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LiveDrive AI: A Pilot Study of a Machine Learning-Powered Diagnostic System for Real-Time, Non-Invasive Detection of Mild Cognitive Impairment
Al-Hindawi F, Serhan P, Geda YE, Tsow F, Wu T, Forzani E.. Bioengineering (Basel, Switzerland), 2025-01-17
Topic: Risk, prevention and care in population studies
Alzheimer's disease (AD) represents a significant global health issue, affecting over 55 million individuals worldwide, with a progressive impact on cognitive and functional abilities. Early detection, particularly of mild cognitive impairment (MCI) as an indicator of potential AD onset, is crucial yet challenging, given the limitations of current diagnostic biomarkers and the need for non-invasive, accessible tools. This study aims to address these gaps by exploring driving performance as a novel, non-invasive biomarker for MCI detection. Using the LiveDrive AI system, equipped with multimodal sensing (MMS) technology and a driving performance assessment strategy, the proposed work analyzes the predictive capacity of driving patterns in indicating cognitive decline. Machine learning models, trained on an expert-annotated in-house dataset, were employed to detect MCI status from driving performance. Key findings demonstrate the feasibility of using nuanced driving features, such as velocity and acceleration during turning, as indicators of cognitive decline. This approach holds promise for integration into smartphone or car applications, enabling real-time, continuous cognitive health monitoring. The implications of this work suggest a transformative step towards scalable, real-world solutions for early AD diagnosis, with the potential to improve patient outcomes and disease management.
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APA 7th
Al-Hindawi, F., Serhan, P., Geda, Y. E., Tsow, F., Wu, T., & Forzani, E. (2025). LiveDrive AI: A Pilot Study of a Machine Learning-Powered Diagnostic System for Real-Time, Non-Invasive Detection of Mild Cognitive Impairment. Bioengineering (Basel, Switzerland). https://doi.org/10.3390/bioengineering12010086
Vancouver
Al-Hindawi F, Serhan P, Geda YE, Tsow F, Wu T, Forzani E. LiveDrive AI: A Pilot Study of a Machine Learning-Powered Diagnostic System for Real-Time, Non-Invasive Detection of Mild Cognitive Impairment. Bioengineering (Basel, Switzerland). 2025. doi:10.3390/bioengineering12010086. PMID: 39851360.
BibTeX
@article{alhindawi2025-39851360,
author = {Al-Hindawi, F. and Serhan, P. and Geda, Y. E. and Tsow, F. and Wu, T. and Forzani, E.},
title = {{LiveDrive AI: A Pilot Study of a Machine Learning-Powered Diagnostic System for Real-Time, Non-Invasive Detection of Mild Cognitive Impairment}},
journal = {Bioengineering (Basel, Switzerland)},
year = {2025},
doi = {10.3390/bioengineering12010086},
pmid = {39851360},
url = {https://doi.org/10.3390/bioengineering12010086}
}
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Key findings
- Alzheimer's disease (AD) represents a significant global health issue, affecting over 55 million individuals worldwide, with a progressive impact on cognitive and functional abilities.
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 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 26 of the 58 points we could judge it on, which is 45 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Bioengineering (Basel, Switzerland)) |
| 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 | 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 | 4 / 5 | 2025 — within 3 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 39851360) |
| Free to read | 2 / 2 | Free to read |
| Points | 26 / 58 | Scored on 11 of 20 parts. We have no data for the rest, so they are left out: 26 of the 58 points we could judge it on is 45 out of 100. |
| Total | 45 / 100 | — |
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