Ophthalmology Optical Coherence Tomography Databases for Artificial Intelligence Algorithm: A Review.

BACKGROUND: Imaging plays a pivotal role in eye assessment. With the introduction of advanced machine learning and artificial intelligence (AI), the focus has shifted to imaging datasets in ophthalmology. While disparities and health inequalities hidden within data are well-documented, the ophthalmology field faces specific challenges to the creation and maintenance of datasets. Optical Coherence Tomography (OCT) is useful for the diagnosis and monitoring of retinal pathologies, making it valuable for AI applications. This review aims to identify and compare the landscape of publicly available optical coherence tomography databases for AI applications. METHODS: We conducted a literature review on OCT and AI articles with publicly accessible datasets, using PubMed, Scopus, and Web of Science databases. The review retrieved 183 articles, and after full-text analysis, 50 articles were included. From the included articles were identified 8 publicly available OCT datasets, focusing on patient demographics and clinical details for thorough assessment and comparison. RESULTS: The resulting datasets encompass 154,313 images collected from Spectralis, Cirrus HD, Topcon 3D, and Bioptigen devices. These datasets included normal exams, age-related macular degeneration, and diabetic maculopathy, among others. Comprehensive demographic information is available in one dataset and the USA is the most represented population. DISCUSSION: Current publicly available OCT databases for AI applications exhibit limitations, stemming from their non-representative nature and the lack of comprehensive demographic information. Limited datasets hamper research and equitable AI development. To promote equitable AI algorithmic development in ophthalmology, there is a need for the creation and dissemination of more representative datasets.

Association between leptospirosis-related knowledge and practices of male pedicab drivers in Manila

OBJECTIVE: The study aimed to determine whether or not an association exists between leptospirosis-related knowledge and the practices of pedicab drivers in Manila. METHODS: An analytic, cross-sectional study was done among 174 male pedicab drivers. Face-to-face interviews were conducted using an interview schedule containing 12 questions covering knowledge of transmission and prevention, and 8 questions covering preventive occupation-related practices against leptospirosis. Logistic regression was employed to examine association while controlling for the confounding effects of other variables. RESULTS: Forty-nine percent were found to have good knowledge on the transmission and prevention of leptospirosis while 21% had satisfactory preventive and health-seeking practice. After controlling for the confounding effect of pedicab ownership, the odds of having unsatisfactory practices among those with low knowledge were found to be 13 times higher than those with good knowledge. Education was found to be an effect measure modifier. CONCLUSION: The results suggest that a low level of education combined with poor leptospirosis-related knowledge has a magnified effect on practices. Increasing the knowledge of pedicab drivers especially those with a low level of education is necessary to improve their practices. Information dissemination on leptospirosis should be further intensified. Collaboration between health workers and organizations of pedicab drivers can be done to organize health information seminars.