Ischemic stroke, defined as the sudden onset of a focal neurological deficit resulting from hypo-perfusion due to an occluded cerebral artery, is a leading cause of mortality and long-term disability worldwide. The standard treatment for acute ischemic strokes (AIS) is an endovascular thrombectomy (EVT), a minimally invasive procedure for locally removing the occlusion. The effectiveness of an EVT is assessed by estimating the reperfusion status on digital subtraction angiography (DSA) images using a six-category visual grading system termed the extended Thrombolysis In Cerebral Infarction (eTICI) scale. However, eTICI scoring suffers from inter-observer variability, complicating EVT outcome comparisons. To address this, ‘autoTICI’, a fully automated and objective deep-learning-based TICI scoring method, was developed by our research group. This thesis aimed to improve and evaluate autoTICI in clinical practice.

Chapter 1 provides a general introduction, highlighting the clinical background and relevance of automated TICI scoring.

Chapter 2 presents a pilot study that explored the technical feasibility and clinical implementability of autoTICI within the interventional radiology workflow. The pilot study showed that the performance of autoTICI is currently insufficient for implementation in clinical practice. Nevertheless, with an average System Usability Score (SUS) of 81.3, autoTICI still demonstrated excellent usability within the clinical workflow, indicating its potential for future clinical use. Besides, its potential as a decision-support tool was recognized by multiple interventional radiologists, particularly in complex or high-stress scenarios.

Chapter 3 presents a core-laboratory study assessing the inter-observer agreement and efficiency of eTICI scoring with and without autoTICI feedback, showing a substantial agreement both with and without autoTICI, with weighted kappa values of 0.65 and 0.67, respectively. Additionally, autoTICI had no significant effect on scoring efficiency, suggesting that the supplementary information did not increase the time required for eTICI scoring.

Chapter 4 details the development of an automated deep-learning approach for direct segmentation of vascular territories on cerebral DSA, aimed at improving the robustness of autoTICI by replacing the atlas registration step in its pipeline. A nnUNet model was trained, validated, and benchmarked against the atlas registration method. The proposed model demonstrated excellent performance on a held-out test set, achieving a 100% success rate compared to 52.5% for atlas registration. Additionally, it significantly outperformed the atlas registration method in both segmentation accuracy and computational efficiency.

Chapter 5 provides a general discussion and conclusion, synthesizing the results from the previous chapters and offering recommendations for future research on autoTICI.

In this master’s thesis, autoTICI was successfully integrated into the clinical workflow of the interventional radiology. Through the pilot study and inter-observer analysis, we were able to identify autoTICI’s intended use and added value within the clinical workflow, along with key issues, challenges, pitfalls, and future opportunities. By addressing one of these identified issues — atlas registration — through automated vascular territory segmentation, the performance of autoTICI was significantly improved. Although autoTICI is not yet ready for clinical implementation, with further optimization needed, the contributions of this master thesis have brought it significantly closer to that goal.