Pixyl.Neuro.FL

Pixyl

Pixyl.Neuro.FL integrates into the routine workflow to automatically identify, highlight and quantify any hyperintense regions in the MRI, aimed at supporting the diagnosis and monitoring of patients. In a longitudinal study where there are multiple visits, the software identifies any evolution of hyperintensities compared to the previous visit and quantifies that change, thus providing data on the activity of the disease. The software outputs both a quantitative report on total lesion volume and evolution, as well as annotated MRI images with lesions contoured.
Product specifications Information source: Vendor
Last updated: Feb. 19, 2023
General
Product name Pixyl.Neuro.FL
Company Pixyl
Subspeciality Neuro
Modality MR
Disease targeted Leukoencephalopathy, vascular dementia, small-vessel disease
Key-features White matter hyperintensities detection, white matter hyperintensities volume quantification, longitudinal analysis
Suggested use During: perception aid (prompting all abnormalities/results/heatmaps)
Data characteristics
Population patients with known or suspected White-Matter Hyperintensities
Input MR sequence, 3D T2-weighted FLAIR, 2D T2-weighted FLAIR, 3D T1-weighted (optional), 3D T1-weighted + contrast (optional)
Input format DICOM
Output structured report, segmentation overlay, quantified values
Output format PDF report, DICOM report, annotated DICOM sequences
Technology
Integration Integration in standard reading environment (PACS), Integration via AI marketplace or distribution platform, Stand-alone webbased
Deployment Locally on dedicated hardware, Locally virtualized (virtual machine, docker), Cloud-based, Hybrid solution
Trigger for analysis Automatically, right after the image acquisition, On demand, triggered by a user through e.g. a button click, image upload, etc.
Processing time 1 - 10 minutes
Certification
CE
Certified, Class IIa , MDD
FDA
 No or not yet
Market presence
On market since 10-2019
Distribution channels Incepto, GE, Siemens, Osimis
Countries present (clinical, non-research use) >7
Paying clinical customers (institutes) >50
Research/test users (institutes) >50
Pricing
Pricing model Subscription
Based on Number of installations, Number of analyses
Evidence
Peer reviewed papers on performance

  • Artificial intelligence to predict clinical disability in patients with multiple sclerosis using FLAIR MRI (read)
Non-peer reviewed papers on performance

  • Leveraging 3d information in unsupervised brain mri segmentation (read)

  • Use of PIXYL software analysis of brain MRI (with & without contrast) as valuable metric in clinical trial tracking in study of multiple sclerosis (MS) and related neurodegenerative processes (read)

  • Use of Software Analytics of Brain MRI (with & without contrast) As Objective Metric in Neurological Disorders and Degenerative Diseases (read)
Other relevant papers