ArticleFrontiers in surgery2026
Quaternion-based recurrent neural networks and machine learning for 3D modeling of superficial femoral artery dynamics: a theoretical framework for enhanced treatment outcomes.
Article in Frontiers in surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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3 authors.
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Abstract
The superficial femoral artery (SFA) is highly susceptible to atherosclerotic disease, with restenosis rates which are exceeding 30%-50% following endovascular intervention. Current diagnostic approaches including two-dimensional angiography and duplex ultrasound are failing to capture the complex three-dimensional (3D) spatiotemporal dynamics of vessel remodeling during physiological motion. This theoretical framework is integrating quaternion-based recurrent neural networks (QRNNs) with machine learning pipelines to model dynamic 3D changes in SFA geometry from serial imaging data or continuous recording of SFA quaternion dynamics during activities by wearable
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