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Scalable diagnostic screening of mild cognitive impairment using AI dialogue agent
Fengyi Tang, Ikechukwu Uchendu, Fei Wang, Hiroko H Dodge, Jiayu Zhou. Scientific reports, 2020-03-31
Topic: Brain imaging, connectivity and assessment
The search for early biomarkers of mild cognitive impairment (MCI) has been central to the Alzheimer's Disease (AD) and dementia research community in recent years. To identify MCI status at the earliest possible point, recent studies have shown that linguistic markers such as word choice, utterance and sentence structures can potentially serve as preclinical behavioral markers. Here we present an adaptive dialogue algorithm (an AI-enabled dialogue agent) to identify sequences of questions (a dialogue policy) that distinguish MCI from normal (NL) cognitive status. Our AI agent adapts its questioning strategy based on the user's previous responses to reach an individualized conversational strategy per user. Because the AI agent is adaptive and scales favorably with additional data, our method provides a potential avenue for large-scale preclinical screening of neurocognitive decline as a new digital biomarker, as well as longitudinal tracking of aging patterns in the outpatient setting.
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APA 7th
Tang, F., Uchendu, I., Wang, F., Dodge, H. H., & Zhou, J. (2020). Scalable diagnostic screening of mild cognitive impairment using AI dialogue agent. Scientific reports. https://doi.org/10.1038/s41598-020-61994-0
Vancouver
Tang F, Uchendu I, Wang F, Dodge HH, Zhou J. Scalable diagnostic screening of mild cognitive impairment using AI dialogue agent. Scientific reports. 2020. doi:10.1038/s41598-020-61994-0. PMID: 32235884.
BibTeX
@article{tang2020-32235884,
author = {Tang, Fengyi and Uchendu, Ikechukwu and Wang, Fei and Dodge, Hiroko H and Zhou, Jiayu},
title = {{Scalable diagnostic screening of mild cognitive impairment using AI dialogue agent}},
journal = {Scientific reports},
year = {2020},
doi = {10.1038/s41598-020-61994-0},
pmid = {32235884},
url = {https://doi.org/10.1038/s41598-020-61994-0}
}
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Key findings
- To identify MCI status at the earliest possible point, recent studies have shown that linguistic markers such as word choice, utterance and sentence structures can potentially serve as preclinical behavioral markers.
Study details
- Study designlongitudinal study
- Evidence strengthModerate
How this study scored 55/100
Published in a peer-reviewed journal, and it is a longitudinal study following the same people over time. Scored zero for 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, multi-centre collaboration and free to read, so those were left out: it earned 31 of the 56 points we could judge it on, which is 55 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Scientific reports) |
| Journal's citation record | — / 4 | No journal record from OpenAlex — left out of this score |
| Study design | 7 / 12 | A longitudinal study following the same people over time |
| Strength of evidence | 3 / 5 | Rated “moderate” 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) |
| 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 32235884) |
| Free to read | — / 2 | Access not recorded — left out of this score |
| Points | 31 / 56 | Scored on 10 of 20 parts. We have no data for the rest, so they are left out: 31 of the 56 points we could judge it on is 55 out of 100. |
| Total | 55 / 100 | — |
What each of these 20 components measures, and what this score is not →
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