- AI
- Molecular Imaging
- CT
- X-Ray
- Ultrasound
- MRI
- Facility Management
- Mammography
FDA Clears New Version of AI Segmentation Software for Brain MRI
Offering rapid automated segmentation of brain structures, the ClearPoint 2.2 software reportedly enhances visualization for a variety of targeted, MRI-guided procedures.
The Food and Drug Administration (FDA) has granted 510(k) clearance for the artificial intelligence (AI)-enabled ClearPoint 2.2 software, which provides automated segmentation of brain structures from magnetic resonance imaging (MRI) scans.
ClearPoint Neuro, the manufacturer of the software, said it provides rapid segmentation that facilitates a variety of targeted, MRI-guided procedures ranging from deep brain stimulation to laser ablation.
Here one can see a comparison of the ClearPoint Maestro Brain Model (a) and the ClearPoint 2.2 software (b), which includes segmentation of subnuclei. (Images courtesy of NeuroImage.)
The ClearPoint 2.2 software also includes the Maestro Brain Model, which was recently validated in a study published in NeuroImage. In a quantitative reproducibility analysis, researchers found the combination of the shape-constrained deformable brain model and voxel-wise tissue segmentation was superior to manual expert segmentation.
“The ClearPoint system can now offer fast, peri-procedural segmentation of the cortical structures of the brain to identify both targets and safety zones for cell and gene therapy delivery, laser ablation, biopsy and deep brain stimulation,” noted Joe Burnett, the president and CEO of ClearPoint Neuro.
New Meta-Analysis Examines MRI Assessment for Treatment of Esophageal Cancer
September 12th 2024Diffusion-weighted MRI provided pooled sensitivity and specificity rates of 82 percent and 81 percent respectively for gauging patient response to concurrent chemoradiotherapy for esophageal cancer, according to new meta-analysis.
Study Assesses Lung CT-Based AI Models for Predicting Interstitial Lung Abnormality
September 6th 2024A machine-learning-based model demonstrated an 87 percent area under the curve and a 90 percent specificity rate for predicting interstitial lung abnormality on CT scans, according to new research.
