Acceptance and Perception of Artificial Intelligence Usability in Eye Care (APPRAISE) for Ophthalmologists: A Multinational Perspective

Background: Many artificial intelligence (AI) studies have focused on development of AI models, novel techniques, and reporting guidelines. However, little is understood about clinicians' perspectives of AI applications in medical fields including ophthalmology, particularly in light of recent regulatory guidelines. The aim for this study was to evaluate the perspectives of ophthalmologists regarding AI in 4 major eye conditions: diabetic retinopathy (DR), glaucoma, age-related macular degeneration (AMD) and cataract. Methods: This was a multi-national survey of ophthalmologists between March 1st, 2020 to February 29th, 2021 disseminated via the major global ophthalmology societies. The survey was designed based on microsystem, mesosystem and macrosystem questions, and the software as a medical device (SaMD) regulatory framework chaired by the Food and Drug Administration (FDA). Factors associated with AI adoption for ophthalmology analyzed with multivariable logistic regression random forest machine learning. Results: One thousand one hundred seventy-six ophthalmologists from 70 countries participated with a response rate ranging from 78.8 to 85.8% per question. Ophthalmologists were more willing to use AI as clinical assistive tools (88.1%, n = 890/1,010) especially those with over 20 years' experience (OR 3.70, 95% CI: 1.10–12.5, p = 0.035), as compared to clinical decision support tools (78.8%, n = 796/1,010) or diagnostic tools (64.5%, n = 651). A majority of Ophthalmologists felt that AI is most relevant to DR (78.2%), followed by glaucoma (70.7%), AMD (66.8%), and cataract (51.4%) detection. Many participants were confident their roles will not be replaced (68.2%, n = 632/927), and felt COVID-19 catalyzed willingness to adopt AI (80.9%, n = 750/927). Common barriers to implementation include medical liability from errors (72.5%, n = 672/927) whereas enablers include improving access (94.5%, n = 876/927). Machine learning modeling predicted acceptance from participant demographics with moderate to high accuracy, and area under the receiver operating curves of 0.63–0.83. Conclusion: Ophthalmologists are receptive to adopting AI as assistive tools for DR, glaucoma, and AMD. Furthermore, ML is a useful method that can be applied to evaluate predictive factors on clinical qualitative questionnaires. This study outlines actionable insights for future research and facilitation interventions to drive adoption and operationalization of AI tools for Ophthalmology

Longitudinal Changes of Fixation Location and Stability Within 12 Months in Stargardt Disease: ProgStar Report No. 12

Purpose: To investigate the natural history of Stargardt disease (STGD1) using fixation location and fixation stability. // Design: Multicenter, international, prospective cohort study. // Methods: Fixation testing was performed using the Nidek MP-1 microperimeter as part of the prospective, multicenter, natural history study on the Progression of Stargardt disease (ProgStar). A total of 238 patients with ABCA4-related STGD1 were enrolled at baseline (bilateral enrollment in 86.6%) and underwent repeat testing at months 6 and 12. // Results: Outcome measures included the distance of the preferred retinal locus from the fovea (PRL) and the bivariate contour ellipse area (BCEA). After 12 months of follow-up, the change in the eccentricity of the PRL from the anatomic fovea was −0.0014 degrees (95% confidence interval [CI], −0.27 degrees, 0.27 degrees; P = .99). The deterioration in the stability of fixation as expressed by a larger BCEA encompassing 1 standard deviation of all fixation points was 1.21 degrees squared (deg2) (95% CI, −1.23 deg2, 3.65 deg2; P = .33). Eyes with increases and decreases in PRL eccentricity and/or BCEA values were observed. // Conclusions: Our observations point to the complexity of fixation parameters. The association of increasingly eccentric and unstable fixation with longer disease duration that is typically found in cross-sectional studies may be countered within individual patients by poorly understood processes like neuronal adaptation. Nevertheless, fixation parameters may serve as useful secondary outcome parameters in selected cases and for counseling patients to explain changes to their visual functionality

Macular sensitivity measured with microperimetry in stargardt disease in the progression of atrophy secondary to stargardt disease (ProgStar) study report No. 7

IMPORTANCE: New outcome measures for treatment trials for Stargardt disease type 1 (STGD1) and other macular diseases are needed. Microperimetry allows mapping of light sensitivity of the macula and provides topographic information on visual function beyond visual acuity. OBJECTIVE: To measure and analyze retinal light sensitivity of the macula in STGD1 using fundus-controlled perimetry (microperimetry). DESIGN, SETTING, AND PARTICIPANTS: Thiswas a multicenter prospective cohort study. A total of 199 patients and 326 eyes with molecularly confirmed (ABCA4) STGD1 underwent testing with the Nidek MP-1 microperimeter as part of the multicenter, prospective Natural History of the Progression of Atrophy Secondary to Stargardt Disease (ProgStar) study. Sensitivity of 68 retinal loci was tested, and the mean sensitivity (MS) was determined; each point was categorized as "normal," "relative," or "deep" scotoma. MAIN OUTCOMES AND MEASURES: Mean sensitivity and the number of points with normal sensitivity, relative, or deep scotomas. RESULTS: Mean (SD) patient age was 34.2 (14.7) years, mean (SD) best-corrected visual acuity of all eyes was 47.8 (16.9) Early Treatment Diabetic Retinopathy Study letter score (approximately 20/100 Snellen equivalent), and mean MS of all eyes of all 68 points was 11.0 (5.0) dB. The median number of normal points per eye was 49 (mean [SD], 41.3 [20.8] ; range, 0-68); abnormal sensitivity and deep scotomas were more prevalent in the central macula. Mean sensitivity was lower in the fovea (mean [SD], 2.7 [4.4] dB) than in the inner (mean [SD], 6.8 [5.8] dB) and outer ring (mean [SD], 12.7 [5.3] dB). Overall MS per eye was 0.086 dB lower per year of additional age (95%CI, -0.13 to -0.041; P < .001) and 0.21 dB lower per additional year of duration of STGD1 (95%CI, -0.28 to -0.14; P < .001). Longer duration of STGD1 was associated with worse MS (β = -0.18; P < .001), with a lower number of normal test points (β = -0.71; P < .001), and with a higher number of deep scotoma points (β = -0.70; P < .001).We found 11 eyes with lowMS ( < 6 dB) but very good best-corrected visual acuity of at least 72 Early Treatment Diabetic Retinopathy Study letter score (20/40 Snellen equivalent). CONCLUSIONS AND RELEVANCE: We provide an extensive analysis of macular sensitivity parameters in STGD1 and demonstrate their association with demographic characteristics and vision. These data suggest microperimetry testing provides a more comprehensive assessment of retinal function and will be an important outcome measure in future clinical trials