The Sources of Researcher Variation in Economics

SSRN Electronic Journal · 2025-01-01 · 1 citations

Comparison of a Novel Ultra-Widefield Three-Color Scanning Laser Ophthalmoscope to Other Retinal Imaging Modalities in Chorioretinal Lesion Imaging

Translational Vision Science & Technology · 2025-01-13 · 6 citations

Purpose: To compare the assessment of clinically relevant retinal and choroidal lesions as well as optic nerve pathologies using a novel three-wavelength ultra-widefield (UWF) scanning laser ophthalmoscope with established retinal imaging techniques for ophthalmoscopic imaging. Methods: Eighty eyes with a variety of retinal and choroidal lesions were assessed on the same time point using Topcon color fundus photography (CFP) montage, Optos red/green (RG), Heidelberg SPECTRALIS MultiColor 55-color montage (MCI), and novel Optos red/green/blue (RGB). Paired images of the optic nerve, retinal, or choroidal lesions were initially diagnosed based on CFP imaging. The accuracy of the imaging was then evaluated in comparison to CFP using a grading scale ranging from -1 (losing imaging information) to +1 (gaining imaging information). Results: Eighty eyes of 43 patients with 116 retinal or choroidal pathologies, as well as 59 eyes with optic nerve imaging using CFP, MCI, RG, and RGB, were included in this study. Across all subgroups, RGB provided significantly more accurate clinical imaging with CFP as ground truth and compared to other modalities. This was true comparing RGB to both RG (P = 0.0225) and MCI (P < 0.001) overall. Although RGB provided more accurate clinical information overall, it was inferior to RG for melanocytic choroidal lesions (P = 0.011). Conclusions: RGB can be considered as a useful tool to detect characteristics of central, midperipheral, and peripheral retinal lesions. Regarding melanocytic choroidal lesions, RGB was inferior to RG, and MCI was inferior to both RG and RGB modalities due to color changes. Translational Relevance: Traditional retinal ultra-widefield imaging uses two wavelengths. Here, we evaluated three wavelengths for ultra-widefield imaging. We examined new optics (basic science) effect on patient imaging (clinical care).

Retinal nerve fibre layer optical texture analysis: retinal nerve fibre bundle defect patterns and the extent of macular involvement across different stages of glaucoma

British Journal of Ophthalmology · 2025-11-04 · 2 citations

BACKGROUND/AIMS: To apply retinal nerve fibre layer (RNFL) optical texture analysis (ROTA) to investigate (1) the patterns of RNFL bundle defects, and (2) the frequency of papillomacular and papillofoveal bundle involvement across early, moderate and advanced glaucoma. METHODS: All eyes underwent 24-2 visual field (VF) testing and optical coherence tomography (OCT) for ROTA. The borders of RNFL defects were delineated from ROTA, and the involvement of the arcuate, papillomacular and papillofoveal bundles was determined for each eye. 24-2 VF stimulus projections were mapped onto the corresponding topographic areas of ROTA images. Multilevel logistic regression analysis was applied to evaluate the structure-function association. RESULTS: Papillomacular bundle defects were highly prevalent in glaucoma, increasing from 87.7% in early to 95.35% in moderate and 100% in advanced glaucoma. Papillofoveal bundle defects were also common, increasing from 29.7% in early to 36.05% in moderate and 60.98% in advanced glaucoma. Central four 24-2 test locations that projected onto the trajectories of papillomacular or papillofoveal RNFL bundle defects demonstrated significantly increased likelihood of VF sensitivity abnormality (ORs of 22.42 at PDP<5% and 20.26 at TDP<5%, respectively, p<0.001 for both). CONCLUSION: ROTA uncovers a wide spectrum of RNFL bundle defects spanning the entire glaucoma continuum. It also provides visualisation of the preserved RNFL bundles in advanced glaucoma. Papillomacular and papillofoveal RNFL bundle defects are present in a considerable proportion of eyes with early, moderate and advanced glaucoma, and, when detected, they significantly increase the likelihood of abnormality in the corresponding central 24-2 test locations.

Evaluation and Optimization of Diabetic Retinopathy Screenings for Uninsured Latinx Patients in a Resource-Limited Student-Run Free Clinic

Journal of Student-Run Clinics · 2024-01-18 · 2 citations

Background: Diabetic retinopathy (DR) is a sight-threatening condition that causes progressive retina damage. Student-run free clinics represent a valuable opportunity to provide DR screenings to high-risk populations. We characterized the patient population, evaluated the performance, and conducted a needs assessment of DR screenings at the University of California, San Diego Student-Run Ophthalmology Free Clinic, which provides care to predominantly uninsured, Latino patients. Methods: Retrospective chart review was conducted of all patients seen at the free clinic since 2019 with a diagnosis of type II diabetes. Date and outcome of all DR-related screenings or visits from 2015 onward, demographics information, and DR risk factors such as A1c and insulin dependence were recorded. Predictors of diabetic retinopathy and frequency of DR screenings for each patient were analyzed using multiple logistic regression, t-test for equality of means, and Pearson's correlation. Results: Of 179 uninsured diabetic patients receiving care at the free clinic, 71% were female and average age was 59. 83% had hypertension, 93% had hyperlipidemia, and 79% had metabolic syndrome. Prevalence of non-proliferative DR was 34% and that of proliferative DR was 15% in diabetic patients. The free clinic capacity in recent years plateaued at just under 50% of patients seen for DR screening or visit per year, though average wait time was over 2 years between visits. Patients with higher no-show rates had less frequent DR screenings. Chronic kidney disease and poor glycemic control were the strongest predictors of DR. Conclusion: The student-run free ophthalmology clinic has been effective in providing screening and follow-up care for DR patients. Creation of a protocol to identify which patients are at highest risk of DR and should be seen more urgently, addressing no-shows, and implementation of a tele-retina program are potential avenues for improving clinic efficiency in a resource-limited setting for vulnerable populations.

Analysis of ChatGPT Responses to Ophthalmic Cases: Can ChatGPT Think like an Ophthalmologist?

Ophthalmology Science · 2024-08-23 · 35 citations

Objective: Large language models such as ChatGPT have demonstrated significant potential in questionanswering within ophthalmology, but there is a paucity of literature evaluating its ability to generate clinical assessments and discussions.The objectives of this study were to (1) assess the accuracy of assessment and plans generated by ChatGPT and (2) evaluate ophthalmologists' abilities to distinguish between responses generated by clinicians versus ChatGPT.Design: Cross-sectional mixed-methods study.Subjects: Sixteen ophthalmologists from a single academic center, of which 10 were board-eligible and 6 were board-certified, were recruited to participate in this study.Methods: Prompt engineering was used to ensure ChatGPT output discussions in the style of the ophthalmologist author of the Medical College of Wisconsin Ophthalmic Case Studies.Cases where ChatGPT accurately identified the primary diagnoses were included and then paired.Masked human-generated and ChatGPT-generated discussions were sent to participating ophthalmologists to identify the author of the discussions.Response confidence was assessed using a 5-point Likert scale score, and subjective feedback was manually reviewed.Main Outcome Measures: Accuracy of ophthalmologist identification of discussion author, as well as subjective perceptions of human-generated versus ChatGPT-generated discussions.Results: Overall, ChatGPT correctly identified the primary diagnosis in 15 of 17 (88.2%)cases.Two cases were excluded from the paired comparison due to hallucinations or fabrications of nonuser-provided data.Ophthalmologists correctly identified the author in 77.9% AE 26.6% of the 13 included cases, with a mean Likert scale confidence rating of 3.6 AE 1.0.No significant differences in performance or confidence were found between board-certified and board-eligible ophthalmologists.Subjectively, ophthalmologists found that discussions written by ChatGPT tended to have more generic responses, irrelevant information, hallucinated more frequently, and had distinct syntactic patterns (all P < 0.01).Conclusions: Large language models have the potential to synthesize clinical data and generate ophthalmic discussions.While these findings have exciting implications for artificial intelligence-assisted health care delivery, more rigorous real-world evaluation of these models is necessary before clinical deployment.

Comparison of the Effects of Latanoprostene Bunod and Timolol on Retinal Blood Vessel Density: A Randomized Clinical Trial

American Journal of Ophthalmology · 2022-05-06 · 16 citations

Racial and Sociodemographic Disparities in the Detection of Narrow Angles before Detection of Primary Angle-Closure Glaucoma in the United States

Ophthalmology Glaucoma · 2022-01-25 · 19 citations

Rates and Patterns of Diagnostic Conversion from Anatomical Narrow Angle to Primary Angle-Closure Glaucoma in the United States

Ophthalmology Glaucoma · 2022-09-02 · 13 citations

Detection of Nonglaucomatous Macula Findings With Ganglion Cell Analysis Printouts vs Full Macular Cube Scans

JAMA Ophthalmology · 2022-09-08 · 1 citations

Importance: Ganglion cell analysis (GCA) of ocular coherence tomography (OCT) imaging is routinely used to detect and monitor glaucomatous damage of the ganglion cell complex in the macula. The GCA printout provides qualitative and quantitative data about the macular ganglion cell-inner plexiform layer and a single B-scan of the retina through the fovea. However, the full macular cube scan, including all 128 B-scans, is available for review. The macular cube scan provides considerable information about nonglaucomatous ocular pathology that may be missed if clinicians review only the GCA printout. Objective: To determine the frequency and type of nonglaucomatous macular findings that are observable in the full macular cube scan but not the GCA printout. Design, Setting, and Participants: A retrospective cross-sectional analysis of GCA printouts and full macular cube scans to detect nonglaucomatous macular pathology at a tertiary care academic center. Consecutive patients undergoing ganglion cell complex imaging during routine glaucoma evaluations over a 1-week period in a multi-clinician glaucoma clinic. Main Outcomes and Measures: The prevalence and type of nonglaucomatous macular pathology visible on the GCA printout or macular cube scan. Results: Among 105 patients (mean (SD) age, 67 (15.46) years; 63 [60%] female and 42 [40%] male) 201 eyes were imaged (64 [31.7%] with suspected glaucoma, 126 [62.4%] with open-angle glaucoma, 6 [3.0%] with closed-angle glaucoma, and 6 [3.0%] with other glaucoma). GCA printouts and macular cube scans revealed nonglaucomatous macular pathology in 65 eyes (32.2%). Of these, 25 eyes (38.5%) included findings that were not visible on the GCA printout. Of the cases not visible on the printout, 16 eyes (64.0% ) included macular pathology that required further evaluation. Conclusions and Relevance: The findings indicate that nonglaucomatous macular pathology may be missed based on GCA printouts alone. While it may be beneficial to review the full macular cube to detect potentially vision-threatening disease and ensure proper patient care, this study cannot determine if this missed pathology affects clinical outcomes.

Usability and Clinician Acceptance of a Deep Learning-Based Clinical Decision Support Tool for Predicting Glaucomatous Visual Field Progression

Journal of Glaucoma · 2022-12-21 · 14 citations

PRCIS: We updated a clinical decision support tool integrating predicted visual field (VF) metrics from an artificial intelligence model and assessed clinician perceptions of the predicted VF metric in this usability study. PURPOSE: To evaluate clinician perceptions of a prototyped clinical decision support (CDS) tool that integrates visual field (VF) metric predictions from artificial intelligence (AI) models. METHODS: Ten ophthalmologists and optometrists from the University of California San Diego participated in 6 cases from 6 patients, consisting of 11 eyes, uploaded to a CDS tool ("GLANCE", designed to help clinicians "at a glance"). For each case, clinicians answered questions about management recommendations and attitudes towards GLANCE, particularly regarding the utility and trustworthiness of the AI-predicted VF metrics and willingness to decrease VF testing frequency. MAIN OUTCOMES AND MEASURES: Mean counts of management recommendations and mean Likert scale scores were calculated to assess overall management trends and attitudes towards the CDS tool for each case. In addition, system usability scale scores were calculated. RESULTS: The mean Likert scores for trust in and utility of the predicted VF metric and clinician willingness to decrease VF testing frequency were 3.27, 3.42, and 2.64, respectively (1=strongly disagree, 5=strongly agree). When stratified by glaucoma severity, all mean Likert scores decreased as severity increased. The system usability scale score across all responders was 66.1±16.0 (43rd percentile). CONCLUSIONS: A CDS tool can be designed to present AI model outputs in a useful, trustworthy manner that clinicians are generally willing to integrate into their clinical decision-making. Future work is needed to understand how to best develop explainable and trustworthy CDS tools integrating AI before clinical deployment.