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NeuroAIreh@b: an artificial intelligence-based methodology for personalized and adaptive neurorehabilitation
Faria AL, Almeida Y, Branco D, Câmara J, Cameirão M, Ferreira L, Moreira A, Paulino T et al.. Frontiers in neurology, 2024-01-19
Topic: Amyloid, tau and blood-test diagnosis
Cognitive impairments are a prevalent consequence of acquired brain injury, dementia, and age-related cognitive decline, hampering individuals' daily functioning and independence, with significant societal and economic implications. While neurorehabilitation represents a promising avenue for addressing these deficits, traditional rehabilitation approaches face notable limitations. First, they lack adaptability, offering one-size-fits-all solutions that may not effectively meet each patient's unique needs. Furthermore, the resource-intensive nature of these interventions, often confined to clinical settings, poses barriers to widespread, cost-effective, and sustained implementation, resulting in suboptimal outcomes in terms of intervention adaptability, intensity, and duration. In response to these challenges, this paper introduces NeuroAIreh@b, an innovative cognitive profiling and training methodology that uses an AI-driven framework to optimize neurorehabilitation prescription. NeuroAIreh@b effectively bridges the gap between neuropsychological assessment and computational modeling, thereby affording highly personalized and adaptive neurorehabilitation sessions. This approach also leverages virtual reality-based simulations of daily living activities to enhance ecological validity and efficacy. The feasibility of NeuroAIreh@b has already been demonstrated through a clinical study with stroke patients employing a tablet-based intervention. The NeuroAIreh@b methodology holds the potential for efficacy studies in large randomized controlled trials in the future.
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APA 7th
Faria, A. L., Almeida, Y., Branco, D., Câmara, J., Cameirão, M., Ferreira, L., et al. (2023). NeuroAIreh@b: an artificial intelligence-based methodology for personalized and adaptive neurorehabilitation. Frontiers in neurology. https://doi.org/10.3389/fneur.2023.1258323
Vancouver
Faria AL, Almeida Y, Branco D, Câmara J, Cameirão M, Ferreira L, et al. NeuroAIreh@b: an artificial intelligence-based methodology for personalized and adaptive neurorehabilitation. Frontiers in neurology. 2023. doi:10.3389/fneur.2023.1258323. PMID: 38322797.
BibTeX
@article{faria2023-38322797,
author = {Faria, A. L. and Almeida, Y. and Branco, D. and Câmara, J. and Cameirão, M. and Ferreira, L. and Moreira, A. and Paulino, T. and others},
title = {{NeuroAIreh@b: an artificial intelligence-based methodology for personalized and adaptive neurorehabilitation}},
journal = {Frontiers in neurology},
year = {2023},
doi = {10.3389/fneur.2023.1258323},
pmid = {38322797},
url = {https://doi.org/10.3389/fneur.2023.1258323}
}
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Key findings
- Cognitive impairments are a prevalent consequence of acquired brain injury, dementia, and age-related cognitive decline, hampering individuals' daily functioning and independence, with significant societal and economic implications.
- The feasibility of NeuroAIreh@b has already been demonstrated through a clinical study with stroke patients employing a tablet-based intervention.
Study details
- Evidence strengthHigh
How this study scored 50/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, effect reported, studied in people and randomised / blinded because the record does not state it. We have no data on journal's citation record, times cited, citations per year, impact for its field and year, cited by studies here, still being cited, senior author's track record, institution's track record and multi-centre collaboration, so those were left out: it earned 29 of the 58 points we could judge it on, which is 50 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Frontiers in neurology) |
| Journal's citation record | — / 4 | No journal record from OpenAlex — left out of this score |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 5 / 5 | Rated “high” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 3 / 5 | 2023 — within 6 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) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | — / 7 | No citation count (not found in OpenAlex) — left out of this score |
| Citations per year | — / 5 | No citation count or no year — left out of this score |
| Impact for its field and year | — / 6 | No field-weighted citation impact (OpenAlex has none yet) — left out of this score |
| Cited by studies here | — / 6 | Not yet checked against this library's citation graph — left out of this score |
| Still being cited | — / 4 | No yearly citation counts — left out of this score |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | — / 3 | No institution listed in OpenAlex — left out of this score |
| Multi-centre collaboration | — / 3 | Institutions not listed — left out of this score |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 38322797) |
| Free to read | 2 / 2 | Free to read |
| Points | 29 / 58 | Scored on 11 of 20 parts. We have no data for the rest, so they are left out: 29 of the 58 points we could judge it on is 50 out of 100. |
| Total | 50 / 100 | — |
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