A Deep Learning Model for Artery of Adamkiewicz Detection in Bronchial Artery Embolization: A Multicenter Retrospective Study.

PURPOSE: To evaluate the effectiveness of a deep learning model for recognizing the artery of Adamkiewicz and anterior spinal artery (ASA) to prevent spinal artery non-target embolization during bronchial artery embolization (BAE). MATERIALS AND METHODS: This multicenter, retrospective study included 2036 patients from January 2019 to December 2023 with hemoptysis who underwent de novo BAE. A deep learning-based framework was proposed for spinal artery identification, comprising region of interest (ROI) perception and target spinal artery identification. The ROI perception extracted vessel-related regions to improve the conspicuity of the spinal artery. The target spinal artery identification utilized a progressive refinement learning network, localizing the artery from the global view, and progressively refined the identification results through cross-scale information interaction. RESULTS: Seventy-eight patients (3.8%) had an identifiable artery of Adamkiewicz and ASA on right intercostal-bronchial artery angiography. The sensitivity of the proposed method was 92.1% and the specificity was 84.6%, with no statistically significant difference from a fellow radiologist interpretation. CONCLUSIONS: The deep learning system for spinal artery detection during BAE exhibited high sensitivity and performance, comparable to radiology fellows.