Deep Learning for Detection of Intracranial Aneurysms From Computed Tomography Angiography Images

Background: The rates of the fatality and disability will be extremely high once the intracranial aneurysms rupture. Radiologic examination based on the computed tomography angiography (CTA) images is the preferred method for the diagnosis of intracranial aneurysms, where the interpretation of imaging data is usually performed by an experienced radiologist. However, the accuracy of the interpretation and the time spent highly rely on the professional competence of the radiologist, which may yield inconsistent diagnostic outcomes. In addition, with steadily increasing workloads of radiology departments for providing various imaging diagnostic services, the fatigue of radiologists due to overloading may cause them neglecting or mistaking interpretations. Therefore, the aim of this study was to develop a new method to automatically detect intracranial aneurysms from CTA images using deep learning based on convolutional neural network (CNN) implemented on the Deepmedic platform. This new method can serve as a clinical diagnostic tool or a preliminary reference for diagnosis of intracranial aneurysms. It will be of great clinical value in reducing the diagnosis time and the missed or mistaken diagnosis caused by radiologists' different professional levels or their fatigue. Methods: We implemented a deep learning architecture with a 3D CNN model on Deepmedic platform for automatic segmentation and detection of intracranial aneurysms from CTA images which were retrospectively collected. The retrospective dataset was preprocessed and then was divided into two parts: the training part (80 subjects) to train the CNN model, and the testing part (10 subjects) to evaluate the performance of the automatic detection system. Sensitivity, positive predictive value (PPV) and the false positives per case were evaluated with the reference standard of digital subtraction angiography (DSA) or intraoperative results. Findings: The overall sensitivity and PPV of this system for the detection of intracranial aneurysms from CTA images were 92.3% and 100%, and the segmentation sensitivity also was 92.3%. The performance of the system in the detection of intracranial aneurysms was related to their size: the detection sensitivity for tiny intracranial aneurysms with a diameter ≤ 3mm was 66.7%, whereas the sensitivities of detection for large and medium-sized intracranial aneurysms of > 3mm both were 100%. Interpretation: The deep learning architecture with a 3D CNN model on Deepmedic platform could reliably segment and detect intracranial aneurysms from CTA images with an extremely high sensitivity, especially for large and medium-sized intracranial aneurysms, and this required only very limited training samples. Moreover, the detection sensitivity of tiny intracranial aneurysms was higher than those reported recently in other studies. Funding Statement: This study was supported in part by National Natural Science Foundation of China (81601558), Breeding Foundation of Zhuhai People's Hospital of China (2019PY-16) and Medical Research Foundation of Zhuhai City of China (20191207A010017). Declaration of Interests: No authors report any financial relationships with commercial interests Ethics Approval Statement: This retrospective study was approved by the Ethics Committee of Zhuhai People’s Hospital and the First Affiliated Hospital of Harbin Medical University.