Supervised machine learning quality control for magnetic resonance artifacts in neonatal data sets
Bibliographic record
Abstract
Quality control (QC) of brain magnetic resonance images (MRI) is an important process requiring a significant amount of manual inspection. Major artifacts, such as severe subject motion, are easy to identify to naïve observers but lack automated identification tools. Clinical trials involving motion-prone neonates typically pool data to obtain sufficient power, and automated quality control protocols are especially important to safeguard data quality. Current study tested an open source method to detect major artifacts among 2D neonatal MRI via supervised machine learning. A total of 1,020 two-dimensional transverse T2-weighted MRI images of preterm newborns were examined and classified as either QC Pass or QC Fail. Then 70 features across focus, texture, noise, and natural scene statistics categories were extracted from each image. Several different classifiers were trained and their performance was compared with subjective rating as the gold standard. We repeated the rating process again to examine the stability of the rating and classification. When tested via 10-fold cross validation, the random undersampling and adaboost ensemble (RUSBoost) method achieved the best overall performance for QC Fail images with 85% positive predictive value along with 75% sensitivity. Similar classification performance was observed in the analyses of the repeated subjective rating. Current results served as a proof of concept for predicting images that fail quality control using no-reference objective image features. We also highlighted the importance of evaluating results beyond mere accuracy as a performance measure for machine learning in imbalanced group settings due to larger proportion of QC Pass quality images.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".