A novel diagnostic approach to reduce artifacts caused by cerebral atrophy in regional CBF assessment of mild cognitive impairment (MCI) and Alzheimer's disease by [99mTc]-ECD and SPECT.
Bibliographic record
Abstract
Abstract Objectives Accurately evaluated regional cerebral blood flow (rCBF) by single photon emission computed tomography (SPECT) is increasingly important for the diagnosis of mild cognitive impairment (MCI) and early Alzheimer's disease (AD). We propose a novel diagnostic approach to minimize artifacts and Z-score over- or underestimation caused by cerebral atrophy, better reflecting rCBF, thus improving diagnoses accuracy. Methods [99mTc]-ECD and SPECT studies were performed on 15 cognitively normal patients, 40 patients with MCI, and 16 patients with probable AD. Patient SPECT images were compared with those of age-matched controls using SPM2. The Z-scores obtained were displayed on a standardized MRI based on the orbito-meatal line. The atrophy correction method was incorporated to reduce artifacts through the MRI masking procedure applied to the target patient. Regional Z-score, percent extent, and atrophy correction rate obtained using the masking procedure, Mask (+), were compared with those obtained without the mask, Mask (-). The atrophy correction rate was defined as follows: atrophy correction rate = [ZscoreMask (-) - ZscoreMask (+)]/ZscoreMask (-)*100. To assess the approach, the receiver operating characteristic curve analysis was conducted for five dementia-related regions. Results This approach significantly reduced artifacts, regional Z-score, and percent extent in most regions, leading to improved estimates. The mean atrophy correction rate ranged from 10.4 to 12.0%. In MCI and AD, the convexities of the frontal and parietal lobes and the posterior medial cerebrum were particularly sensitive to cerebral atrophy, and the Z-score Mask (-) were overestimated, whereas the posterior cingulate cortex and the cerebellum were less sensitive to artifacts. The diagnostic accuracy for MCI increased from 67% for Mask (-) to 69% for Mask (+), and for AD from 78% for Mask (-) to 82% for Mask (+). The mean Z-scores in the inferior parietal lobule were higher in AD than in MCI. Conclusion This approach demonstrated improved Z-score assessments with reduced artifacts and improved diagnostic accuracy. A semi-automatic implementation of this is recommended for routine clinical studies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".