Overview on Incorporating Computer-Aided Diagnosis Systems for Dementia
Bibliographic record
Abstract
Dementia is one of the major issues in public health all over the world. Alzheimer's disease (AD) is its most common and famous form. Late detection of AD has irreparable effects for the people suffering from it. Cognitive assessment tests are the conventional approach to detect AD. They are quick to do, and not costly. However, they have low predictive values. Therefore, other ways such as magnetic resonance imaging (MRI) are used. Recently, advances in computer-aided diagnosis system (CADS) using MRI have provided useful information in the quantitative evaluation of AD at an early stage. Although it cannot be substituted with the doctors, but it helps. Many algorithms for CADS were presented, which means CADS is one of the growing techniques in this field. Because there is no standardized approach to determine the best one, it is essential to be familiar with general approaches to design a CADS. This chapter deals with a general approach for design and develop a reliable CADS using biomarkers extracted from MRI. The advancement of using CAS and MRI for AD are discussed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.059 | 0.037 |
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".