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Digital Biomarkers for the Early Detection of Mild Cognitive Impairment: Artificial Intelligence Meets Virtual Reality
Cavedoni S, Chirico A, Pedroli E, Cipresso P, Riva G.. Frontiers in human neuroscience, 2020-07-24
Topic: Risk, prevention and care in population studies
Elderly people affected by Mild Cognitive Impairment (MCI) usually report a perceived decline in cognitive functions that deeply impacts their quality of life. This subtle waning, although it cannot be diagnosable as dementia, is noted by caregivers on the basis of their relative's behaviors. Crucially, if this condition is also not detected in time by clinicians, it can easily turn into dementia. Thus, early detection of MCI is strongly needed. Classical neuropsychological measures - underlying a categorical model of diagnosis - could be integrated with a dimensional assessment approach involving Virtual Reality (VR) and Artificial Intelligence (AI). VR can be used to create highly ecologically controlled simulations resembling the daily life contexts in which patients' daily instrumental activities (IADL) usually take place. Clinicians can record patients' kinematics, particularly gait, while performing IADL (Digital Biomarkers). Then, Artificial Intelligence employs Machine Learning (ML) to analyze them in combination with clinical and neuropsychological data. This integrated computational approach would enable the creation of a predictive model to identify specific patterns of cognitive and motor impairment in MCI. Therefore, this new dimensional cognitive-behavioral assessment would reveal elderly people's neural alterations and impaired cognitive functions, typical of MCI and dementia, even in early stages for more time-sensitive interventions.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
Cavedoni, S., Chirico, A., Pedroli, E., Cipresso, P., & Riva, G. (2020). Digital Biomarkers for the Early Detection of Mild Cognitive Impairment: Artificial Intelligence Meets Virtual Reality. Frontiers in human neuroscience. https://doi.org/10.3389/fnhum.2020.00245
Vancouver
Cavedoni S, Chirico A, Pedroli E, Cipresso P, Riva G. Digital Biomarkers for the Early Detection of Mild Cognitive Impairment: Artificial Intelligence Meets Virtual Reality. Frontiers in human neuroscience. 2020. doi:10.3389/fnhum.2020.00245. PMID: 32848660.
BibTeX
@article{cavedoni2020-32848660,
author = {Cavedoni, S. and Chirico, A. and Pedroli, E. and Cipresso, P. and Riva, G.},
title = {{Digital Biomarkers for the Early Detection of Mild Cognitive Impairment: Artificial Intelligence Meets Virtual Reality}},
journal = {Frontiers in human neuroscience},
year = {2020},
doi = {10.3389/fnhum.2020.00245},
pmid = {32848660},
url = {https://doi.org/10.3389/fnhum.2020.00245}
}
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. Check the citation against the original before you use it.
Key findings
- VR can be used to create highly ecologically controlled simulations resembling the daily life contexts in which patients' daily instrumental activities (IADL) usually take place.
- Clinicians can record patients' kinematics, particularly gait, while performing IADL (Digital Biomarkers).
- Then, Artificial Intelligence employs Machine Learning (ML) to analyze them in combination with clinical and neuropsychological data.
- This integrated computational approach would enable the creation of a predictive model to identify specific patterns of cognitive and motor impairment in MCI.
- Therefore, this new dimensional cognitive-behavioral assessment would reveal elderly people's neural alterations and impaired cognitive functions, typical of MCI and dementia, even in early stages for more time-sensitive interventions.
Why it matters
This paper proposes combining virtual reality and artificial intelligence to detect mild cognitive impairment earlier using digital biomarkers such as gait. It is a proposed approach, not a tested treatment or prevention.
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Study details
- Evidence strengthLow
How this study scored 45/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, effect reported, studied in people and randomized / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Frontiers in human neuroscience) |
| Journal's citation record | 2 / 4 | Frontiers in Human Neuroscience: journal h-index 221 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 2 / 5 | 2020 — within 10 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomized / blinded | 0 / 2 | No randomization or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 4 / 7 | Cited 87 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 10.9 citations a year over 8 year(s) since 2020 |
| Impact for its field and year | 3 / 6 | Cited 5.8× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 1 / 6 | Cited by 1 other studies in this library |
| Still being cited | 3 / 4 | Cited 51 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 4 / 4 | Last-listed author Giuseppe Riva: h-index 104 (OpenAlex, whole career) |
| Institution's track record | 0 / 3 | Università Cattolica del Sacro Cuore: institution h-index 606 (OpenAlex) |
| Multicenter collaboration | 1 / 3 | OpenAlex lists 3 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 32848660) |
| Free to read | 2 / 2 | Free to read |
| Total | 45 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.3389/fnhum.2020.00245
- PubMed: 32848660
- Original source →
- Free full text: Frontiers in Human Neuroscience →
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
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