Implementation of deep learning artificial intelligence in vision-threatening disease screenings for an underserved community during COVID-19.

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 ± 13.3 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.

Bibliometric Analysis of the Top 100 Cited Articles in Pediatric Ophthalmology.

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. 2023;60(5):330-336.].

Cycloplegic Autorefraction as a Substitute for Cycloplegic Retinoscopy in the Pediatric Population.

PURPOSE: To evaluate whether cycloplegic autorefraction can provide similar results as cycloplegic retinoscopy, allowing more comprehensive ophthalmologists to be comfortable in managing pediatric refractive error and refractive amblyopia. METHODS: This retrospective chart review was performed to determine the mean difference in sphere, cylinder, and axis between cycloplegic autorefraction and retinoscopy, both of which were obtained on the same eye at least 30 minutes after cycloplegia and dilation with a mixed solution of tropicamide, cyclopentolate, and phenylephrine. RESULTS: A total of 34 eyes (18 right, 16 left) from 18 patients were included in the analysis. Mean sphere difference between cycloplegic autorefraction and retinoscopy was 0.044 ± 0.278 diopters (D) (95% CI: -1.275 to 1.363 D), mean cylinder difference was -0.081 ± 0.236 D (95% CI: -0.706 to 0.544 D), and mean axis difference was 7.059 ± 19.676 degrees (95% CI: -32.527 to 38.878 degrees). Mean differences in sphere, cylinder, and axis were not statistically significant (P = .362, .0541, and .377, respectively). CONCLUSIONS: In this small sample population, cycloplegic autorefraction was comparable to cycloplegic retinoscopy. Recognition of amblyopia should still prompt evaluation by a pediatric ophthalmologist. Further research is necessary to confirm whether uncomplicated refractive error in children may be sufficiently detected and managed by a comprehensive ophthalmologist. [J Pediatr Ophthalmol Strabismus. 2022:59(6):422-427.].

Smart Manufacturing.

Historic manufacturing enterprises based on vertically optimized companies, practices, market share, and competitiveness are giving way to enterprises that are responsive across an entire value chain to demand dynamic markets and customized product value adds; increased expectations for environmental sustainability, reduced energy usage, and zero incidents; and faster technology and product adoption. Agile innovation and manufacturing combined with radically increased productivity become engines for competitiveness and reinvestment, not simply for decreased cost. A focus on agility, productivity, energy, and environmental sustainability produces opportunities that are far beyond reducing market volatility. Agility directly impacts innovation, time-to-market, and faster, broader exploration of the trade space. These changes, the forces driving them, and new network-based information technologies offering unprecedented insights and analysis are motivating the advent of smart manufacturing and new information technology infrastructure for manufacturing.