient focus groups. Nineteen cognitive interviews were used to ensure that questions were understandable to respondents. RESULTS: The focus group identified the following key issues and concerns as being important to glaucoma patients: functional limitations (eg, driving), bodily discomfort (eg, stinging from drops), changes in appearance (eg, drooping eyelid), and psychosocial concerns (eg, mental burden associated with a diagnosis of glaucoma, financial burden of treatment). Cognitive interviews resulted in the following improvements to the questionnaire: changes in wording to clarify lighting conditions, and additional questions addressing psychosocial issues, such as job loss, severity of disease, and perception of MIGS. CONCLUSIONS: A patient-reported outcomes instrument, the Glaucoma Outcomes Survey, was developed to evaluate MIGS for patients with mild to moderate glaucoma. Next steps include electronic administration to patients selected from the American Academy of Ophthalmology Intelligent Research in Sight (IRIS) registry. An electronic patient-reported outcomes platform will be used to administer the questionnaire before and after MIGS. The questionnaire will improve understanding of how surgical interventions such as MIGS impact vision-targeted health-related quality-of-life in glaucoma patients.

Development, Validation, and Innovation in Ophthalmic Laser-Based Imaging: Report From a US Food and Drug Administration-Cosponsored Forum.

In April 2019, the US Food and Drug Administration, in conjunction with 11 professional ophthalmic, vision science, and optometric societies, convened a forum on laser-based imaging. The forum brought together the Food and Drug Administration, clinicians, researchers, industry members, and other stakeholders to stimulate innovation and ensure that patients in the US are the first in the world to have access to high-quality, safe, and effective medical devices. This conference focused on the technology, clinical applications, regulatory issues, and reimbursement issues surrounding innovative ocular imaging modalities. Furthermore, the emerging role of artificial intelligence in ophthalmic imaging was reviewed. This article summarizes the presentations, discussion, and future directions.

Study design considerations for sleep-disordered breathing devices.

None: In recent years, sleep-disordered breathing (SDB) has been recognized as a prevalent but under-diagnosed condition in adults and has prompted the need for new and better diagnostic and therapeutic options. To facilitate the development and availability of innovative, safe and effective SDB medical device technologies for patients in the United States, the US Food and Drug Administration collaborated with six SDB-related professional societies and a consumer advocacy organization to convene a public workshop focused on clinical investigations of SDB devices. Sleep medicine experts discussed appropriate definitions of terms used in the diagnosis and treatment of SDB, the use of home sleep testing versus polysomnography, clinical trial design issues in studying SDB devices, and current and future trends in digital health technologies for diagnosis and monitoring SDB. The panel's breadth of clinical expertise and experience across medical specialties provided useful and important insights regarding clinical trial designs for SDB devices.

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

BACKGROUND: Many 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. OBJECTIVES: To summarize the progress to date. METHODS: Questionnaire 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. RESULTS: Questionnaire 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." CONCLUSIONS: Patients 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.