The comparison of serial magnetic resonance imaging studies is a common task in clinical radiology. Such clinical judgments are, however, not very reproducible. There are a variety of reasons for this, including the confounding of acquisition related changes with disease related changes, and issues related to information presentation.
The Change Detector is a software system that compares serial magnetic resonance imaging studies, and presents changes in the form of a color-coded change map, superimposed on the anatomical images. Using the Change Detector it may be possible to identify changes months earlier than is possible using manual inspection alone.
The Change Detector detects changes in the image types that radiologists use: T1, T1-post, T2, FLAIR, proton density (PD), magnetization transfer suppression (MTS), apparent diffusion coefficient (ADC), cerebral blood volume (CBV), cerebral blood flow (CBF), mean transit time (MTT), and time to peak (TTP).
The Change Detector is an example of a layered artificial intelligence (AI) system. It demonstrates how practical AI systems can free expert radiologists from routine tasks such as searching images for changing regions, to allow them to focus on reaching critical clinical judgments. The system demonstrates how AI can simply turn information overload, into information affluence.
We're currently preparing an FDA 510(k) application for the Change Detector.
Our research priority is to implement general purpose change detection, with the Change Detector supporting new areas of the anatomy (e.g. breast cancer screening), and new modalities (e.g. CT).
Would you like to learn more about the Change Detector? We'd love to hear from you! You can reach us at: firstname.lastname@example.org.