Multimodal Deep Learning for Pediatric Mild Traumatic Brain Injury Detection
Bibliographic record
Abstract
Despite its prevalence, little is known about the pathophysiology of mild traumatic brain injury (mTBI). This makes it difficult for clinicians to accurately diagnose mTBI and to predict outcomes in affected children, thereby highlighting the urgent need to identify novel and efficacious biomarkers of pediatric mTBI. To address this important knowledge gap, this study introduced a multimodal magnetic resonance imaging (MRI) based deep learning approach toward the classification of mTBI as compared with mild orthopedic injury (OI) by considering both structural MRI (sMRI) and diffusion tensor imaging (DTI). Firstly, convolutional features were extracted by employing a pre-trained DenseNet to capture the morphological features of both modalities. Next, by employing Deep Canonical Correlation Analysis (DCCA), distinct features obtained from the sMRI and DTI data were integrated into a multi-modal embedding. The obtained DCCA fused compact multimodal features were then fed to a random forest (RF) classifier that was used to classify mTBI versus mild OI. Additionally, to visualize the intra-individually heterogeneous brain regions that DenseNet most heavily relied upon for making classification, Gradient-weighted Class Activation Mapping (Grad-CAM) was applied to the DenseNet outcomes for both modalities. According to the experimental outcomes on the clinical dataset, the introduced multimodal deep learning strategy improved the classification accuracy by 8.6% (from 75.8% to 84.4%) and 7.8% (from 76.6% to 84.4%) when compared to the unimodal morphological features, as generated from sMRI and DTI.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".