From Single-Visit to Multi-Visit Image-Based Models: Single-Visit Models are Enough to Predict Obstructive Hydronephrosis
Stanley Bryan Z. Hua, Mandy Rickard, John Weaver, Alice Xiang, Daniel Alvarez, Kyla N. Velear, Kunj Sheth, Gregory E. Tasian, Armando J. Lorenzo, Anna Goldenberg, Lauren Erdman
Code Available — Be the first to reproduce this paper.
ReproduceCode
- github.com/stan-hua/temporal_hydronephrosisOfficialIn paperpytorch★ 3
Abstract
Previous work has shown the potential of deep learning to predict renal obstruction using kidney ultrasound images. However, these image-based classifiers have been trained with the goal of single-visit inference in mind. We compare methods from video action recognition (i.e. convolutional pooling, LSTM, TSM) to adapt single-visit convolutional models to handle multiple visit inference. We demonstrate that incorporating images from a patient's past hospital visits provides only a small benefit for the prediction of obstructive hydronephrosis. Therefore, inclusion of prior ultrasounds is beneficial, but prediction based on the latest ultrasound is sufficient for patient risk stratification.