DSA Image Analysis of Clinical Features and Nursing Care of Cerebral Aneurysm Patients Based on the Deep Learning Algorithm

Abstract

Objective. A deep learning algorithm was developed for automatic detection and localization of intracranial aneurysms in DSA, and its clinical characteristics were analyzed, and targeted nursing measures were formulated. Methods. Using a retrospective multicenter study method based on radiology reports, DSA images of aneurysms were randomly divided into 75 cases in the training set, 20 cases in the internal test set, and 35 cases in the external test set. Using a computer-aided detection method based on the three-dimensional U-Net (3D U-Net), after preprocessing DSA images, automatic segmentation of intracranial blood vessels is performed to obtain regions of interest, and based on the segmentation results, physicians’ annotations are introduced. The 3D U-Net network model is trained and adjusted, and the obtained model is used to automatically detect the cerebral aneurysm area. Results. Fivefold cross-validation was used for the training set and the internal test set, and a sensitivity of $\left(94.4\pm 1.1\right)\%$ was obtained. Automatic detection of aneurysms was performed on the external test set, and the average false positive rate was 0.86 FPs/case (false positives/case). The resulting sensitivity was 82.9%. The classification comparison of external test sets showed that the sensitivity of the method for detecting aneurysms with sizes of 5.00~<10.00 mm and ≥10.00 mm (88.2% and 100.0%) was higher than that for aneurysms with sizes of <3.00 mm and 3.00~<5.00 mm (50.0% and 72.7%). The sensitivity of patients aged 50-60 years and >60 years (90.0% and 87.5%) was higher than that of patients aged <50 years (66.7%), and there was little difference between different genders (84.6% in males and 81.8% in females). Conclusion. The deep learning algorithm has high diagnostic performance in detecting intracranial aneurysms, which is verified by external datasets.