Integrated CT pipeline for automatic intracranial hemorrhage evaluation with GPT-enhanced clinical decision support.

objectivesIntracranial hemorrhage (ICH) is a time-critical neurological emergency in which rapid CT-based assessment directly informs treatment decisions. This study aimed to develop an automated deep-learning pipeline to enhance ICH detection, segmentation, and localization, complemented by clinical decision-making support through a large language model. MATERIALS AND

methodsThe detection model was trained on 21,784 labeled and 3528 unlabeled CT scans from the RSNA dataset using semi-supervised learning. The segmentation model was trained on 1226 scans from the HS dataset to delineate six ICH subtypes. Hydrocephalus and midline-shift models were trained on a dedicated 507-scan subset of the HS dataset. Hemorrhage and edema locations were registered to standard brain regions to improve interpretability. For evaluation, the CQ500 dataset (491 patients) was used as an external validation and test cohort. Clinical recommendations were generated using the GPT-4o Assistants API based on published guidelines and trials.

resultsOn the test set, detection achieved an AUC of 0.96 (95% CI: 0.94-0.98), and segmentation yielded Dice values ranging from 0.71 to 0.93 with corresponding 95% CIs from 0.61-0.76 to 0.90-0.96, while volume estimation showed high concordance (CCC 0.820-0.996). Intraparenchymal hemorrhage (IPH) localization demonstrated strong agreement with κ values of 0.85-1.00 across brain regions. Clinical decisions generated by the pipeline were highly rated, with one neurosurgeon assigning median scores of 4 and 5 for examination and treatment, and the other assigning 5 for both.

conclusionsThis deep learning pipeline combines imaging analysis with actionable clinical decisions, demonstrating significant potential as a valuable tool for emergency care. KEY POINTS: Question Rapid and accurate identification of ICH on CT is critical for guiding treatment, yet remains difficult using standard emergency radiological evaluation. Findings The end-to-end artificial intelligence pipeline achieved high accuracy in ICH detection, segmentation, and localization, with strong concordance to manual measurements and reliable clinical recommendations. Clinical relevance By automating image analysis and clinical decision-making, the pipeline demonstrated significant potential to reduce diagnostic delays, improve treatment guidance, and enhance patient outcomes in emergency care settings.