by Sean Ruck
, Contributing Editor | March 12, 2019
From the March 2019 issue of HealthCare Business News magazine
Radlearn.ai is built on top of a popular open-source package called Cornerstone. Since Cornerstone is used as its… cornerstone, essentially any DICOM data supported by Cornerstone is supported by Radlearn.ai, which means MRI, CT, ultrasound, etc. Radlearn.ai, essentially, helps manipulate pixel data in a browser environment. Radiologists can use the tools to annotate images based on a variety of characteristics. Goel provided a simple example using an aggressive brain tumor – Radlearn.ai allows someone to easily make distinct annotations of the necrotic tumor, and the amorphous edema around the primary tumor. “Capturing amorphous pathology like edema is a weak point in many annotation solutions, as the pathology is not confined by a clean border,” he explained.
Goel provided an explanation in more basic terms, “You could imagine Radlearn.ai is a fancy version of MS Paint for radiology data. Radlearn.ai takes the basic paintbrush tool a few steps further allowing you to precisely control annotation, based on specified characteristics, within the confines of a web browser.”
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Ultimately, Goel believes that Radlearn.ai could be effective for any hospital or organization that is interested in creating radiology AI models with an optimal workflow. The platform will be able to be readily integrated into a hospital infrastructure so all data is secure and protected. “As they annotate the data, all of the information would be saved on an internal server within the hospital network. The labeled data can then be used for model development or other research interests,” he said.
Rollout for the platform would require an initial set-up by the Radlearn team. “We haven’t integrated our platform at any organizations yet, but we look forward to that milestone in the future. Fortunately, the radiology infrastructure is well defined and modular, so this makes the integration process much easier.”
Goel’s next-steps are testing his platform and optimizing and improving upon current annotation functionalities. He’s currently in the process of arranging a collaboration project with an organization, that will allow him to test his system with an extremely large amount of data. And his time in the SIIM spotlight isn’t over yet either. In June, he’ll be in front of the SIIM audience again to present an update on the platform. By that time, he hopes to share some preliminary results of the big data collaboration.
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