Development of predictive models to identify the intracranial aneurysm responsible for subarachnoid hemorrhage in...

To develop and test machine learning (ML) models using computed tomography angiography to identify the intracranial aneurysm (IA) responsible for subarachnoid hemorrhage (SAH) accurately in patients with multiple saccular IAs and to determine whether these models outperform traditional predictive markers.

Two hundred seven SAH patients with 460 IAs from four hospitals were included from May 2018-December 2023 and randomly divided into training (80%) and internal validation (20%) sets. Additionally, an external validation set comprising 65 patients with 147 IAs from other four hospitals was used. The predictive models were developed using ML methods that integrated the morphological features of IAs (e.g., size and shape) to identify the responsible IA. These models were then compared with traditional predictive markers that relies on hemorrhage patterns and the maximum IA size.

The areas under the curves (AUCs) for the hemorrhage patterns and the maximum IA size were 0.496-0.505, 0.502-0.523, and 0.488-0.498 in the training, internal validation, and external validation sets, respectively. Among the 13 ML models, the best-performing models were the Gaussian process, logistic regression, and quadratic discriminant analysis models, with AUCs of 0.912 [95 % confidence interval (CI): 0.881-0.943], 0.894 (95 % CI: 0.861-0.928), and 0.890 (95 % CI: 0.756-0.924), respectively, for the training set; 0.869 (95 % CI: 0.798-0.941), 0.872 (95 % CI: 0.802-0.942), and 0.853 (95 % CI: 0.778-0.929), respectively, for the internal validation set; and 0.898 (95 % CI: 0.848-0.947), 0.892 (95 % CI: 0.840-0.943), and 0.897 (95 % CI: 0.847-0.947), respectively, for the external validation set. DeLong tests revealed no significant differences among these models, but all the models outperformed traditional predictive markers (P < 0.001).

ML models that integrate multiple morphological features can predict the IA responsible for SAH accurately in patients with multiple IAs. These models outperform traditional predictive markers in identifying the responsible IA, thereby facilitating prompt and effective treatment.