Deep learning system to predict the 5-year risk of high myopia using fundus imaging in children

Our study aims to identify children at risk of developing high myopia for timely assessment and intervention, preventing myopia progression and complications in adulthood through the development of a deep learning system (DLS). Using a school-based cohort in Singapore comprising 998 children (aged 6-12 years old), we train and perform primary validation of the DLS using 7456 baseline fundus images of 1878 eyes; with external validation using an independent test dataset of 821 baseline fundus images of 189 eyes together with clinical data (age, gender, race, parental myopia, and baseline spherical equivalent (SE)). We derive three distinct algorithms - image, clinical, and mix (image + clinical) models to predict high myopia development (SE ≤ -6.00 diopter) during teenage years (5 years later, age 11-17). Model performance is evaluated using the area under the receiver operating curve (AUC). Our image models (Primary dataset AUC 0.93-0.95; Test dataset 0.91-0.93), clinical models (Primary dataset AUC 0.90-0.97; Test dataset 0.93-0.94) and mixed (image + clinical) models (Primary dataset AUC 0.97; Test dataset 0.97-0.98) achieve clinically acceptable performance. The addition of 1 year SE progression variable has minimal impact on the DLS performance (clinical model AUC 0.98 versus 0.97 in the primary dataset, 0.97 versus 0.94 in the test dataset; mixed model AUC 0.99 versus 0.97 in the primary dataset, 0.95 versus 0.98 in test dataset). Thus, our DLS allows prediction of the development of high myopia by teenage years amongst school-going children. This has potential utility as a clinical decision support tool to identify "at-risk" children for early intervention.info:eu-repo/semantics/publishedVersio

Analogy-Making as a Core Primitive in the Software Engineering Toolbox

An analogy is an identification of structural similarities and correspondences between two objects. Computational models of analogy making have been studied extensively in the field of cognitive science to better understand high-level human cognition. For instance, Melanie Mitchell and Douglas Hofstadter sought to better understand high-level perception by developing the Copycat algorithm for completing analogies between letter sequences. In this paper, we argue that analogy making should be seen as a core primitive in software engineering. We motivate this argument by showing how complex software engineering problems such as program understanding and source-code transformation learning can be reduced to an instance of the analogy-making problem. We demonstrate this idea using Sifter, a new analogy-making algorithm suitable for software engineering applications that adapts and extends ideas from Copycat. In particular, Sifter reduces analogy-making to searching for a sequence of update rule applications. Sifter uses a novel representation for mathematical structures capable of effectively representing the wide variety of information embedded in software. We conclude by listing major areas of future work for Sifter and analogy-making in software engineering.Comment: Conference paper at SPLASH 'Onward!' 2020. Code is available at https://github.com/95616ARG/sifte

Games-to-teach or games-to-learn : unlocking the power of digital game-based learning through performance /

Comprend des références bibliographiques et un index.The book presents a critical evaluation of current approaches related to the use of digital games in education. The author identifies two competing paradigms: that of games-to-teach and games-to-learn. Arguing in favor of the latter, the author advances the case for approaching game-based learning through the theoretical lens of performance, rooted in play and dialog, to unlock the power of digital games for 21st century learning. Drawing upon the author's research, three concrete exemplars of game-based learning curricula are described and discussed. The challenge of advancing game-based learning in education is addressed in the context of school reform. Finally, future prospects of and educational opportunities for game-based learning are articulated.Introduction -- Games-to-teach or games-to-learn: What's the difference and why it matters -- Theory of game-based learning as performance -- Statecraft X: Learning governance by governing -- Legends of Alkhimia: Engaging in scientific inquiry by being a chemist -- Escape from Centauri 7: Reifying electromagnetic forces through simulation -- Game-based learning and the challenges of school reform -- Conclusion: Future prospects and educational opportunities

pp. 43–54. Symposium Organizing Committee, Dongseo University. Virtual Reality in Education: Rooting Learning in Experience

There has been a general tendency for learning to get further removed from experience as students progress from primary education, through secondary education, and to tertiary education. Learning becomes more language-based, conceptual, and abstract. There are important side effects. In the domain of physics, for example, it is well known that many students can readily solve physics problems drawn from textbooks. However, they have little "feel " and understanding of the qualitative dimensions of the phenomena they study. In this talk, I shall argue for the need to root learning in experience. I shall discuss how the technology of virtual reality can be used to achieve this goal, thereby providing a foundation for students' conceptual and higher-order learning. I shall illustrate the ideas using C–VISions, a networked collaborative virtual learning environment developed for this purpose