Abstract TP58: A Novel Deep Learning Approach for Automated Diagnosis of Cerebral Infarction on Computed Tomography
Bibliographic record
Abstract
Introduction: Deep learning is a novel machine learning approach that enables automated extraction and classification of imaging features. We aimed to use deep learning to enhance detection of brain infarction. Methods: Patients with acute ischemic stroke admitted to our institution between 2011-2015 were prospectively registered in an IRB-approved database. On a subset of these patients, radiologists annotated the images by marking infarct area. Data was split randomly into a training set and test set (80:20). Deep learning models, including a 3D multi-scale fully convolutional neural network, were developed and trained on the training set and independently tested on the test set. The performance of this model was compared to the expert consensus interpretation. Diagnostic test characteristics including the area under the curve (AUC) for the deep learning algorithm were calculated both at a voxel and imaging-study level. Computer-generated heat maps were created to denote the possibility of infarct. Results: The study group included 114 patients (training set n = 92, test set n = 22). In the training set of 5,888 images, infarction was present in 602 (10.2%) images. In the testing set of 920 images, infarction was present in 130 (14.1%) images. A total of 1.5 billion voxels were used to train the model. The AUC for the deep learning algorithm for voxel accuracy was 0.973 (95% CI 0.972-0.974). Voxel accuracy, sensitivity, and specificity were 92%, 93%, and 92%, respectively. Positive predictive value (PPV) and negative predictive value (NPV) were 86% and 92%, respectively. The AUC for the deep learning algorithm for automated diagnosis of infarction at the imaging-study level was 0.91 (95% CI 0.90-0.94). Diagnostic accuracy, sensitivity, and specificity were 88%, 65%, and 91%, respectively. PPV and NPV were 49% and 95%, respectively. Conclusions: A machine-learned algorithm employing a novel deep learning algorithm enabled accurate diagnosis of brain infarction.
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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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".