Kartik Varadarajan

PhD

Visionairy Health Inc.

Chief Operating Officer

PLCO (Learn more about this study)

PLCOI-748

Mar 15, 2021

Application of deep learning to automate detection of lung nodules on single and serial chest radiographs

Chest radiographs are widely used for diagnosis of various respiratory and cardiac conditions. The interpretation of these images is performed by physicians with a wide range of training and specialization (e.g. family physicians, emergency physicians, pulmonologists, radiologists etc.). Thus, the risk of a critical clinical condition being missed or a significant delay in diagnosis occurring is dependent on the training and expertise of the interpreter. To address disparity in quality of care, we are developing deep learning-based algorithms to detect lung nodules/masses on chest radiographs. The first objective of the proposed project is to assess how well our current deep learning model generalizes on the PLCO dataset. Many deep learning models are currently designed to analyze images from a single timepoint in the disease process. However, comparing a given image to available historical images in standard clinical practice. This comparison helps the physician judge whether changes in previously detected findings have occurred or if new findings have developed. Therefore, a second goal of this project is to develop and test deep learning algorithms that use timeseries of radiographs as input for detection of nodules/masses.