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A Deep Learning Model for Artery of Adamkiewicz Detection in Bronchial Artery Embolization: A Multicenter
Chao Zhang1, Hu Lin2, Hongwei Zhang3
1Department of General Surgery, Xuanwu Hospital of Capital Medical University, Beijing, People's Republic of China; Oncology and Vascular Intervention Center, National Clinical Research Center for Geriatric Diseases, Beijing, People's Republic of China.
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.
