• The A.I. Analysis, Inc. Change Detector compares serial head MR imaging studies, spatially registering the image volumes, and presenting changes between “baseline-followup” pairs of volumes as a color-coded overlay indicating what is changing, where, and by how much. The Change Detector reduces information overload and enhances radiologist productivity, increasing value.
• The Change Detector analyzes the MR image types that radiologists use, including T1, T1-post, T2, FLAIR, PD, MTS, ADC, CBV, CBF, MTT, and TTP. And the Change Detector automatically adapts to new image types, even ones that haven’t been invented yet.
• The Change Detector demonstrates how practical A.I. systems can free radiologists from the routine task of searching images for changes, making characterization easier, and allowing radiologists to focus on reaching critical judgments and directing therapy.

The Challenge

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.

Automated Change Detection

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.

Use Cases

In the Clinic

Monitoring responses to therapy, and identifying and characterizing disease recurrence. Changes may be found months earlier than they would be using manual inspection alone.

In Research

Comparing the efficacy of interventions.

In All Settings

Results are automatic, quantitative, and reproducible. Given the same input studies, the Change Detector will always generate the same analysis results.

What We're Working On

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).

Questions?

Would you like to learn more about the Change Detector? We'd love to hear from you! You can reach us at: info@aianalysis.com.