Neuro

Pixyl.Neuro by Pixyl

Neuroimaging Insight powered by AI

A solution for quantifying neurological biomarkers

The analysis of MRI slices as part of the follow-up of neurodegenerative and neuroinflammatory diseases, such as Multiple Sclerosis, is essential. In fact, it allows to evaluate and predict disease time trends and response to treatment where appropriate. This analytical work is essential for radiologists whose image analysis workload is increasing by 10% each year (for a stable number of radiologists).

What if an AI solution could quantify automatically
brain abnormalities of the white substance?

Based on advanced Artificial Intelligence technology for brain MRI analysis. Pixyl.neuro allows you to quantify and track the progress of the disease in your patients with multiple sclerosis or dementia.
The software is compatible with any type of MR exam.

The results are provided, directly into your clinical workflow in a fully automated way and in about 5 minutes.

Expected benefits

  •  Objective radiological report through accurate and standardized measurements.
  • Quick reading through segmentation using an intuitive color code.
  • An Automatic tool assist in decision making in your workflow, avalaible in about 5 minutes.
  • Earlier prediction of disability, disease progression and treatment response.

What Radiologists are saying

Like icobrain, brain biomarker measurements need to be extremely reproducible and sensitive enough to detect relevant clinical changes.

Dr. Max Wintermark, Professor of Radiology and Chief of Neuroradiology at Stanford University Medical Center, USA.

icobrain has been a really valuable addition to our clinical practice as patients can conceptually understand much better what we are looking for in their brain scans.

Prof. Jeffrey Dunn, MD.

Publications

  1. Fragoso, Yara Dadalti, Paulo Roberto Wille, Marcelo Abreu, Joseph Bruno B. Brooks, Ronaldo Maciel Dias, Juliana Avila Duarte, Luciano Farage, et al. “Correlation of Clinical Findings and Brain Volume Data in Multiple Sclerosis.” Journal of Clinical Neuroscience 44 (October 2017): 155–57. https://doi.org/10.1016/j.jocn.2017.06.006.
  2. Jain, Saurabh, Diana M. Sima, Annemie Ribbens, Melissa Cambron, Anke Maertens, Wim Van Hecke, Johan De Mey, et al. “Automatic Segmentation and Volumetry of Multiple Sclerosis Brain Lesions from MR Images.” NeuroImage: Clinical 8 (2015): 367–75. https://doi.org/10.1016/j.nicl.2015.05.003.
  3. Lysandropoulos, Andreas P., Julie Absil, Thierry Metens, Nicolas Mavroudakis, François Guisset, Eline Van Vlierberghe, Dirk Smeets, Philippe David, Anke Maertens, and Wim Van Hecke. “Quantifying Brain Volumes for Multiple Sclerosis Patients Follow-up in Clinical Practice – Comparison of 1.5 and 3 Tesla Magnetic Resonance Imaging.” Brain and Behavior 6, no. 2 (February 2016): n/a-n/a. https://doi.org/10.1002/brb3.422.
  4. Smeets, Dirk, Annemie Ribbens, Diana M. Sima, Melissa Cambron, Dana Horakova, Saurabh Jain, Anke Maertens, et al. “Reliable Measurements of Brain Atrophy in Individual Patients with Multiple Sclerosis.” Brain and Behavior 6, no. 9 (September 2016): e00518. https://doi.org/10.1002/brb3.518.
  5. Wang, C, H N Beadnall, S N Hatton, G Bader, D Tomic, D G Silva, and M H Barnett. “Automated Brain Volumetrics in Multiple Sclerosis: A Step Closer to Clinical Application.” Journal of Neurology, Neurosurgery & Psychiatry 87, no. 7 (July 2016): 754–57. https://doi.org/10.1136/jnnp-2015-312304.
  6. Wintermark, Max, Ying Li, Victoria Y. Ding, Yingding Xu, Bin Jiang, Robyn L. Ball, Michael Zeineh, Alisa Gean, and Pina Sanelli. “Neuroimaging Radiological Interpretation System for Acute Traumatic Brain Injury.” Journal of Neurotrauma 35, no. 22 (November 15, 2018): 2665–72. https://doi.org/10.1089/neu.2017.5311.