ABSTRACT
INTRODUCTION: Age-related macular degeneration, diabetic retinopathy, and glaucoma are vision-threatening diseases that are leading causes of vision loss. Many studies have validated deep learning artificial intelligence for image-based diagnosis of vision-threatening diseases. Our study prospectively investigated deep learning artificial intelligence applications in student-run non-mydriatic screenings for an underserved, primarily Hispanic community during COVID-19. METHODS: Five supervised student-run community screenings were held in West New York, New Jersey. Participants underwent non-mydriatic 45-degree retinal imaging by medical students. Images were uploaded to a cloud-based deep learning artificial intelligence for vision-threatening disease referral. An on-site tele-ophthalmology grader and remote clinical ophthalmologist graded images, with adjudication by a senior ophthalmologist to establish the gold standard diagnosis, which was used to assess the performance of deep learning artificial intelligence. RESULTS: A total of 385 eyes from 195 screening participants were included (mean age 52.43 ± 14.5 years, 40.0% female). A total of 48 participants were referred for at least one vision-threatening disease. Deep learning artificial intelligence marked 150/385 (38.9%) eyes as ungradable, compared to 10/385 (2.6%) ungradable as per the human gold standard (p < 0.001). Deep learning artificial intelligence had 63.2% sensitivity, 94.5% specificity, 32.0% positive predictive value, and 98.4% negative predictive value in vision-threatening disease referrals. Deep learning artificial intelligence successfully referred all 4 eyes with multiple vision-threatening diseases. Deep learning artificial intelligence graded images (35.6 ± 13.3â s) faster than the tele-ophthalmology grader (129 ± 41.0) and clinical ophthalmologist (68 ± 21.9, p < 0.001). DISCUSSION: Deep learning artificial intelligence can increase the efficiency and accessibility of vision-threatening disease screenings, particularly in underserved communities. Deep learning artificial intelligence should be adaptable to different environments. Consideration should be given to how deep learning artificial intelligence can best be utilized in a real-world application, whether in computer-aided or autonomous diagnosis.
ABSTRACT
PURPOSE: To determine and analyze the 100 most cited articles in pediatric ophthalmology. METHODS: A literature search was conducted using the ISI Web of Science database on the top 100 most cited articles in pediatric ophthalmology. RESULTS: The 100 most cited articles were published between 1941 and 2018, with the greatest number published in both 2005 and 2012. A total of 29,731 citations were generated during the study period. There has been a significant increase in citations annually since 1941, with a peak number of citations in 2021 with 2,629 citations. Myopia, retinopathy of prematurity, and other forms of refractive error were the topics most studied and cited in these articles. Most of the articles were classified as either large cohort prospective/retrospective studies (34) or randomized clinical trials (19), with case reports/series being the least frequent (7). Investigative Ophthalmology & Visual Science (23), JAMA Ophthalmology (22), and Ophthalmology (22) published the majority of the articles. Institutions that conducted the majority of the studies presented include the National Eye Institute (10), the Ohio State University College of Optometry (9), and the Oregon Health & Science University (6). CONCLUSIONS: This bibliometric analysis provides a unique historical perspective of the literature in the field of pediatric ophthalmology that has not been studied before. The research in the field of pediatric ophthalmology is advancing quickly, with most articles and citations occurring within the past 15 years. The strong focus on prospective cohort studies and clinical trials reveals the importance of advancing the treatment of critical disease within the field of pediatric ophthalmology. [J Pediatr Ophthalmol Strabismus. 20XX;X(X):XX-XX.].