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49/100

✓ Peer-reviewed alzhub usefulness score 49 out of 100. Mild cognitive impairment

Topic: Amyloid, tau and blood-test diagnosis

Detecting Alzheimer's disease (AD) in its earliest stages, particularly during an onset of Mild Cognitive Impairment (MCI), remains challenging due to the overlap of initial symptoms with normal aging processes. Given that no cure exists and current medications only slow the disease's progression, early identification of at-risk individuals is crucial. The combination of eye-tracking and speech analysis offers a promising diagnostic solution by providing a non-invasive method to examine differences between healthy controls and individuals with MCI, who may progress to develop AD. In this study, we analyzed a multimodal clinical eye-tracking and speech dataset collected from 78 participants (37 controls, 20 MCI, and 21 AD) during the King-Devick test and a reading task to classify and diagnose MCI/AD versus healthy controls. To that end, we developed a Fusion Neural Network, a deep learning-based classification model that integrates gaze and speech-derived features, including pupil size variations, fixation duration, saccadic movements, and speech delay, to improve MCI diagnosis performance. We achieved an average classification accuracy of 79.2% for MCI diagnosis and 82% for AD. Our findings indicate that features related to pupil size and eye-speech temporal dynamics are strong indicators for detection tasks. Moreover, the results indicate that using multimodal data (gaze + speech) significantly improves classification accuracy compared to unimodal data from speech or gaze alone.

Cite this study APA, Vancouver, BibTeX · download .bib or .ris

APA 7th

Shah, H. A., Andberg, S., Koivisto, A. M., & Bednarik, R. (2025). A Multimodal Approach for Early Identification of Mild Cognitive Impairment and Alzheimer’s Disease With Fusion Network Using Eye Movements and Speech. IEEE Transactions on Neural Systems and Rehabilitation Engineering. https://doi.org/10.1109/tnsre.2025.3561043

Vancouver

Shah HA, Andberg S, Koivisto AM, Bednarik R. A Multimodal Approach for Early Identification of Mild Cognitive Impairment and Alzheimer’s Disease With Fusion Network Using Eye Movements and Speech. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2025. doi:10.1109/tnsre.2025.3561043. PMID: 40232896.

BibTeX

@article{shah2025-40232896,
  author  = {Shah, Hasnain Ali and Andberg, Sami and Koivisto, Anne Maria and Bednarik, Roman},
  title   = {{A Multimodal Approach for Early Identification of Mild Cognitive Impairment and Alzheimer’s Disease With Fusion Network Using Eye Movements and Speech}},
  journal = {IEEE Transactions on Neural Systems and Rehabilitation Engineering},
  year    = {2025},
  doi     = {10.1109/tnsre.2025.3561043},
  pmid    = {40232896},
  url     = {https://doi.org/10.1109/tnsre.2025.3561043}
}

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Key findings

In this study, we analyzed a multimodal clinical eye-tracking and speech dataset collected from 78 participants (37 controls, 20 MCI, and 21 AD) during the King-Devick test and a reading task to classify and diagnose MCI/AD versus healthy controls.
Our findings indicate that features related to pupil size and eye-speech temporal dynamics are strong indicators for detection tasks.
Moreover, the results indicate that using multimodal data (gaze + speech) significantly improves classification accuracy compared to unimodal data from speech or gaze alone.

Why it matters

This study used eye movements and speech, analyzed with a fusion neural network, to classify mild cognitive impairment and Alzheimer's disease versus healthy controls in 78 participants. It is a small diagnostic study and does not test any treatment, cure or prevention.

Study details

Participants78
Effect size79.2%
Evidence strengthLow

How this study scored 49/100

Published in a peer-reviewed journal. Scored zero for study design and randomised / blinded because the record does not state it.

Component	Points	Why
Journal tier	7 / 10	Tier 3 — peer-reviewed journal (IEEE Transactions on Neural Systems and Rehabilitation Engineering)
Journal's citation record	2 / 4	IEEE Transactions on Neural Systems and Rehabilitation Engineering: journal h-index 190 (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	4 / 5	2025 — within 3 year(s) of the newest study here
Sample size	1 / 4	78 participants
Effect reported	2 / 3	Effect reported as a number, without an interval or test
Studied in people	3 / 3	Studied in people (participant count stated)
Randomised / blinded	0 / 2	No randomisation or blinding stated — scores zero (not stated in the record — scored 0)
Times cited	3 / 7	Cited 15 times (OpenAlex, 2026-10-10)
Citations per year	2 / 5	5.0 citations a year over 3 year(s) since 2025
Impact for its field and year	4 / 6	Cited 9.5× the average for papers of its field and year (OpenAlex FWCI)
Cited by studies here	0 / 6	No other study in this library cites it
Still being cited	2 / 4	Cited 15 times in 2024–2026 (2026 still in progress; OpenAlex)
Senior author's track record	2 / 4	Last-listed author Roman Bednarik: h-index 30 (OpenAlex, whole career)
Institution's track record	1 / 3	University of Helsinki: institution h-index 1,201 (OpenAlex)
Multi-centre collaboration	1 / 3	OpenAlex lists 2 institution(s) in 1 country
Indexed in PubMed	2 / 2	Indexed in PubMed (PMID 40232896)
Free to read	2 / 2	Free to read
Total	49 / 100	—

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