A Novel <i>m</i>CAD for pediatric metabolic brain diseases incorporating DW imaging and MR spectroscopy
Bibliographic record
Abstract
Abstract With the increase in the number of identified rare diseases and the intricacy involved in diagnosis, as exemplified by metabolic brain diseases, the need for computerized diagnostic systems is inevitable. We propose a pilot computer‐assisted medical decision support system (mCAD) which tries to identify and further categorize these diseases, utilizing the information available from magnetic resonance spectroscopy (MRS) and diffusion‐weighted imaging (DWI). In this study, we have utilized wavelets, fuzzy relational classifiers and a collection of signal/image processing routines to extract and to classify disease features. The combined MRS+ DWI system achieved a sensitivity (Se) and positive predictivity (PP) of 65.00% and 72.22%, respectively, in detecting seven categories of metabolic brain diseases. The combined MRS+ DWI system exhibits a 10% and 3.47% increase in Se and PP, respectively, in comparison to the system using only DWI information. It also increases the Se and PP of the system using only the MRS information by 15% and 22.22%, respectively.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".