Computer-Aided Triage of Body CT Scans with Deep Learning

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PROJECT SUMMARY / ABSTRACT Computed tomography (CT) imaging for the body can result in thousands of images spanning many organs and myriad possible diseases. With growing patient load as well as increasing resolution and complexity of scans, the task of CT interpretation has become daunting. To improve radiologist performance, many artificial intelligence (AI) algorithms have been produced, but most are limited by their very narrow application to a specific disease in a specific organ or have been trained on limited data due to the high cost and complexity of manual annotation. As a result, there is an unmet need because existing AI solutions have not significantly improved the workflow or performance of radiologists. To meet these needs, we propose to develop a computer-aided diagnosis triage tool for CT of the chest, abdomen, and pelvis (CAP) that would focus radiologists’ attention on regions with a high likelihood of actionable disease while minimizing search efforts in regions of low likelihood. Our hypothesis is that a triage tool will improve radiologist workflow while simultaneously maintaining or improving performance. Our long-term goal is to create a clinical decision support system that will address bottlenecks of radiologist workflow and performance. As key steps toward demonstrating feasibility for that goal, we propose the following three specific aims: 1. Create framework for the assembly, deidentification, annotation, and sharing of over a million chest, abdomen, pelvis (CAP) CT cases from two major health systems. 2. Develop a triage system trained using multi-site CT datasets through collaborative/federated learning. 3. Pilot use of the triage system at multiple sites to allow radiologists to perform efficiently and equivalently for clinical tasks of assessing actionable disease in CAP CT. Key innovations will include the use of weak supervision to label a massive number of cases from two health systems. Labeling will be based on rule-based expert systems as well as natural language processing. Image classification will be based on deep learning models capable of processing an entire 3D CT volume and trained with federated learning to leverage the rich heterogeneity of data from the two health systems. The expected outcome of this project will be evidence to support a new clinical workflow for radiologist interpretation, which is the foundation for all medical imaging. For this project, we will maximize impact by addressing CAP CT because of the large patient load and complex anatomy/disease, and by producing one of the largest medical imaging datasets that can be shared for future research including grand challenges. In addition, by leveraging existing data in patient archives and radiology reports, our approach has the potential to be applicable to other body sites or imaging modalities in the future.