Deep learning using structural MRI massively improves prediction accuracy of body mass index
Bibliographic record
Abstract
Summary Obesity is a major public health problem globally and there is considerable interest in the neural mechanisms in food overconsumption. Artificial intelligence (AI), particularly machine learning, has shown promise in characterizing links between brain morphometry and obesity. In 1106 adults, compared to other forms of machine learning, deep learning using 3D convolutional neural networks (3D-CNN) dramatically improves prediction of body mass index (BMI). The 3D-CNN model robustly predicted BMI ( R 2 =.325), outperforming random forest, elastic net, and tabnet models ( R 2 s<.07) in a ‘lockbox’ sample. Explainable AI analyses revealed the specific brain regions implicated and these regions were moderately associated with delay discounting, fluid cognition, gait speed, dexterity, and alcohol use. Collectively, these findings reveal the value of deep learning for understanding of the neural basis and motivational processes in the neurobiology of obesity.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".