Validation of a deep-learning based thrombus classifier on digital subtraction angiography using a large-scale dataset.

PURPOSE: Digital subtraction angiography (DSA) interpretation is observer dependent. This study evaluated the diagnostic performance of an existing deep-learning (DL) based thrombus classifier prior to clinical application. The intended use of the model is as a clinical decision-support tool to assist neuroradiological assessment during mechanical recanalization. METHODS: This retrospective study included an in-house dataset of DSA image series from endovascular recanalization procedures for anterior circulation occlusions collected over two years. For each case, two DSA runs were selected: one before and one after recanalization. The artificial intelligence system was applied to classify thrombus presence. Diagnostic performance was assessed using sensitivity, specificity, and false-positive rate. RESULTS: A total of 1,236 DSA series from 309 patients were analyzed, yielding 618 paired biplane acquisitions. The DL classifier achieved an overall sensitivity of 71.7% (95% CI 67.3-75.8%), with the highest sensitivity for proximal vessel occlusions (M1/M2 segments: 87.6%; 95% CI 83.6-90.9%) and substantially lower sensitivity for distal occlusions (M3/M4 segments: 23.1%; 95% CI 14.9-33.1%) as well as for occlusions of the anterior cerebral artery (27.3%; 95% CI 10.7-50.2%). Overall specificity for thrombus detection was 89.8% (149/166) (95% CI: 84.1-93.9%), corresponding to 17 false-positive classifications. CONCLUSION: The developed DL classifier on DSA series confirmed on a large-scale dataset its very high sensitivity to proximal vessel occlusions with a sensitivity of 87.6%. Sensitivity for distal vessel occlusions was very low. Training the system for these lesion types will be the next step prior to clinical application.