The Aneurysm Occlusion Assistant, an AI platform for real time surgical guidance of intracranial aneurysms

Kyle A. Williams; Alexander R. Podgorsak; Mohammad Mahdi Shiraz Bhurwani; Ryan A. Rava; Kelsey N. Sommer; Ciprian N. Ionita

doi:10.1117/12.2581003

15 February 2021 The Aneurysm Occlusion Assistant, an AI platform for real time surgical guidance of intracranial aneurysms

Kyle A. Williams, Alexander R. Podgorsak, Mohammad Mahdi Shiraz Bhurwani, Ryan A. Rava, Kelsey N. Sommer, Ciprian N. Ionita

Author Affiliations +

Proceedings Volume 11601, Medical Imaging 2021: Imaging Informatics for Healthcare, Research, and Applications; 116010V (2021) https://doi.org/10.1117/12.2581003
Event: SPIE Medical Imaging, 2021, Online Only

Abstract

Purpose: In recent years, endovascular treatment has become the dominant approach to treat intracranial aneurysms (IAs). Despite tremendous improvement in surgical devices and techniques, 10-30% of these surgeries require retreatment. Previously, we developed a method which combines quantitative angiography with data-driven modeling to predict aneurysm occlusion within a fraction of a second. This is the first report on a semi-autonomous system, which can predict the surgical outcome of an IA immediately following device placement, allowing for therapy adjustment. Additionally, we previously reported various algorithms which can segment IAs, extract hemodynamic parameters via angiographic parametric imaging, and perform occlusion predictions. Methods: We integrated these features into an Aneurysm Occlusion Assistant (AnOA) utilizing the Kivy library’s graphical instructions and unique language properties for interface development, while the machine learning algorithms were entirely developed within Keras, Tensorflow and skLearn. The interface requires pre- and post-device placement angiographic data. The next steps for aneurysm segmentation, angiographic analysis and prediction have been integrated allowing either autonomous or interactive use. Results: The interface allows for segmentation of IAs and cranial vasculature with a dice index of ~0.78 and prediction of aneurysm occlusion at six months with an accuracy 0.84, in 6.88 seconds. Conclusion: This is the first report on the AnOA to guide endovascular treatment of IAs. While this initial report is on a stand-alone platform, the software can be integrated in the angiographic suite allowing direct communication with the angiographic system for a completely autonomous surgical guidance solution.

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Kyle A. Williams, Alexander R. Podgorsak, Mohammad Mahdi Shiraz Bhurwani, Ryan A. Rava, Kelsey N. Sommer, and Ciprian N. Ionita "The Aneurysm Occlusion Assistant, an AI platform for real time surgical guidance of intracranial aneurysms", Proc. SPIE 11601, Medical Imaging 2021: Imaging Informatics for Healthcare, Research, and Applications, 116010V (15 February 2021); https://doi.org/10.1117/12.2581003

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