In Multiple Sclerosis follow-up, radiologists manually cross-reference every lesion across longitudinal brain MRI scans. The process is time-consuming and carries user-dependent variance. Cordistronic's deep-learning pipeline turns that review into an objective, reproducible workflow with automated segmentation and color-coded change analysis.
The question in MS follow-up is not 'is there a lesion?': it is 'what changed since the last scan?' The radiologist places the previous and current MRI volumes side by side and compares every lesion for location, size, and contrast enhancement.
That comparison demands finding the same slice in both scans, mentally matching lesion boundaries, and noticing subtle changes. Under heavy follow-up load this takes long minutes per patient and inter-observer consistency suffers.
1. Automated segmentation
A deep-learning model automatically segments white-matter demyelinating plaques on 3D volumetric MRI: no manual tracing required.
2. Longitudinal matching
The current scan is spatially aligned to the previous one; each lesion is matched by location.
3. Color-coded report
Lesions are color-coded as stable, new, progressed, or regressed, producing an objective follow-up report.
The efficacy of MS therapy depends on early, consistent detection of new lesions. As part of a clinical decision-support workflow, automated change analysis catches small but meaningful changes that manual review can miss: and makes treatment response measurable.
Does this software make diagnoses?
No. Cordistronic's tool is a decision-support system: it does not diagnose. It is a measurement layer that speeds up and objectifies the radiologist's follow-up assessment.
Which MRI sequences are supported?
The target is 3D volumetric brain MRI protocols including T1 and T2/FLAIR sequences. Integration is configured to the hospital's existing PACS environment.
Where is patient data processed?
Our clinical platform runs on-premise: patient data never leaves the hospital network. Data residency is the foundation of our compliance approach.