Subarachnoid Hemorrhage

Subarachnoid hemorrhage (SAH) is a life-threatening condition characterized by bleeding into the space surrounding the brain, often stemming from a ruptured aneurysm. Current research focuses on improving diagnosis and prognosis prediction using advanced image analysis techniques, particularly deep learning models like convolutional neural networks (CNNs) and transformer-based architectures (e.g., Swin UNETR). These AI-driven methods analyze initial CT scans to automatically segment hemorrhage volume and predict mortality, potentially leading to more accurate assessments and improved patient management. The development of robust and efficient AI tools holds significant promise for enhancing the speed and accuracy of SAH diagnosis and treatment planning.

Papers

December 29, 2023

A Fully Automated Pipeline Using Swin Transformers for Deep Learning-Based Blood Segmentation on Head CT Scans After Aneurysmal Subarachnoid Hemorrhage
Sergio Garcia Garcia, Santiago Cepeda, Ignacio Arrese, Rosario Sarabia
Swin Transformer Head Computed Tomography Blood Segmentation Subarachnoid Hemorrhage

August 25, 2023

Enhanced Mortality Prediction In Patients With Subarachnoid Haemorrhage Using A Deep Learning Model Based On The Initial CT Scan
Sergio Garcia-Garcia, Santiago Cepeda, Dominik Muller, Alejandra Mosteiro, Ramon Torne, Silvia Agudo, Natalia de la Torre, Ignacio Arrese, Rosario Sarabia
Deep Learning Model Patient Friendly Language Mortality Prediction Year Specific Mortality Surface Subarachnoid Hemorrhage

Subarachnoid Hemorrhage

Papers

A Fully Automated Pipeline Using Swin Transformers for Deep Learning-Based Blood Segmentation on Head CT Scans After Aneurysmal Subarachnoid Hemorrhage

Enhanced Mortality Prediction In Patients With Subarachnoid Haemorrhage Using A Deep Learning Model Based On The Initial CT Scan