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Presentation at the 2019 National Library of Medicine Informatics Training Conference on Automated Fundus Image Quality Assessment in Retinopathy of Prematurity.

We assess the ability of a Random Forest model to predict image quality from clinical data alone (i.e., without evaluating images).

The purpose of this study was to implement an algorithm for automatically assessing the quality of fundus images, and to evaluate its performance on a set of Retinopathy of Prematurity (ROP) images compared to expert assessment. The computer-based algorithm assessed image quality with strong correlation to the expert consensus rank, although not as high as individual expert correlation with the consensus rank.

We assessed the neuroprotective effects of Flibanserin (BIMT-17, Addyi), a dual 5-HT1A agonist and 5-HT2A antagonist, in a light-induced retinopathy mouse model.

We share herethe software development best practices of the AI-READI project, one of the data generation projects funded under the NIH Bridge2AI program. These best practices are maintained from a dedicated GitHub repositorywhere you can follow the latest developments.Use theGitHub issuesfor submitting feedback or making suggestions. The rationales for establishing these best practices are to: Help the standardization of the AI-READI software to facilitate future reuse, i.e. make the AI-READI software Findable, Accessible, Interoperable, and Reusable (FAIR) Ensure the efficient and effective development of software across the project, especially when contributions from several teams are required Facilitate on-boarding of new developers to the project Our focus here is on establishing actionable best practices that can be directly implemented by developers of the project. These best practices are intended to remain general enough such that they can apply to any software development project within AI-READI.&nbsp