Abstract 141: Artificial Intelligence to Improve the Detection and Triage of Cerebral Aneurysms
Bibliographic record
Abstract
Cerebral Aneurysms (CAs) may occur in 5-10% of the population. They can be often missed because they require a very methodological diagnostic approach. We developed an algorithm using artificial intelligence to assist and supervise and detect CAs. Methods: We developed an automated algorithm to detect CAs. The algorithm is based on 3D convolutional neural network modeled as a U-net. We included all saccular CAs from 2014 to 2016 from a single center. Normal and pathological datasets were prepared and annotated in 3D using an in-house developed platform. To assess the accuracy and to optimize the model, we assessed preliminary results using a validation dataset. After the algorithm was trained, a dataset was used to evaluate final IA detection and aneurysm measurements. The accuracy of the algorithm was derived using ROC curves and Pearson correlation tests. Results: We used 528 CTAs with 674 aneurysms at the following locations: ACA (3%), ACA/ACOM (26.1%), ICA/MCA (26.3%), MCA (29.4%), PCA/PCOM (2.3%), Basilar (6.6%), Vertebral (2.3%) and other (3.7%). Training datasets consisted of 189 CA scans. We plotted ROC curves and achieved an AUC of 0.85 for unruptured and 0.88 for ruptured CAs. We improved the model performance by increasing the training dataset employing various methods of data augmentation to leverage the data to its fullest. The final model tested was performed in 528 CTAs using 5-fold cross-validation and an additional set of 2400 normal CTAs. There was a significant improvement compared to the initial assessment, with an AUC of 0.93 for unruptured and 0.94 for ruptured. The algorithm detected larger aneurysms more accurately, reaching an AUC of 0.97 and a 91.5% specificity at 90% sensitivity for aneurysms larger than 7mm. Also, the algorithm accurately detected CAs in the following locations: basilar(AUC of 0.97) and MCA/ACOM (AUC of 0.94). The volume measurement (mm3) by the model compared to the annotated one achieved a Pearson correlation of 99.36. Conclusion: The Viz.ai aneurysm algorithm was able to detect and measure ruptured and unruptured CAs in consecutive CTAs. The model has demonstrated that a deep learning AI algorithm can achieve clinically useful levels of accuracy for clinical decision support.
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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 | 0.002 | 0.008 |
| 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 | 0.002 | 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".