Faculty Bibliography

PURPOSE:Cost-effectiveness analyses (CEAs) quantify and compare both costs and measures of efficacy for different interventions. As the costs of glaucoma management to patients, payers, and physicians are increasing, we seek to investigate the role of CEAs in the field of glaucoma and how such studies impact clinical management. METHODS:We adhered to the "Preferred Reporting Items for Systematic Reviews and Meta-analyses" guidelines for our systematic review structure. Eligible studies included any full-text articles that investigated cost-effectiveness or cost-utility as it relates to the field of open angle glaucoma management in the United States. Risk of bias assessment was conducted using the validated Joanna Briggs Institute Critical Appraisal Checklist for Economic Evaluations. RESULTS:Eighteen studies were included in the review. Dates of publication ranged from 1983 to 2021. Most of the studies were published in the 2000s and performed CEAs in the domains of treatment/therapy, screening, and adherence for patients with primary angle open glaucoma. Of the 18 articles included, 14 focused on treatment, 2 on screening, and 2 on adherence. Most of these studies focused on the cost-effectiveness of different topical medical therapies, whereas only a few studies explored laser procedures, surgical interventions, or minimally invasive procedures. Economic models using decision analysis incorporating state-transition Markov cycles or Montecarlo simulations were widely used, however, the methodology among studies was variable, with a wide spectrum of inputs, measures of outcomes, and time horizons used. CONCLUSION:Overall, we found that cost-effectiveness research in glaucoma in the United States remains relatively unstructured, resulting in unclear and conflicting implications for clinical management.

Artificial intelligence and machine learning applications are becoming increasingly popular in health care and medical devices. The development of accurate machine learning algorithms requires large quantities of good and diverse data. This poses a challenge in health care because of the sensitive nature of sharing patient data. Decentralized algorithms through federated learning avoid data aggregation. In this paper we give an overview of federated learning, current examples in healthcare and ophthalmology, challenges, and next steps.

BACKGROUND:The development of accurate machine learning algorithms requires sufficient quantities of diverse data. This poses a challenge in health care because of the sensitive and siloed nature of biomedical information. Decentralized algorithms through federated learning (FL) avoid data aggregation by instead distributing algorithms to the data before centrally updating one global model. OBJECTIVE:To establish a multicenter collaboration and assess the feasibility of using FL to train machine learning models for intracranial hemorrhage (ICH) detection without sharing data between sites. METHODS:Five neurosurgery departments across the United States collaborated to establish a federated network and train a convolutional neural network to detect ICH on computed tomography scans. The global FL model was benchmarked against a standard, centrally trained model using a held-out data set and was compared against locally trained models using site data. RESULTS:A federated network of practicing neurosurgeon scientists was successfully initiated to train a model for predicting ICH. The FL model achieved an area under the ROC curve of 0.9487 (95% CI 0.9471-0.9503) when predicting all subtypes of ICH compared with a benchmark (non-FL) area under the ROC curve of 0.9753 (95% CI 0.9742-0.9764), although performance varied by subtype. The FL model consistently achieved top three performance when validated on any site's data, suggesting improved generalizability. A qualitative survey described the experience of participants in the federated network. CONCLUSION/CONCLUSIONS:This study demonstrates the feasibility of implementing a federated network for multi-institutional collaboration among clinicians and using FL to conduct machine learning research, thereby opening a new paradigm for neurosurgical collaboration.

Diagnosis and detection of progression of glaucoma remains challenging. Artificial intelligence-based tools have the potential to improve and standardize the assessment of glaucoma but development of these algorithms is difficult given the multimodal and variable nature of the diagnosis. Currently, most algorithms are focused on a single imaging modality, specifically screening and diagnosis based on fundus photos or optical coherence tomography images. Use of anterior segment optical coherence tomography and goniophotographs is limited. The majority of algorithms designed for disease progression prediction are based on visual fields. No studies in our literature search assessed the use of artificial intelligence for treatment response prediction and no studies conducted prospective testing of their algorithms. Additional challenges to the development of artificial intelligence-based tools include scarcity of data and a lack of consensus in diagnostic criteria. Although research in the use of artificial intelligence for glaucoma is promising, additional work is needed to develop clinically usable tools.

OBJECTIVE:Our aim was to assess the acceptability and feasibility of iCare HOME tonometer (HT) and Virtual Field (VF) devices in the home monitoring of glaucoma. DESIGN/METHODS:Prospective feasibility and acceptability study. SUBJECTS/METHODS:Twenty patients (39 eyes) with primary open-angle glaucoma, open-angle glaucoma, ocular hypertension, or suspected glaucoma. METHODS:Patients were trained and instructed to bring 2 devices home for 1 week and use the HT 4 times/day for 4 days and the VF 3 times total. MAIN OUTCOME MEASURES/METHODS:For acceptability, we conducted satisfaction surveys and semistructured, qualitative interviews with a thematic analysis. Feasibility was assessed by device usage and quality of tests. RESULTS:Most patients (73.7%) felt that the HT was easy to use, and 100% of them found the HT useful. All patients (100%) felt that VF was easy to use, and 94.4% of them found the VF useful. All patients (100%) obtained acceptable intraocular pressure and completed a VF test at home. We identified 4 key themes, with 33 subthemes. The key themes include the following: (1) advantages of home monitoring; (2) difficulties with home monitoring; (3) future considerations in home monitoring; and (4) the experience of patients with glaucoma. CONCLUSIONS:The HT and VF were acceptable and feasible in a small cohort of motivated subjects. Patients were able to perform these tests proficiently at home, and they were generally enthused to obtain more data about their intraocular health, as it allowed them a heightened sense of security and insight about their chronic disease, as well as a reduction in foreseeable barriers to care. Home monitoring may also improve upon glaucoma care by enhancing patient empowerment and fostering community bonds. The VF should be further evaluated to ensure validity.

Purpose/UNASSIGNED:To validate and assess user satisfaction and usability of the New York University (NYU) Langone Eye Test application, a smartphone-based visual acuity (VA) test. Design/UNASSIGNED:Mixed-methods cross-sectional cohort study. Participants/UNASSIGNED:Two hundred forty-four eyes of 125 participants were included. All participants were adults 18 years of age or older. Participants' eyes with a VA of 20/400 (1.3 logarithm of the minimum angle of resolution [logMAR]) or worse were excluded. Methods/UNASSIGNED:Patients were tested using the clinical standard Rosenbaum near card and the NYU Langone Eye Test application on an iPhone and Android device. Each test was performed twice to measure reliability. Ten patients were selected randomly for subsequent semistructured qualitative interviews with thematic analysis. Main Outcome Measures/UNASSIGNED:Visual acuity was the parameter measured. Bland-Altman analysis was used to measure agreement between the results of the NYU Langone Eye Test application and Rosenbaum card, as well as test-retest reliability of each VA. The correlation between results was calculated using the intraclass correlation coefficient. Satisfaction survey and semistructured interview questions were developed to measure usability and acceptability. Results/UNASSIGNED:Bland-Altman analysis revealed an agreement between the application and the Rosenbaum near card of 0.017 ± 0.28 logMAR (iPhone) and 0.009 ± 0.29 logMAR (Android). The correlation between the application and the Rosenbaum near card was 0.74 for both the iPhone and Android. Test-retest reliability was 0.003 ± 0.22 logMAR (iPhone), 0.01 ± 0.25 logMAR (Android), and 0.01 ± 0.23 logMAR (Rosenbaum card). Of the 125 participants, 97.6% found the application easy to use, and 94.3% were overall satisfied with the application. Thematic analysis yielded 6 key themes: (1) weaknesses of application, (2) benefits of the application, (3) tips for application improvement, (4) difficulties faced while using the application, (5) ideal patient for application, and (6) comparing application with traditional VA testing. Conclusions/UNASSIGNED:The NYU Langone Eye Test application is a user-friendly, accurate, and reliable measure of near VA. The application's integration with the electronic health record, accessibility, and easy interpretation of results, among other features, make it ideal for telemedicine use.

PURPOSE/OBJECTIVE:Traditional ERGs recorded using corneal electrodes can be difficult for some patients to tolerate. In the last several years, adhesive skin electrodes have gained in acceptance. In this report we present a qualitative comparison of waveforms as well as a quantitative analysis of correlation of amplitudes and implicit times of simultaneous ERG recordings using contact lens and skin electrodes. METHODS:89 subjects were included; all were referred for full-field ERG testing for multiple indications. ERGs (obtained according to ISCEV standards) were recorded simultaneously from both eyes with ERG-jet corneal contact lens electrodes and LKC Technologies Sensor Strip adhesive skin electrodes using multi-channel instrumentation (Diagnosys LLC, Espion3). Waveforms, a-wave and b-wave amplitudes and implicit times were compared. RESULTS:Waveform morphologies were similar between electrode types. Regression coefficients (conversion factors) for a-wave and b-wave amplitudes under both photopic and scotopic conditions were tightly clustered. Regression coefficients for implicit times were nearly equal to 1.0. The regression coefficient for the entire amplitude dataset was 0.349, with an overall correlation of 0. 869 between amplitude recorded with skin and contact lens electrodes. The regression coefficient for the entire implicit time dataset was 0.967, with an overall correlation of 0.964 between skin and contact lens electrodes. CONCLUSIONS:Our best estimate for the conversion factor between ERG amplitudes recorded with adhesive skin electrodes and contact lens electrodes is 0.349-amplitudes with skin electrodes are about 1/3 the amplitudes recorded simultaneously from the same eyes with contact lens electrodes, with a high correlation. Implicit times are nearly identical for the two electrode types.