Tolga D. Dittrich, MD
Nielsen M, Waldmann M, Frölich AM, Flottmann F, Hristova E, Bendszus M, Seker F, Fiehler J, Sentker T, Werner R. Deep Learning-Based Automated Thrombolysis in Cerebral Infarction Scoring: A Timely Proof-of-Principle Study. Stroke. 2021.
The success of mechanical thrombectomy is commonly measured by the TICI (Thrombolysis in Cerebral Infarction) score. The score is determined by the visual assessment of the digital subtraction angiography (DSA) images during the intervention by the treating interventionalist. Despite modifications of the original scale (e.g., modified TICI [mTICI]) that have become established in the meantime, a relatively high inter- and intraobserver variability of TICI scores can be observed. This investigator dependency poses a challenge, particularly regarding clinical studies, as it may affect the comparability of results.
In the present study, based on occlusions of the middle cerebral artery in the M1 segment, Nielsen and colleagues sought to develop an automated and thus more objective TICI assessment using a deep learning (DL) approach. Agreement between DL and expert-based assessment (gold standard) was evaluated, and a comparison with corresponding published numbers on expert assessment variability was performed.