Quantitative differential diagnostics in dementia

cNeuro cDSI is a tool for clinical decision support in dementia based on combination of imaging, lab results and clinical data.


  • Cloud-based tool running in a standard browser.
  • Comparison of patient’s data to data from previously diagnosed patients providing a quantitative estimate about patient’s similarity to different disease groups.
  • Intuitive and interactive visualization of the similarity.
  • Decision models available for etiology and progression.
  • Patient data supported: clinical and neuropsychological test data, MRI imaging biomarkers, CSF biomarkers and APOE gene.
In clinical diagnostics, the role of quantitative data acquired, e.g., from clinical and neuropsychological tests, imaging and body fluids is increasing. Interpretation of all these, sometimes contradictory, data is challenging, especially when simultaneously keeping in mind patient demographics, increasing knowledge on disease subtypes and existing economic constraints for acquiring data. cDSI helps to systemize this interpretation and make it more quantitative.


  • Cloud-based – software runs in a standard web browser.
  • Computation of disease state index measuring the similarity of the patient to previously diagnosed patients.
  • Data sources supported:
    • Clinical and neuropsychological tests: MMSE, RAVLT/CERAD recall & delayed recall, TMT A&B, Animal fluency, NPI.
    • MRI imaging biomarkers from cMRI: volumes, computed MTA, GCA (global and local), Fazekas.
    • Biomarkers: CSF biomarkers, APOE.
  • Decision models available:
    • Etiology: Alzheimer’s disease (AD), frontotemporal lobar degeneration (FTLD), vascular dementia (VaD), cognitively normal (CN).
    • Progression: stable and progressive mild cognitive impairment.
  • Compact visualization of all data using the patented disease state fingerprint technology.
Disease state fingerprint comparing FTLD and AD. Red indicative to FTLD and blue to AD.
Decision model for etiology showing the highest similarity to FTLD.
Distributions of beta amyloid CSF biomarker in AD (blue) and FTLD (red) groups and the value measured for the patient (yellow).

List of publications

  • Tolonen, H. Rhodius-Meester, M. Bruun, J. Koikkalainen, F. Barkhof, A. Lemstra, T. Koene, P. Scheltens, C. Teunissen, T. Tong, R. Guerrero, A. Schuh, C. Ledig, M. Baroni, D. Rueckert, H. Soininen, A. Remes, G. Waldemar, S. Hasselbalch, P. Mecocci, W. van der Flier and J. Lötjönen. Data-driven differential diagnosis of dementia using multiclass Disease State Index. Frontiers in Aging Neuroscience 10:111, 1-11, 2018.  https://doi.org/10.3389/fnagi.2018.00111
  • Rhodius-Meester et al. Integrating biomarkers for underlying Alzheimer’s disease in mild cognitive impairment in daily practice: Comparison of a clinical decision support system with individual biomarkers. Journal of Alzheimer’s Disease 50: 261-270, 2016.
  • Hall et al. Generalizability of the Disease State Index Prediction Model for Identifying Patients Progressing from Mild Cognitive Impairment to Alzheimer’s Disease. Journal of Alzheimer’s Disease 44(1): 79-92, 2015.
  • Hall et al. Predicting Progression from Cognitive Impairment to Alzheimer’s Disease with the Disease State Index. Current Alzheimer Research, 12: 69-79, 2015.
  • Runtti el al. Quantitative evaluation of disease progression in longitudinal mild cognitive impairment cohort. Journal of Alzheimer’s Disease, 39(1): 49-61, 2014.
  • Liu et al. Predicting AD conversion: comparison between prodromal AD guidelines and computer assisted PredictAD tool. PLoSOne 8(2), e55246, 2013.
  • Muñoz-Ruiz et al. Disease state fingerprint in frontotemporal degeneration with reference to Alzheimer’s disease and mild cognitive impairment. Journal of Alzheimer’s Disease, 35(4), 727-739, 2013.
  • Mattila et al. Optimizing the diagnosis of early Alzheimer’s disease in mild cognitive impairment subjects. Journal of Alzheimer’s Disease 32: 969-979, 2012.
  • Simonsen et al. Application of the PredictAD software tool to patients with mild cognitive impairment. Dementia and Geriatric Cognitive Disorders 34: 344-450, 2012.
  • Mattila et al. Disease State Fingerprint for Evaluating the State of Alzheimer’s Disease in Patients. Journal of Alzheimer’s Disease 27: 163-176, 2011.


All data transfer uses SSL encryption. Stored data are encrypted and anonymized.
More information is available in a separate security statement.

System Requirements

Supported web browsers:

  • Google Chrome 61 or later
  • Firefox 56 or later
  • Internet Explorer 11 or later.

Recommended display resolution 1680 x 1050 or higher.

Regulatory Compliance

This product complies with the following requirements: European Council Directive 93/42/EEC concerning medical devices. Not cleared by FDA for commercial sale in the US.

Indications for Use

cNeuro cDSI is intended for visualization of imaging biomarkers and other clinical data for a patient being evaluated for a suspected neurodegenerative disease and for comparing these to corresponding values from previously diagnosed patients. The software is intended to provide clinical decision support to healthcare professionals by enabling them to review how biomarkers or clinical test results, individually or combined together, compares to distributions of different neurodegenerative diseases. The intended user profile covers healthcare professionals who work with patients being evaluated for a suspected neurodegenerative disease. The intended operational environment is an office-like environment with a computer.