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

Clinical Snapshot

Key Findings

• P Value: Not statistically significant difference from fellow radiologist

• Effect Size: Sensitivity 92.1%, Specificity 84.6%

• Primary Outcome: Diagnostic accuracy for artery of Adamkiewicz and anterior spinal artery detection

• Nnt Or Sensitivity: Sensitivity 92.1% for detecting spinal arteries during bronchial artery embolization

• Confidence Interval: Not reported

Clinical Application

Requires implementation of deep learning infrastructure and integration with angiographic systems Relevant to Australian interventional radiology practice; may require TGA approval for clinical implementation as medical device software Patients undergoing bronchial artery embolization for hemoptysis in interventional radiology departments

Abstract

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.

References

1. 1.Zhang, C., Lin, H., Zhang, H., Guo, X., Zhang, Q., Xin, F., Luo, T., & Ao, G. (2026). A deep learning model for artery of Adamkiewicz detection in bronchial artery embolization: A multicenter retrospective study. Journal of Vascular and Interventional Radiology, 37(5), 108584. https://doi.org/10.1016/j.jvir.2026.108584