Spaced radiology: encouraging durable memory using spaced testing in pediatric radiology.

Applied memory research in the field of cognitive and educational psychology has generated a large body of data to support the use of spacing and testing to promote long-term or durable memory. Despite the consensus of this scientific community, most learners, including radiology residents, do not utilize these tools for learning new information. We present a discussion of these parallel and synergistic learning techniques and their incorporation into a software platform, called Spaced Radiology, which we created for teaching radiology residents. Specifically, this software uses these evidence-based strategies to teach pediatric radiology through a flashcard deck system.

Constructing a Computer-Aided Differential Diagnosis Engine from Open-Source APIs.

This paper describes the design and implementation of an application that parses and analyzes radiology report text to provide a radiologic differential diagnosis. The system was constructed using a combination of freely available web-based APIs and originally developed during the Society for Imaging Informatics in Medicine (SIIM) 2014 Hackathon. Continued development has refined and increased the accuracy of the algorithm. This project demonstrates the power and possibilities of combining existing technologies to solve unique problems as well as the stimulus of the hackathon setting to spur innovation.

Personalizing lung cancer risk prediction and imaging follow-up recommendations using the National Lung Screening Trial dataset.

OBJECTIVE: To demonstrate a data-driven method for personalizing lung cancer risk prediction using a large clinical dataset. MATERIALS AND METHODS: An algorithm was used to categorize nodules found in the first screening year of the National Lung Screening Trial as malignant or nonmalignant. Risk of malignancy for nodules was calculated based on size criteria according to the Fleischner Society recommendations from 2005, along with the additional discriminators of pack-years smoking history, sex, and nodule location. Imaging follow-up recommendations were assigned according to Fleischner size category malignancy risk. RESULTS: Nodule size correlated with malignancy risk as predicted by the Fleischner Society recommendations. With the additional discriminators of smoking history, sex, and nodule location, significant risk stratification was observed. For example, men with ≥60 pack-years smoking history and upper lobe nodules measuring >4 and ≤6 mm demonstrated significantly increased risk of malignancy at 12.4% compared to the mean of 3.81% for similarly sized nodules (P < .0001). Based on personalized malignancy risk, 54% of nodules >4 and ≤6 mm were reclassified to longer-term follow-up than recommended by Fleischner. Twenty-seven percent of nodules ≤4 mm were reclassified to shorter-term follow-up. DISCUSSION: Using available clinical datasets such as the National Lung Screening Trial in conjunction with locally collected datasets can help clinicians provide more personalized malignancy risk predictions and follow-up recommendations. CONCLUSION: By incorporating 3 demographic data points, the risk of lung nodule malignancy within the Fleischner categories can be considerably stratified and more personalized follow-up recommendations can be made.