How Generalizable Are Findings from a Community-Based Prospective Cohort Study? Extending Estimates from the Adult Changes in Thought Study to Its Source Population.

Background: The Adult Changes in Thought (ACT) study is a cohort of Kaiser Permanente Washington members ages 65+ that began in 1994. Objective: We wanted to know how well ACT participants represented all older adults in the region, and how well ACT findings on eye disease and its relationship with Alzheimer's disease generalized to all older adults in the Seattle Metropolitan Region. Methods: We used participation weights derived from pooling ACT and Behavioral Risk Factor Surveillance System (BRFSS) data to estimate prevalences of common eye diseases and their associations with Alzheimer's disease incidence. Cox proportional hazards models accounted for age, education, smoking, sex, and APOE genotype. Confidence intervals for weighted analyses were bootstrapped to account for error in estimating the weights. Results: ACT participants were fairly similar to older adults in the region. The largest differences were more self-reported current cholesterol medication use in BRFSS and higher proportions with low education in ACT. Incorporating the weights had little impact on prevalence estimates for age-related macular degeneration or glaucoma. Weighted estimates were slightly higher for diabetic retinopathy (weighted 5.7% (95% Confidence Interval 4.3, 7.1); unweighted 4.1% (3.6, 4.6)) and cataract history (weighted 51.8% (49.6, 54.3); unweighted 48.6% (47.3, 49.9)). The weighted hazard ratio for recent diabetic retinopathy diagnosis and Alzheimer's disease was 1.84 (0.34, 4.29), versus 1.32 (0.87, 2.00) in unweighted ACT. Conclusions: Most, but not all, associations were similar after participation weighting. Even in community-based cohorts, extending inferences to broader populations may benefit from evaluation with participation weights.

A Clinician's Guide to Sharing Data for AI in Ophthalmology.

Data is the cornerstone of using AI models, because their performance directly depends on the diversity, quantity, and quality of the data used for training. Using AI presents unique potential, particularly in medical applications that involve rich data such as ophthalmology, encompassing a variety of imaging methods, medical records, and eye-tracking data. However, sharing medical data comes with challenges because of regulatory issues and privacy concerns. This review explores traditional and nontraditional data sharing methods in medicine, focusing on previous works in ophthalmology. Traditional methods involve direct data transfer, whereas newer approaches prioritize security and privacy by sharing derived datasets, creating secure research environments, or using model-to-data strategies. We examine each method's mechanisms, variations, recent applications in ophthalmology, and their respective advantages and disadvantages. By empowering medical researchers with insights into data sharing methods and considerations, this review aims to assist informed decision-making while upholding ethical standards and patient privacy in medical AI development.

Sociodemographic and Visual Outcomes of Juvenile Idiopathic Arthritis Uveitis: IRIS® Registry Study.

Purpose: Understanding sociodemographic factors associated with poor visual outcomes in children with juvenile idiopathic arthritis-associated uveitis may help inform practice patterns. Patients and Methods: Retrospective cohort study on patients <18 years old who were diagnosed with both juvenile idiopathic arthritis and uveitis based on International Classification of Diseases tenth edition codes in the Intelligent Research in Sight Registry through December 2020. Surgical history was extracted using current procedural terminology codes. The primary outcome was incidence of blindness (20/200 or worse) in at least one eye in association with sociodemographic factors. Secondary outcomes included cataract and glaucoma surgery following uveitis diagnosis. Hazard ratios were calculated using multivariable-adjusted Cox proportional hazards models. Results: Median age of juvenile idiopathic arthritis-associated uveitis diagnosis was 11 (Interquartile Range: 8 to 15). In the Cox models adjusting for sociodemographic and insurance factors, the hazard ratios of best corrected visual acuity 20/200 or worse were higher in males compared to females (HR 2.15; 95% CI: 1.45-3.18), in Black or African American patients compared to White patients (2.54; 1.44-4.48), and in Medicaid-insured patients compared to commercially-insured patients (2.23; 1.48-3.37). Conclusion: Sociodemographic factors and insurance coverage were associated with varying levels of risk for poor visual outcomes in children with juvenile idiopathic arthritis-associated uveitis.

Convolutional neural network-based classification of glaucoma using optic radiation tissue properties.

BACKGROUND: Sensory changes due to aging or disease can impact brain tissue. This study aims to investigate the link between glaucoma, a leading cause of blindness, and alterations in brain connections. METHODS: We analyzed diffusion MRI measurements of white matter tissue in a large group, consisting of 905 glaucoma patients (aged 49-80) and 5292 healthy individuals (aged 45-80) from the UK Biobank. Confounds due to group differences were mitigated by matching a sub-sample of controls to glaucoma subjects. We compared classification of glaucoma using convolutional neural networks (CNNs) focusing on the optic radiations, which are the primary visual connection to the cortex, against those analyzing non-visual brain connections. As a control, we evaluated the performance of regularized linear regression models. RESULTS: We showed that CNNs using information from the optic radiations exhibited higher accuracy in classifying subjects with glaucoma when contrasted with CNNs relying on information from non-visual brain connections. Regularized linear regression models were also tested, and showed significantly weaker classification performance. Additionally, the CNN was unable to generalize to the classification of age-group or of age-related macular degeneration. CONCLUSIONS: Our findings indicate a distinct and potentially non-linear signature of glaucoma in the tissue properties of optic radiations. This study enhances our understanding of how glaucoma affects brain tissue and opens avenues for further research into how diseases that affect sensory input may also affect brain aging.

In this study, we explored the relationship between glaucoma, the most common cause of blindness, and changes within the brain. We used data from diffusion MRI, a measurement method which assesses the properties of brain connections. We examined 905 individuals with glaucoma alongside 5292 healthy people. We refined the test cohort to be closely matched in age, sex, ethnicity, and socioeconomic backgrounds. The use of deep learning neural networks allowed accurate detection of glaucoma by focusing on the tissue properties of the optic radiations, a major brain pathway that transmits visual information, rather than other brain pathways used for comparison. Our work provides additional evidence that brain connections may age differently based on varying sensory inputs.

Directrices para presentación de informes de ensayos clínicos sobre intervenciones con inteligencia artificial: extensión CONSORT-AI / Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension / Diretrizes para relatórios de ensaios clínicos com intervenções que utilizam inteligência artificial: a extensão CONSORT-AI

resumen está disponible en el texto completo

ABSTRACT The CONSORT 2010 statement provides minimum guidelines for reporting randomized trials. Its widespread use has been instrumental in ensuring transparency in the evaluation of new interventions. More recently, there has been a growing recognition that interventions involving artificial intelligence (AI) need to undergo rigorous, prospective evaluation to demonstrate impact on health outcomes. The CONSORT-AI (Consolidated Standards of Reporting Trials-Artificial Intelligence) extension is a new reporting guideline for clinical trials evaluating interventions with an AI component. It was developed in parallel with its companion statement for clinical trial protocols: SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials-Artificial Intelligence). Both guidelines were developed through a staged consensus process involving literature review and expert consultation to generate 29 candidate items, which were assessed by an international multi-stakeholder group in a two-stage Delphi survey (103 stakeholders), agreed upon in a two-day consensus meeting (31 stakeholders) and refined through a checklist pilot (34 participants). The CONSORT-AI extension includes 14 new items that were considered sufficiently important for AI interventions that they should be routinely reported in addition to the core CONSORT 2010 items. CONSORT-AI recommends that investigators provide clear descriptions of the AI intervention, including instructions and skills required for use, the setting in which the AI intervention is integrated, the handling of inputs and outputs of the AI intervention, the human-AI interaction and provision of an analysis of error cases. CONSORT-AI will help promote transparency and completeness in reporting clinical trials for AI interventions. It will assist editors and peer reviewers, as well as the general readership, to understand, interpret and critically appraise the quality of clinical trial design and risk of bias in the reported outcomes.

RESUMO A declaração CONSORT 2010 apresenta diretrizes mínimas para relatórios de ensaios clínicos randomizados. Seu uso generalizado tem sido fundamental para garantir a transparência na avaliação de novas intervenções. Recentemente, tem-se reconhecido cada vez mais que intervenções que incluem inteligência artificial (IA) precisam ser submetidas a uma avaliação rigorosa e prospectiva para demonstrar seus impactos sobre os resultados de saúde. A extensão CONSORT-AI (Consolidated Standards of Reporting Trials - Artificial Intelligence) é uma nova diretriz para relatórios de ensaios clínicos que avaliam intervenções com um componente de IA. Ela foi desenvolvida em paralelo à sua declaração complementar para protocolos de ensaios clínicos, a SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials - Artificial Intelligence). Ambas as diretrizes foram desenvolvidas por meio de um processo de consenso em etapas que incluiu revisão da literatura e consultas a especialistas para gerar 29 itens candidatos. Foram feitas consultas sobre esses itens a um grupo internacional composto por 103 interessados diretos, que participaram de uma pesquisa Delphi em duas etapas. Chegou-se a um acordo sobre os itens em uma reunião de consenso que incluiu 31 interessados diretos, e os itens foram refinados por meio de uma lista de verificação piloto que envolveu 34 participantes. A extensão CONSORT-AI inclui 14 itens novos que, devido à sua importância para as intervenções de IA, devem ser informados rotineiramente juntamente com os itens básicos da CONSORT 2010. A CONSORT-AI preconiza que os pesquisadores descrevam claramente a intervenção de IA, incluindo instruções e as habilidades necessárias para seu uso, o contexto no qual a intervenção de IA está inserida, considerações sobre o manuseio dos dados de entrada e saída da intervenção de IA, a interação humano-IA e uma análise dos casos de erro. A CONSORT-AI ajudará a promover a transparência e a integralidade nos relatórios de ensaios clínicos com intervenções que utilizam IA. Seu uso ajudará editores e revisores, bem como leitores em geral, a entender, interpretar e avaliar criticamente a qualidade do desenho do ensaio clínico e o risco de viés nos resultados relatados.

Directrices para los protocolos de ensayos clínicos de intervenciones con inteligencia artificial: la extensión SPIRIT-AI / Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension / Diretrizes para protocolos de ensaios clínicos com intervenções que utilizam inteligência artificial: a extensão SPIRIT-AI

resumen está disponible en el texto completo

ABSTRACT The SPIRIT 2013 statement aims to improve the completeness of clinical trial protocol reporting by providing evidence-based recommendations for the minimum set of items to be addressed. This guidance has been instrumental in promoting transparent evaluation of new interventions. More recently, there has been a growing recognition that interventions involving artificial intelligence (AI) need to undergo rigorous, prospective evaluation to demonstrate their impact on health outcomes. The SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials-Artificial Intelligence) extension is a new reporting guideline for clinical trial protocols evaluating interventions with an AI component. It was developed in parallel with its companion statement for trial reports: CONSORT-AI (Consolidated Standards of Reporting Trials-Artificial Intelligence). Both guidelines were developed through a staged consensus process involving literature review and expert consultation to generate 26 candidate items, which were consulted upon by an international multi-stakeholder group in a two-stage Delphi survey (103 stakeholders), agreed upon in a consensus meeting (31 stakeholders) and refined through a checklist pilot (34 participants). The SPIRIT-AI extension includes 15 new items that were considered sufficiently important for clinical trial protocols of AI interventions. These new items should be routinely reported in addition to the core SPIRIT 2013 items. SPIRIT-AI recommends that investigators provide clear descriptions of the AI intervention, including instructions and skills required for use, the setting in which the AI intervention will be integrated, considerations for the handling of input and output data, the human-AI interaction and analysis of error cases. SPIRIT-AI will help promote transparency and completeness for clinical trial protocols for AI interventions. Its use will assist editors and peer reviewers, as well as the general readership, to understand, interpret and critically appraise the design and risk of bias for a planned clinical trial.

RESUMO A declaração SPIRIT 2013 tem como objetivo melhorar a integralidade dos relatórios dos protocolos de ensaios clínicos, fornecendo recomendações baseadas em evidências para o conjunto mínimo de itens que devem ser abordados. Essas orientações têm sido fundamentais para promover uma avaliação transparente de novas intervenções. Recentemente, tem-se reconhecido cada vez mais que intervenções que incluem inteligência artificial (IA) precisam ser submetidas a uma avaliação rigorosa e prospectiva para demonstrar seus impactos sobre os resultados de saúde. A extensão SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials - Artificial Intelligence) é uma nova diretriz de relatório para protocolos de ensaios clínicos que avaliam intervenções com um componente de IA. Essa diretriz foi desenvolvida em paralelo à sua declaração complementar para relatórios de ensaios clínicos, CONSORT-AI (Consolidated Standards of Reporting Trials - Artificial Intelligence). Ambas as diretrizes foram desenvolvidas por meio de um processo de consenso em etapas que incluiu revisão da literatura e consultas a especialistas para gerar 26 itens candidatos. Foram feitas consultas sobre esses itens a um grupo internacional composto por 103 interessados diretos, que participaram de uma pesquisa Delphi em duas etapas. Chegou-se a um acordo sobre os itens em uma reunião de consenso que incluiu 31 interessados diretos, e os itens foram refinados por meio de uma lista de verificação piloto que envolveu 34 participantes. A extensão SPIRIT-AI inclui 15 itens novos que foram considerados suficientemente importantes para os protocolos de ensaios clínicos com intervenções que utilizam IA. Esses itens novos devem constar dos relatórios de rotina, juntamente com os itens básicos da SPIRIT 2013. A SPIRIT-AI preconiza que os pesquisadores descrevam claramente a intervenção de IA, incluindo instruções e as habilidades necessárias para seu uso, o contexto no qual a intervenção de IA será integrada, considerações sobre o manuseio dos dados de entrada e saída, a interação humano-IA e a análise de casos de erro. A SPIRIT-AI ajudará a promover a transparência e a integralidade nos protocolos de ensaios clínicos com intervenções que utilizam IA. Seu uso ajudará editores e revisores, bem como leitores em geral, a entender, interpretar e avaliar criticamente o delineamento e o risco de viés de um futuro estudo clínico.

Large language models and their impact in ophthalmology.

The advent of generative artificial intelligence and large language models has ushered in transformative applications within medicine. Specifically in ophthalmology, large language models offer unique opportunities to revolutionise digital eye care, address clinical workflow inefficiencies, and enhance patient experiences across diverse global eye care landscapes. Yet alongside these prospects lie tangible and ethical challenges, encompassing data privacy, security, and the intricacies of embedding large language models into clinical routines. This Viewpoint highlights the promising applications of large language models in ophthalmology, while weighing up the practical and ethical barriers towards their real-world implementation. This Viewpoint seeks to stimulate broader discourse on the potential of large language models in ophthalmology and to galvanise both clinicians and researchers into tackling the prevailing challenges and optimising the benefits of large language models while curtailing the associated risks.

Evaluating Access to Laser Eye Surgery by Driving Times Using Medicare Data and Geographical Mapping.

Importance: Recently, several states have granted optometrists privileges to perform select laser procedures (laser peripheral iridotomy, selective laser trabeculoplasty, and YAG laser capsulotomy) with the aim of increasing access. However, whether these changes are associated with increased access to these procedures among each state's Medicare population has not been evaluated. Objective: To compare patient access to laser surgery eye care by estimated travel time and 30-minute proximity to an optometrist or ophthalmologist. Design, Setting, and Participants: This retrospective cohort database study used Medicare Part B claims data from 2016 through 2020 for patients accessing new patient or laser eye care (laser peripheral iridotomy, selective laser trabeculoplasty, YAG) from optometrists or ophthalmologists in Oklahoma, Kentucky, Louisiana, Arkansas, and Missouri. Analysis took place between December 2021 and March 2023. Main Outcome and Measures: Percentage of each state's Medicare population within a 30-minute travel time (isochrone) of an optometrist or ophthalmologist based on US census block group population and estimated travel time from patient to health care professional. Results: The analytic cohort consisted of 1 564 307 individual claims. Isochrones show that optometrists performing laser eye surgery cover a geographic area similar to that covered by ophthalmologists. Less than 5% of the population had only optometrists (no ophthalmologists) within a 30-minute drive in every state except for Oklahoma for YAG (301 470 [7.6%]) and selective laser trabeculoplasty (371 097 [9.4%]). Patients had a longer travel time to receive all laser procedures from optometrists than ophthalmologists in Kentucky: the shortest median (IQR) drive time for an optometrist-performed procedure was 49.0 (18.4-71.7) minutes for YAG, and the the longest median (IQR) drive time for an ophthalmologist-performed procedure was 22.8 (12.1-41.4) minutes, also for YAG. The median (IQR) driving time for YAG in Oklahoma was 26.6 (12.2-56.9) for optometrists vs 22.0 (11.2-40.8) minutes for ophthalmologists, and in Arkansas it was 90.0 (16.2-93.2) for optometrists vs 26.5 (11.8-51.6) minutes for ophthalmologists. In Louisiana, the longest median (IQR) travel time to receive laser procedures from optometrists was for YAG at 18.5 (7.6-32.6) minutes and the shortest drive to receive procedures from ophthalmologists was for YAG at 20.5 (11.7-39.7) minutes. Conclusions and Relevance: Although this study did not assess impact on quality of care, expansion of laser eye surgery privileges to optometrists was not found to lead to shorter travel times to receive care or to a meaningful increase in the percentage of the population with nearby health care professionals.

Race, Ethnicity, Insurance, and Population Density Associations with Pediatric Strabismus and Strabismic Amblyopia in the IRIS® Registry.

PURPOSE: To evaluate the associations of sociodemographic factors with pediatric strabismus diagnosis and outcomes. DESIGN: Retrospective cohort study. PARTICIPANTS: American Academy of Ophthalmology IRIS® Registry (Intelligent Research in Sight) patients with strabismus diagnosed before the age of 10 years. METHODS: Multivariable regression models evaluated the associations of race and ethnicity, insurance, population density, and ophthalmologist ratio with age at strabismus diagnosis, diagnosis of amblyopia, residual amblyopia, and strabismus surgery. Survival analysis evaluated the same predictors of interest with the outcome of time to strabismus surgery. MAIN OUTCOME MEASURES: Age at strabismus diagnosis, rate of amblyopia and residual amblyopia, and rate of and time to strabismus surgery. RESULTS: The median age at diagnosis was 5 years (interquartile range, 3-7) for 106 723 children with esotropia (ET) and 54 454 children with exotropia (XT). Amblyopia diagnosis was more likely with Medicaid insurance than commercial insurance (odds ratio [OR], 1.05 for ET; 1.25 for XT; P < 0.01), as was residual amblyopia (OR, 1.70 for ET; 1.53 for XT; P < 0.01). For XT, Black children were more likely to develop residual amblyopia than White children (OR, 1.34; P < 0.01). Children with Medicaid were more likely to undergo surgery and did so sooner after diagnosis (hazard ratio [HR], 1.23 for ET; 1.21 for XT; P < 0.01) than those with commercial insurance. Compared with White children, Black, Hispanic, and Asian children were less likely to undergo ET surgery and received surgery later (all HRs < 0.87; P < 0.01), and Hispanic and Asian children were less likely to undergo XT surgery and received surgery later (all HRs < 0.85; P < 0.01). Increasing population density and clinician ratio were associated with lower HR for ET surgery (P < 0.01). CONCLUSIONS: Children with strabismus covered by Medicaid insurance had increased odds of amblyopia and underwent strabismus surgery sooner after diagnosis compared with children covered by commercial insurance. After adjusting for insurance status, Black, Hispanic, and Asian children were less likely to receive strabismus surgery with a longer delay between diagnosis and surgery compared with White children. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Thyroid Eye Disease and its Vision-Threatening Manifestations in the Academy IRIS Registry: 2014-2018.

PURPOSE: To evaluate prevalence of thyroid eye disease (TED) and associated factors in the American Academy of Ophthalmology IRISⓇ Registry (Intelligent Research in Sight). DESIGN: Cross-sectional analysis of the IRIS Registry. METHODS: IRIS Registry patients (18-90 years old) were classified as TED (ICD-9: 242.00, ICD-10: E05.00 on ≥2 visits) or non-TED cases, and prevalence was estimated. Odds ratios (OR) and 95% Confidence Intervals (CIs) were estimated using logistic regression. RESULTS: 41,211 TED patients were identified. TED prevalence was 0.09%, showed a unimodal age distribution (highest prevalence in ages 50-59 years (y) (0.12%)), higher rates in females than males (0.12% vs. 0.04%) and in non-Hispanics than Hispanics (0.10% vs. 0.05%). Prevalence differed by race (from 0.08% in Asians to 0.12% in Black/African-Americans), with varying peak ages of prevalence. Factors associated with TED in multivariate analysis included age: ((18-<30y (reference), 30-39y: OR (95%CI) 2.2 (2.0, 2.4), 40-49y: 2.9 (2.7,3.1), 50-59y: 3.3 (3.1, 3. 5), 60-69y: 2.7 (2.54, 2.85), 70+: 1.5 (1.46, 1.64)); female sex vs male (reference), 3.5 (3.4,3.6), race: White (reference), Blacks: 1.1 (1.1,1.2), Asian: 0.9 (0.8,0.9), Hispanic ethnicity vs not Hispanic (reference), 0.68 (0.6,0.7), smoking status: (never (ref), former: 1.64 (1.6,1.7), current 2.16: (2.1,2.2)) and Type 1 diabetes (yes vs no (reference): 1.87 (1.8, 1.9). CONCLUSIONS: This epidemiologic profile of TED includes new observations such as a unimodal age distribution and racial variation in prevalence. Associations with female sex, smoking, and Type 1 diabetes are consistent with prior reports. These findings raise novel questions about TED in different populations.

Predictors of Long-term Ophthalmic Complications after Closed Globe Injuries Using the Intelligent Research in Sight (IRIS®) Registry.

Purpose: To identify clinical factors associated with the need for future surgical intervention following closed globe ocular trauma. Design: Retrospective cohort study. Subjects Participants and/or Controls: Patients in the American Academy of Ophthalmology Intelligent Research in Sight (IRIS®) Registry with a diagnosis of closed globe ocular trauma occurring between 2013 and 2019, identified using International Classification of Disease, 10th Revision and Systematized Nomenclature of Medicine codes. Methods: Diagnosis codes were used to identify multiple concomitant diagnoses present on the date of closed globe ocular trauma. Survival analyses were performed for each outcome of interest, and linear regression was used to identify clinical factors associated with the risk of surgical intervention. Main Outcome Measures: Outcomes included retinal break treatment, retinal detachment (RD) repair, retinal break treatment or RD repair, glaucoma surgery, and cataract surgery. Results: Of the 206 807 patients with closed globe ocular trauma, 9648 underwent surgical intervention during the follow-up period (mean, 444 days): 1697 (0.8%) had RD repair, 1658 (0.8%) had retinal break treatment, 600 (0.3%) had glaucoma surgery, and 5693 (2.8%) had cataract surgery. Traumatic cataract was the strongest risk factor for cataract surgery (hazard ratio, 13.0; 95% confidence interval, 10.8-15.6), traumatic hyphema showed highest risk for glaucoma surgery (7.24; 4.60-11.4), and vitreous hemorrhage was the strongest risk factor for retinal break treatment and detachment repair (11.01; 9.18-13.2 and 14.2; 11.5-17.6, respectively) during the first 60 days after trauma date. Vitreous hemorrhage was a risk factor for cataract surgery at > 60 days after trauma date only. Iris-angle injury was the strongest risk factor for glaucoma surgery > 60 days after trauma, while vitreous hemorrhage remained the strongest factor for retinal break treatment and detachment repair at > 60 days. Traumatic hyphema was a risk factor for all surgical outcomes during all follow-up intervals. Conclusions: Diagnosis of concomitant traumatic cataract, vitreous hemorrhage, traumatic hyphema, and other risk factors may increase the likelihood of requiring surgical intervention after closed globe ocular trauma.

Directrices para los protocolos de ensayos clínicos de intervenciones con inteligencia artificial: la extensión SPIRIT-AI / Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension / Diretrizes para protocolos de ensaios clínicos com intervenções que utilizam inteligência artificial: a extensão SPIRIT-AI

resumen está disponible en el texto completo

ABSTRACT The SPIRIT 2013 statement aims to improve the completeness of clinical trial protocol reporting by providing evidence-based recommendations for the minimum set of items to be addressed. This guidance has been instrumental in promoting transparent evaluation of new interventions. More recently, there has been a growing recognition that interventions involving artificial intelligence (AI) need to undergo rigorous, prospective evaluation to demonstrate their impact on health outcomes. The SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials-Artificial Intelligence) extension is a new reporting guideline for clinical trial protocols evaluating interventions with an AI component. It was developed in parallel with its companion statement for trial reports: CONSORT-AI (Consolidated Standards of Reporting Trials-Artificial Intelligence). Both guidelines were developed through a staged consensus process involving literature review and expert consultation to generate 26 candidate items, which were consulted upon by an international multi-stakeholder group in a two-stage Delphi survey (103 stakeholders), agreed upon in a consensus meeting (31 stakeholders) and refined through a checklist pilot (34 participants). The SPIRIT-AI extension includes 15 new items that were considered sufficiently important for clinical trial protocols of AI interventions. These new items should be routinely reported in addition to the core SPIRIT 2013 items. SPIRIT-AI recommends that investigators provide clear descriptions of the AI intervention, including instructions and skills required for use, the setting in which the AI intervention will be integrated, considerations for the handling of input and output data, the human-AI interaction and analysis of error cases. SPIRIT-AI will help promote transparency and completeness for clinical trial protocols for AI interventions. Its use will assist editors and peer reviewers, as well as the general readership, to understand, interpret and critically appraise the design and risk of bias for a planned clinical trial.

RESUMO A declaração SPIRIT 2013 tem como objetivo melhorar a integralidade dos relatórios dos protocolos de ensaios clínicos, fornecendo recomendações baseadas em evidências para o conjunto mínimo de itens que devem ser abordados. Essas orientações têm sido fundamentais para promover uma avaliação transparente de novas intervenções. Recentemente, tem-se reconhecido cada vez mais que intervenções que incluem inteligência artificial (IA) precisam ser submetidas a uma avaliação rigorosa e prospectiva para demonstrar seus impactos sobre os resultados de saúde. A extensão SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials - Artificial Intelligence) é uma nova diretriz de relatório para protocolos de ensaios clínicos que avaliam intervenções com um componente de IA. Essa diretriz foi desenvolvida em paralelo à sua declaração complementar para relatórios de ensaios clínicos, CONSORT-AI (Consolidated Standards of Reporting Trials - Artificial Intelligence). Ambas as diretrizes foram desenvolvidas por meio de um processo de consenso em etapas que incluiu revisão da literatura e consultas a especialistas para gerar 26 itens candidatos. Foram feitas consultas sobre esses itens a um grupo internacional composto por 103 interessados diretos, que participaram de uma pesquisa Delphi em duas etapas. Chegou-se a um acordo sobre os itens em uma reunião de consenso que incluiu 31 interessados diretos, e os itens foram refinados por meio de uma lista de verificação piloto que envolveu 34 participantes. A extensão SPIRIT-AI inclui 15 itens novos que foram considerados suficientemente importantes para os protocolos de ensaios clínicos com intervenções que utilizam IA. Esses itens novos devem constar dos relatórios de rotina, juntamente com os itens básicos da SPIRIT 2013. A SPIRIT-AI preconiza que os pesquisadores descrevam claramente a intervenção de IA, incluindo instruções e as habilidades necessárias para seu uso, o contexto no qual a intervenção de IA será integrada, considerações sobre o manuseio dos dados de entrada e saída, a interação humano-IA e a análise de casos de erro. A SPIRIT-AI ajudará a promover a transparência e a integralidade nos protocolos de ensaios clínicos com intervenções que utilizam IA. Seu uso ajudará editores e revisores, bem como leitores em geral, a entender, interpretar e avaliar criticamente o delineamento e o risco de viés de um futuro estudo clínico.

Association of Environmental Factors with Age-Related Macular Degeneration using the Intelligent Research in Sight Registry.

Purpose: Investigate associations of natural environmental exposures with exudative and nonexudative age-related macular degeneration (AMD) across the United States. Design: Database study. Participants: Patients aged ≥ 55 years who were active in the IRIS Registry from 2016 to 2018 were analyzed. Patients were categorized as nonexudative, inactive exudative, and active exudative AMD by International Classification of Diseases 10th Revision and Current Procedural Terminology (CPT) codes. Patients without provider-level ZIP codes matching any ZIP code tabulation area were excluded. Methods: Environmental data were obtained from public sources including the US Geological Survey, National Renewable Energy Laboratory, National Oceanic and Atmospheric Administration, and Environmental Protection Agency. Multiple variable, mixed effects logistic regression models with random intercepts per ZIP code tabulation area quantified the association of each environmental variable with any AMD versus non-AMD patients, any exudative AMD versus nonexudative AMD, and active exudative AMD versus inactive exudative and nonexudative AMD using 3 separate models, while adjusting for age, sex, race, insurance type, smoking history, and phakic status. Main Outcome Measure: Odds ratios for environmental factors. Results: A total of 9 884 527 patients were included. Elevation, latitude, solar irradiance measured in global horizontal irradiance (GHI) and direct normal irradiance (DNI), temperature and precipitation variables, and pollution variables were included in our models. Statistically significant associations with active exudative AMD were GHI (odds ratio [OR], 3.848; 95% confidence interval [CI] with Bonferroni correction, 1.316-11.250), DNI (OR, 0.581; 95% CI, 0.370-0.913), latitude (OR, 1.110; 95% CI, 1.046-1.178), ozone (OR, 1.014; 95% CI, 1.004-1.025), and nitrogen dioxide (OR, 1.005; 95% CI, 1.000-1.010). The only significant environmental associations with any AMD were inches of snow in the winter (OR, 1.005; 95% CI, 1.001-1.009) and ozone (OR, 1.011; 95% CI, 1.003-1.019). Conclusions: The strongest environmental associations differed between AMD subgroups. The solar variables GHI, DNI, and latitude were significantly associated with active exudative AMD. Two pollutant variables, ozone and nitrogen dioxide, also showed positive associations with AMD. Further studies are warranted to investigate the clinical relevance of these associations. Our curated environmental dataset has been made publicly available at https://github.com/uw-biomedical-ml/AMD_environmental_dataset.

Machine Learning Prediction of Adenovirus D8 Conjunctivitis Complications from Viral Whole-Genome Sequence.

Objective: To obtain complete DNA sequences of adenoviral (AdV) D8 genome from patients with conjunctivitis and determine the relation of sequence variation to clinical outcomes. Design: This study is a post hoc analysis of banked conjunctival swab samples from the BAYnovation Study, a previously conducted, randomized controlled clinical trial for AdV conjunctivitis. Participants: Ninety-six patients with AdV D8-positive conjunctivitis who received placebo treatment in the BAYnovation Study were included in the study. Methods: DNA from conjunctival swabs was purified and subjected to whole-genome viral DNA sequencing. Adenovirus D8 variants were identified and correlated with clinical outcomes, including 2 machine learning methods. Main Outcome Measures: Viral DNA sequence and development of subepithelial infiltrates (SEIs) were the main outcome measures. Results: From initial sequencing of 80 AdV D8-positive samples, full adenoviral genome reconstructions were obtained for 71. A total of 630 single-nucleotide variants were identified, including 156 missense mutations. Sequence clustering revealed 3 previously unappreciated viral clades within the AdV D8 type. The likelihood of SEI development differed significantly between clades, ranging from 83% for Clade 1 to 46% for Clade 3. Genome-wide analysis of viral single-nucleotide polymorphisms failed to identify single-gene determinants of outcome. Two machine learning models were independently trained to predict clinical outcome using polymorphic sequences. Both machine learning models correctly predicted development of SEI outcomes in a newly sequenced validation set of 16 cases (P = 1.5 × 10-5). Prediction was dependent on ensemble groups of polymorphisms across multiple genes. Conclusions: Adenovirus D8 has ≥ 3 prevalent molecular substrains, which differ in propensity to result in SEIs. Development of SEIs can be accurately predicted from knowledge of full viral sequence. These results suggest that development of SEIs in AdV D8 conjunctivitis is largely attributable to pathologic viral sequence variants within the D8 type and establishes machine learning paradigms as a powerful technique for understanding viral pathogenicity.

Demographics and Seasonality of Retinal Detachment, Retinal Breaks, and Posterior Vitreous Detachment from the Intelligent Research in Sight Registry.

Purpose: To investigate the incidence, seasonal variation, and differences among age, sex, and race for rhegmatogenous retinal detachment (RRD) repair, retinal break (RB) treatment, and posterior vitreous detachment (PVD) in the Intelligent Research in Sight (IRIS) Registry. Design: Retrospective database study. Participants: Patients in the IRIS Registry who underwent RRD repair, RB treatment, or cataract surgery (CS) based on Current Procedural Terminology codes and PVD diagnosis based on International Classification of Diseases, Ninth and Tenth Revision, codes. Methods: Daily incidence rates were defined as the ratio of patients who underwent RRD repair or RB treatment and patients with a diagnosis of PVD to the total number of patients followed on a given day within the IRIS Registry. The CS group was included as a comparison for seasonal variation. Rates were stratified by decade of life, sex, and race. Main Outcome Measures: Time series trends for incidence rates of RRD, RB, and PVD. Results: A total of 7 115 774 patients received a diagnosis of incident PVD, 237 646 patients underwent RRD repair, and 359 022 patients underwent RB treatment. Also included were 5 940 448 patients who underwent CS. The mean daily incidence for RRD repair, RB treatment, PVD diagnosis, and CS were 0.46 per 100 000 patients, 0.70 per 100 000 patients, 13.90 per 100 000 patients, and 11.80 per 100 000 patients, respectively. Men showed higher incidence of RRD repair and RB treatment than women, whereas women showed higher incidence of PVD diagnosis. Rhegmatogenous retinal detachment incidence was higher in White people compared with other races. Seasonal decreases in PVD, RB treatment, RRD repair, and CS corresponded to national holidays, with larger decreases in winter months. Kaplan-Meier estimates showed that RRD repair and RB treatment typically occurred within 60 days of PVD diagnosis. Conclusions: Within the IRIS Registry, the highest incidence of RRD was in the 6th and 7th decade of life. There was a higher incidence of RRD repair and RB treatment in men compared with women. The seasonal variation associated with national holidays was less pronounced for RRD repair and RB treatment.

Deep Metagenomic Sequencing for Endophthalmitis Pathogen Detection Using a Nanopore Platform.

PURPOSE: To evaluate the utility of nanopore sequencing for identifying potential causative pathogens in endophthalmitis, comparing culture results against full-length 16S rRNA nanopore sequencing (16S Nanopore), whole genome nanopore sequencing (Nanopore WGS), and Illumina (Illumina WGS). DESIGN: Cross-sectional diagnostic comparison. METHODS: Patients with clinically suspected endophthalmitis underwent intraocular vitreous biopsy as per standard care. Clinical samples were cultured by conventional methods, together with full-length 16S rRNA and WGS using nanopore and Illumina sequencing platforms. RESULTS: Of 23 patients (median age 68.5 years [range 47-88]; 14 males [61%]), 18 cases were culture-positive. Nanopore sequencing identified the same cultured organism in all of the culture-positive cases and identified potential pathogens in two culture-negative cases (40%). Nanopore WGS was able to additionally detect the presence of bacteriophages in three samples. The agreements at genus level between culture and 16S Nanopore, Nanopore WGS, and Illumina WGS were 75%, 100%, and 78%, respectively. CONCLUSIONS: Whole genome sequencing has higher sensitivity and provides a viable alternative to culture and 16S sequencing for detecting potential pathogens in endophthalmitis. Moreover, WGS has the ability to detect other potential pathogens in culture-negative cases. Whilst Nanopore and Illumina WGS provide comparable data, nanopore sequencing provides potential for cost-effective point-of-care diagnostics.

Retinal Biomarkers for Alzheimer Disease: The Facts and the Future.

ABSTRACT: Alzheimer disease (AD) is a significant cause of morbidity and mortality worldwide, with limited treatment options and considerable diagnostic challenges. Identification and validation of retinal changes that correlate with clinicopathologic features of AD could provide a noninvasive method of screening and monitoring progression of disease, with notable implications for developing new therapies, particularly in its preclinical stages. Retinal biomarkers that have been studied to date include structural changes in neurosensory retinal layers, alterations in vascular architecture and function, and pathologic deposition of proteins within the retina, which have all demonstrated variable correlation with the presence of preclinical or clinical AD. Evolution of specialized retinal imaging modalities and advances in artificial intelligence hold great promise for future study in this burgeoning field. The current status of research in retinal biomarkers, and some of the challenges that will need to be addressed in future work, are reviewed herein.

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.