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Enhancing Mild Cognitive Impairment Auxiliary Identification Through Multimodal Cognitive Assessment with Eye Tracking and Convolutional Neural Network Analysis
Na Li, Ziming Wang, Wen Ren, Hong Zheng, Shuai Liu, Yi Zhou, Kang Ju, Zhongting Chen. Biomedicines, 2025-03-18
Topic: Risk, prevention and care in population studies
Overall, the combined features model achieved a higher discrimination accuracy than models with single feature sets alone. Specifically, the model’s ability to differentiate MCI from healthy individuals, including young adults, reached an average accuracy of 74.62%. For distinguishing MCI from elderly controls, the model’s accuracy averaged 66.50%. Results show that a multimodal model significantly outperforms single-feature models in identifying MCI, highlighting the potential of eye-tracking for early detection. These findings suggest that integrating multimodal data can enhance the effectiveness of MCI auxiliary identification, providing a novel potential pathway for community-based early detection efforts.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
Li, N., Wang, Z., Ren, W., Zheng, H., Liu, S., Zhou, Y., et al. (2025). Enhancing Mild Cognitive Impairment Auxiliary Identification Through Multimodal Cognitive Assessment with Eye Tracking and Convolutional Neural Network Analysis. Biomedicines. https://doi.org/10.3390/biomedicines13030738
Vancouver
Li N, Wang Z, Ren W, Zheng H, Liu S, Zhou Y, et al. Enhancing Mild Cognitive Impairment Auxiliary Identification Through Multimodal Cognitive Assessment with Eye Tracking and Convolutional Neural Network Analysis. Biomedicines. 2025. doi:10.3390/biomedicines13030738. PMID: 40149714.
BibTeX
@article{li2025-40149714,
author = {Li, Na and Wang, Ziming and Ren, Wen and Zheng, Hong and Liu, Shuai and Zhou, Yi and Ju, Kang and Chen, Zhongting},
title = {{Enhancing Mild Cognitive Impairment Auxiliary Identification Through Multimodal Cognitive Assessment with Eye Tracking and Convolutional Neural Network Analysis}},
journal = {Biomedicines},
year = {2025},
doi = {10.3390/biomedicines13030738},
pmid = {40149714},
url = {https://doi.org/10.3390/biomedicines13030738}
}
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Key findings
- Overall, the combined features model achieved a higher discrimination accuracy than models with single feature sets alone.
- Specifically, the model’s ability to differentiate MCI from healthy individuals, including young adults, reached an average accuracy of 74.62%.
- For distinguishing MCI from elderly controls, the model’s accuracy averaged 66.50%.
- Results show that a multimodal model significantly outperforms single-feature models in identifying MCI, highlighting the potential of eye-tracking for early detection.
- These findings suggest that integrating multimodal data can enhance the effectiveness of MCI auxiliary identification, providing a novel potential pathway for community-based early detection efforts.
Why it matters
This study found that a multimodal model combining eye tracking and convolutional neural network analysis identified mild cognitive impairment with an average accuracy of 74.62% against healthy individuals including young adults, and 66.50% against elderly controls. It is about an auxiliary identification method and does not treat, cure or prevent Alzheimer's disease.
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Study details
- Participants128
- Effect size74.62%; 66.50%
- Evidence strengthLow
How this study scored 41/100
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 (Biomedicines) |
| Journal's citation record | 1 / 4 | Biomedicines: journal h-index 133 (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 | 2 / 4 | 128 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 | 1 / 7 | Cited 3 times (OpenAlex, 2026-10-10) |
| Citations per year | 1 / 5 | 1.0 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 2 / 6 | Cited 1.9× 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 | 1 / 4 | Cited 3 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 1 / 4 | Last-listed author Zhongting Chen: h-index 13 (OpenAlex, whole career) |
| Institution's track record | 0 / 3 | East China Normal University: institution h-index 506 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 2 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 40149714) |
| Free to read | 2 / 2 | Free to read |
| Total | 41 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
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