Artificial intelligence and stroke imaging

Though simple in its fundamental mechanism - a critical disruption of local blood supply - stroke is complicated by the intricate nature of the neural substrate, the neurovascular architecture, and their complex interactions in generating its clinical manifestations. This complexity is adequately described by high-resolution imaging with sensitivity not only to parenchymal macrostructure but also microstructure and functional tissue properties, in conjunction with detailed characterization of vascular topology and dynamics. Such descriptive richness mandates models of commensurate complexity only artificial intelligence could plausibly deliver, if we are to achieve the goal of individually precise, personalized care.

Advances in machine vision technology, especially deep learning, are delivering higher fidelity predictive, descriptive, and inferential tools, incorporating increasingly rich imaging information within ever more flexible models. Impact at the clinical front line remains modest, however, owing to the challenges of delivering models robust to the noisy, incomplete, biased, and comparatively small-scale data characteristic of real-world practice.

The potential benefit of introducing AI to stroke, in imaging and elsewhere, is now unquestionable, but the optimal approach - and the path to real-world application - remain unsettled. Deep generative models offer a compelling solution to current obstacles and are predicted powerfully to catalyse innovation in the field.