Automatic diagnosis and measurement of intracranial aneurysms using deep learning in MRA raw images.

Background: The traditional procedure of intracranial aneurysm (IA) diagnosis and evaluation in MRA is manually operated, which is time-consuming and labor-intensive. In this study, a deep learning model was established to diagnose and measure IA automatically based on the original MR images. Methods: A total of 1,014 IAs (from 852 patients) from hospital 1 were included and randomly divided into training, testing, and internal validation sets in a 7:2:1 ratio. Additionally, 315 patients (179 cases with IA and 136 cases without IA) from hospital 2 were used for independent validation. A deep learning model of MR 3DUnet was established for IA diagnosis and size measurement. The true positive (TP), false positive (FP), false negative (FN), recall, sensitivity, and specificity indices were used to evaluate the diagnosis performance of MR 3DUnet. The two-sample Results: The fully automatic model processed the original MRA data in 13.6 s and provided real-time results, including IA diagnosis and size measurement. For the IA diagnosis, in the training, testing, and internal validation sets, the recall rates were 0.80, 0.75, and 0.79, and the sensitivities were 0.82, 0.75, and 0.75, respectively. In the independent validation set, the recall rate, sensitivity, specificity, and AUC were 0.71, 0.74, 0.77, and 0.75, respectively. Subgroup analysis showed a recall rate of 0.74 for IA diagnosis based on DSA. For IA size measurement, no significant difference was found between our MR 3DUnet and the manual measurements of DSA or MRA. Conclusion: In this study, a one-click, fully automatic deep learning model was developed for automatic IA diagnosis and size measurement based on 2D original images. It has the potential to significantly improve doctors' work efficiency and reduce patients' examination time, making it highly valuable in clinical practice.