Corneal nerve and brain imaging in mild cognitive impairment and dementia: A cross-sectional study
Bibliographic record
Abstract
Abstract Background: Visual rating of medial temporal lobe atrophy (MTA) is an accepted biomarker of Alzheimer’s disease. Corneal confocal microscopy (CCM) is a non-invasive ophthalmic imaging biomarker of neurodegeneration. We sought to determine the diagnostic accuracy of CCM to distinguish mild cognitive impairment (MCI) and dementia from no cognitive impairment (NCI) in relation to MTA rating.Methods: Subjects aged 60-85 with NCI, MCI and dementia were recruited from the geriatric and memory clinic in Rumailah Hospital, Doha, Qatar between 18/09/16 and 31/07/19. The diagnosis of MCI and dementia were based on the International Classification of Diseases (ICD-10) criteria. Subjects underwent cognitive screening using the Montreal Cognitive Assessment (MoCA), CCM and MTA rating on MRI. Statistical tests used were ANOVA with Bonferroni’s post hoc test, kappa statistics and receiver operating characteristic (ROC) curve analysis. A two-tailed P value of ≤0.05 was considered significant.Results: 182 subjects with NCI (n=36), MCI (n=80) and dementia (n=66), including AD (n=19, 28.8%), VaD (n=13, 19.7%) and combined AD (n=34, 51.5%) were studied. CCM showed a progressive reduction in corneal nerve fiber density (CNFD, fibers/mm2) (32.0±7.5 vs 24.5±9.6 vs 20.8±9.3, p<0.0001), branch density (CNBD, branches/mm2) (90.9±46.5 vs 59.3±35.7 vs 53.9±38.7, p<0.0001), and fiber length (CNFL, mm/mm2) (22.9±6.1 vs 17.2±6.5 vs 15.8±7.4, p<0.0001), in subjects with MCI and dementia compared to NCI. The MTA rating in the dementia group was significantly higher compared with the NCI and MCI group in the right (1.9±1.0 vs 0.5±0.6 and 0.6±0.8, p<0.0001) and left (2.1±1.1 vs 0.6±0.7 and 0.8±0.8, p<0.0001) hemispheres. The area under the ROC curve (95% CI) for the diagnostic accuracy of CNFD, CNBD, CNFL vs MTA-right and -left for MCI was 78% (67-90%), 82% (72-92%), 86% (77-95%) vs 53% (36-69%) and 40% (25-55%), respectively, and for dementia it was 85% (76-94%), 84% (75-93%), 85% (76-94%) vs 86% (76-96%) and 82% (72-92%), respectively.Conclusions: The diagnostic accuracy of CCM, a non-invasive ophthalmic biomarker of neurodegeneration was high and comparable with MTA rating for dementia and superior to MTA rating for MCI.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".