A multimodal multitask deep learning model for predicting stroke lesion and functional outcomes using 4D CTP imaging and clinical metadata
Bibliographic record
Abstract
Acute ischemic stroke is a major global health challenge, leading to long-term disability or death without timely intervention. Among neuroimaging modalities, spatio-temporal (4D) computed tomography perfusion (CTP) is widely used to assess cerebral blood flow and guide acute treatment decisions. Beyond this role, recent studies have demonstrated its potential to predict lesion outcomes (irreversible tissue damage at follow-up) and functional outcomes (long-term functional independence). Although inherently related, these outcomes are typically modeled and predicted separately, ignoring shared patterns that could enhance predictive accuracy. Multitask learning provides a promising solution by leveraging shared representations across both related tasks. However, only a few stroke studies have adopted this approach so far, and those that did primarily relied on imaging data alone. Thus, we developed CTPredict, the first multimodal, multitask deep learning model that simultaneously predicts follow-up stroke lesions and functional outcomes (specifically, 90-day modified Rankin Scale) from 4D CTP imaging and clinical metadata. CTPredict integrates modality-specific encoders for feature extraction, a multimodal fusion module with cross-attention mechanisms to focus on relevant features from both data sources, and task-specific branches for outcome prediction, all within a computationally efficient framework for multitask learning. Evaluated on a challenging multi-center dataset of 111 AIS patients, CTPredict achieved a 0.23 Dice score and 0.77 accuracy for the lesion and functional outcome prediction tasks, respectively, outperforming single-task variants (0.21 Dice score, 0.73 accuracy). These results demonstrate the benefits of multitask learning and highlight CTPredict's potential to enable more streamlined, data-driven, and personalized stroke outcome predictions in clinical practice.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".