FDA 510k-Cleared Cyber Device and AI Solution for GBM Care
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GBM Segmentation (Flair)
GBM Segmentation (T1C)
Metastatic Brain Tumor Segmentation
Brain Organs-at-Risk Segmentation
Uncertainty Quantification
Our Imaging Solutions
Discover advanced tools and services that streamline brain imaging and support healthcare professionals and researchers.
Streamlined Smart Segmentation
AI-Powered GBM Segmentation
Collaborative TeamSpace
GBM Volumetric Measurements/Plots
Cyber Device With Secure Communications
GBM DICOM Viewer
Download Segmentation
Download Volumes
Other AI-Powered Segmentations
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Sharing/Exchange
Anonymization
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What They Say
Reviews
Here's what clinicians have to say:
Incorporating the MRIMath AI platform into our daily clinical practice has the potential to significantly enhance patient care by providing accurate measurements of brain tumor volume on FLAIR and post-contrast T1 MRI sequences. This accuracy is critical for determining optimal and timely patient management, such as maintaining a specific treatment or modifying the management approach, as well as for clinical trial selection.
In my experience, the MRIMath was exceptionally user-friendly and easy to use for manual contouring of glioblastomas-GBM. Its intuitive design allowed me to quickly review and revise the segmented tumors with ease. Additionally, I find it valuable that the software offers a secure, HIPAA-compliant cloud environment, enabling me to work from anywhere, collaborate on research projects, and consult with colleagues on special cases.
Fanny Morón, M.D
Professor of Radiology, Baylor College of Medicine
I am pleased to share my strong support for the MRIMath(C) platform, which I have had the pleasure of working with for over two years. My experience with MRIMath(C) has been overwhelmingly positive, and I am consistently impressed by its performance. This platform has proven to be a game-changer in the management of glioma and glioblastoma patients by offering safe, HIPPA-compatible cloud storage for MRI data, ensuring that sensitive information is securely handled.
MRIMath(C) also excels in its automated AI-based and semi-automated contouring capabilities for neoplasms on FLAIR and T1C sequences, accurately measuring disease volume. This enhances the ability of radiologists and neuro-oncologists to monitor patients over time, facilitating the early detection of tumor progression and recurrence. The platform's ability to potentially improve patient outcomes and survival makes it an invaluable tool in the fight against these challenging conditions.
Houman Sotoudeh, M.D
Associate Professor of Radiology, University of Texas Southwestern, Dallas, Texas
I am delighted to learn about the FDA approval of the MRIMath AI-powered cyber device for GBM. This is truly a game-changer for a busy CNS radiation oncology practice. After thorough evaluation, I find it easy to learn, user-friendly, and seamlessly integrates into my workflow. Radiation oncologists often spend a significant amount of time contouring using manual platforms, which can result in substantial inter-user variability. With MRIMath, I can work from anywhere, store data securely, create my own team and assign privileges, send and receive information at high speed, and obtain GBM AI segmentations in seconds. The MRIMath Smart Contouring software is a breath of fresh air, offering low-variability, effective, and precise auto-contouring features. The MRIMath cyber device and AI streamline complex treatment planning workflows with high precision and robustness. The innovative features of MRIMath i2Contour will undoubtedly improve quality and advance patient care while also having the potential to lower healthcare costs.
Markus Bredel, M.D., Ph.D
Chairman and Sylvester Professor of Radiation Oncology, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine
"The field of neuro-oncology has struggled to make meaningful advances in outcomes for patients with primary brain cancer. Glioblastoma, the most common malignant primary brain cancer has been resistance to improved outcomes. A contributor to our challenge in advancing improvements for patients has been the difficulty of interpreting MR images following and during many forms of treatment. In addition, imaging changes may appear as findings of disease progression when in fact, there are not. MRIMath cyber device and GBM AI provide needed and advanced tools for the rapid, longitudinal computation of segmented imaging changes occurring in the central nervous system as a result of glioblastoma, the treatment of glioblastoma, or infiltration into the normal brain. This advance in technology will help improve the outcomes for patients with glioblastoma.
Burt Nabors, M.D
Professor, Director, and Vice Chair, Departments of Neurology and Neurosurgery, University of Alabama
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