Search Results

Abstract

PURPOSE: To determine and analyze the usability metrics of a free mobile learning app for ophthalmology in Brazil.
METHODS: Metric data from the management dashboard of the CBOQUIZ app were used. All users registered on the platform between March 2019 and June 30, 2021 were included. The number of questions answered, number of correct answers, number of questions answered and correct answers by subject area, and user performance by geographic region were analyzed.
RESULTS: There were 458 active users during the research period and 107,245 questions answered (average, 234.16 questions per user). Of the questions answered, 81,600 (75.5%) were correct and 2,645 were incorrect. The states in Brazil with the best performance were Espírito Santo, Paraiba, and Paraná. The subject area with the lowest hit rate was basic sciences (69.1%), within which embryology demonstrated the lowest hit rate (58.28%). The posterior segment had the highest number of questions answered, followed by miscellaneous topics and the anterior segment. Questions on strabismus were the least answered.
CONCLUSION: The app was used consistently throughout the period studied, and participants adhered to this teaching modality. Performance asymmetry was observed across the Brazil states. The CBOQUIZ app can be used to homogenize ophthalmology teaching in the country.

Keywords: Ophthalmology; Mobile applications; Teaching; Smartphone; Education, distance; Internship and residency; Brazil

Abstract

PURPOSE: Standard automated perimetry has been the standard method for measuring visual field changes for several years. It can measure an individual’s ability to detect a light stimulus from a uniformly illuminated background. In the management of glaucoma, the primary objective of perimetry is the identification and quantification of visual field abnormalities. It also serves as a longitudinal evaluation for the detection of disease progression. The development of artificial intelligence-based models capable of interpreting tests could combine technological development with improved access to healthcare.
METHODS: In this observational, cross-sectional, descriptive study, we used an artificial intelligence-based model [Inception V3] to interpret gray-scale crops from standard automated perimetry that were performed in an ophthalmology clinic in the Brazilian Amazon rainforest between January 2018 and December 2022.
RESULTS: The study included 1,519 standard automated perimetry test results that were performed using Humphrey HFA-II-i-750 (Zeiss Meditech). The Subsequently, 70%, 10%, and 20% of the dataset were used for training, validation, and testing, respectively. The model achieved 80% (68.23%–88.9%) sensitivity and 94.64% (88.8%–98%) specificity for detecting altered perimetry results. Furthermore, the area under the receiver operating characteristic curve was 0.93.
CONCLUSIONS: The integration of artificial intelligence in the diagnosis, screening, and monitoring of pathologies represents a paradigm shift in ophthalmology, enabling significant improvements in safety, efficiency, availability, and accessibility of treatment.

Keywords: Glaucoma; Disease progression; Perimetry; Visual Fields; Visual field tests; Artificial intelligence; Neural networks, computers; Machine learning