Abstract
Intracranial aneurysms, a brain condition caused by weakened blood vessels, result in abnormal bulging or dilation of the intracranial artery walls. Up to 5% of the global population will suffer from this disease, and early detection is critical. Due to their small size, aneurysms are often missed in initial patient image assessments. Further, the process of interpreting 3D medical images can be time-consuming, error-prone, and require trained radiologists. This study implemented a deep learning model using a Transformer Neural Network to enable the automated computer-assisted detection of aneurysms in 3D TOF-MRA images.
This novel Transformer model demonstrated overall 91.9% patient-level sensitivity and 95.1% patient specificity in detecting intracranial aneurysms. Additionally, for aneurysms exceeding 5 mm in diameter, it reached patient sensitivity of 95.8% and patient specificity of 96.4%. To our knowledge, this is the first study to implement a Transformer Deep Neural Network for the detection of aneurysms from 3D volumetric TOF-MRA images. The successful results underscore the potential of Transformer models in accelerating diagnoses of cerebrovascular diseases while streamlining radiologists’ workflow. This detection method can be applicable for identifying aneurysms in other organs and diseases such as brain tumors.