Key scientific publications

Harmonization of diffusion MRI on healthy subjects using NeuroCombat and LongCombat: a B-Q MINDED brain intra- and inter-scanner study

Pinto et al. (2025)

Independent validation and outlier analysis of EuroPOND alzheimer’s disease staging model using ADNI and real-world clinical data

Wittens et al. (2025)

Monitoring of amyloid related imaging abnormalities: SWI vs T2*-GRE

Sima et al. (2025)

Artificial Intelligence Assistive Software Tool for Automated Detection and Quantification of Amyloid-Related Imaging Abnormalities

Sima et al. (2024)

Brain age as a biomarker for pathological versus healthy ageing – a REMEMBER study

Wittens et al. (2024)

A deep learning model for brain segmentation across pediatric and adult populations

Simarro et al. (2024)

Imaging Findings in Acute TBI: Analysis of 4000+ CTs (CENTER-TBI).

Vande Vyvere et al. (2024)

Brain volume loss in Japanese patients with multiple sclerosis is present in the early to middle stage of the disease

Fujimori & Nakashima (2024)

Patient-reported outcome measurements in a selective cohort of relapsing–remitting multiple sclerosis patients: relationships with physical disability, cognitive impairment, and MRI-derived metrics

London et al. (2023)

Combining semi-quantitative rating and automated brain volumetry in MRI evaluation of patients with probable behavioural variant of fronto-temporal dementia: an added value for clinical practise?

Calloni et al. (2023)

Validation of a deep learning model for traumatic brain injury detection and NIRIS grading on non-contrast CT: a multi-reader study with promising results and opportunities for improvement

Jiang et al. (2023)

Brain volume loss in multiple sclerosis is independent of disease activity and might be prevented by early disease-modifying therapy

Slezáková et al. (2023)

Associations between neurofilament light chain levels, disease activity and brain atrophy in progressive multiple sclerosis

Szilasiova et al. (2022)

Cost-Effectiveness Analysis of AI-Assisted Radiological Assessment in Patients With Relapsing Remitting Multiple Sclerosis in the UK

Esposito et al. (2022)

Morphometric evaluation of traumatic axonal injury and the correlation with post-traumatic cerebral atrophy and functional outcome

Bohyn et al. (2022)

Inter- and Intra-Scanner Variability of Automated Brain Volumetry on Three Magnetic Resonance Imaging Systems in Alzheimer’s Disease and Controls

Wittens et al. (2021)

icobrain ms 5.1: Combining Unsupervised and Supervised Approaches for Improving the Detection of Multiple Sclerosis Lesions.

Rakic et al. (2021)

Diagnostic Performance of Automated MRI Volumetry by icobrain dm for Alzheimer's Disease in a Clinical Setting: A REMEMBER Study

Wittens et al. (2021)

Patterns of cortical grey matter thickness reduction in multiple sclerosis

Fujimori et al. (2021)

Health Economic Impact of Software-Assisted Brain MRI on Therapeutic Decision-Making and Outcomes of Relapsing-Remitting Multiple Sclerosis Patients—A Microsimulation Study

Sima et al. (2021)

MRI Volumetric Measures of Outcome after Severe Adolescent TBI

Ferrazzano et al. (2021)

Neuroanatomical Substrates and Symptoms Associated With Magnetic Resonance Imaging of Patients With Mild Traumatic Brain Injury

Richter et al. (2021)

A Novel Digital Care Management Platform to Monitor Clinical and Subclinical Disease Activity in Multiple Sclerosis

Van Hecke et al. (2021)

Long-term effectiveness of natalizumab on MRI outcomes and no evidence of disease activity in relapsing-remitting multiple sclerosis patients treated in a Czech Republic real-world setting: A longitudinal, retrospective study

Horakova et al. (2020)

Number of MRI T1-hypointensity corrected by T2/FLAIR lesion volume indicates clinical severity in patients with multiple sclerosis

Akaishi et al. (2020)

The association between MRI brain volumes and computerized cognitive scores of people with multiple sclerosis.

Golan et al. (2020)

Towards validation in clinical routine: a comparative analysis of visual MTA ratings versus the automated ratio between inferior lateral ventricle and hippocampal volumes in Alzheimer’s disease diagnosis

Wittens et al. (2024)

Automated MRI volumetry as a diagnostic tool for Alzheimer's disease: validation of icobrain dm

Struyfs et al. (2020)

Single MRI-Based Volumetric Assessment in Clinical Practice Is Associated With MS-Related Disability

D'hooghe et al. (2019)

The Use of Biomarkers and Genetic Screening to Diagnose Frontotemporal Dementia: Evidence and Clinical Implications

Gossye et al. (2019)

Association of short-term cognitive decline and MCI-to-AD dementia conversion with CSF, MRI, amyloid- and 18F-FDG-PET imaging

Ottoy et al. (2019)

Comparing longitudinal brain atrophy measurement techniques in a real-world multiple sclerosis clinical practice cohort: towards clinical integration?

Beadnall et al. (2019)

Automatic Quantification of CT Features in Acute TBI

Jain et al. (2019)

A Retrospective Belgian Multi-Center MRI Biomarker Study in Alzheimer's Disease (REMEMBER)

Niemantsverdriet et al. (2018)

Evaluation of methods for volumetric analysis of pediatric brain data: The childmetrix pipeline versus adult-based approaches

Phan et al. (2018)

Correlation of clinical findings and brain volume data in multiple sclerosis

Fragoso et al. (2017)

Whole brain and grey matter volume of Japanese patients with multiple sclerosis

Akaishi et al. (2017)

Intercontinental validation of brain volume measurements using MSmetrix

Finkelsztejn et al. (2017)

Automated brain volumetrics in multiple sclerosis: a step closer to clinical application

Wang et al. (2016)

Reliable measurements of brain atrophy in individual patientswith multiple sclerosis

Smeets et al. (2016)

Two Time Point MS Lesion Segmentation in Brain MRI: An Expectation-Maximization Framework, Frontiers in Neuroscience: Brain Imaging Methods

Jain et al. (2016)

Automatic segmentation and volumetry of multiple sclerosis brain lesions from MR images

Jain et al. (2015)

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We don’t guess. We validate, verify, and build with clinical rigor - delivering AI you can trust.
Backed by peer-reviewed science and real-world results.

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