The aim of this review is to synoptically illustrate and discuss how artificial intelligence approaches may help to compute single-patient predictions in stroke outcome research in the acute, subacute and chronic stage. Milestones have already been achieved in several health care domains, as big data and artificial intelligence have entered everyday life. Following modern rhythms, the next revolution might well be the strategic use of the steadily increasing amounts of patient-related data for generating models enabling individualized outcome predictions. New and continuously improving treatment options such as thrombolysis and thrombectomy have revolutionized acute stroke treatment in recent years. Stroke ranks among the leading causes for morbidity and mortality worldwide. This improved prediction of recovery could be beneficial to clinical care and might allow for a better choice of intervention. As hypothesized, accuracy in prediction significantly increased (R2 = 0.68) as compared to benchmark features (R2 = 0.38). We added these measures to benchmark structural features, and we used a ridge regression regularization to predict motor recovery at 3 months post-injury. This indirect estimation of the stroke impact on the whole brain connectome is more readily available than direct measures of structural connectivity obtained with magnetic resonance imaging. Here, we evaluated in a data set of 37 patients whether we could predict upper extremity motor recovery from brain connectivity measures obtained by using the patient’s lesion mask to introduce virtual lesions in 60 healthy streamline tractography connectomes.
However, assessing white matter connections in stroke patients is challenging and time-consuming.
Models that consider damage to the entire network instead of only local structural alterations lead to a more accurate prediction of patients’ recovery.
Recent work suggested that disabilities arise not only from focal structural changes but also from widespread alterations in inter-regional connectivity. It is known that lesion volume, initial motor impairment and cortico-spinal tract asymmetry significantly impact motor changes over time. Over time, some patients recover almost completely, while others barely recover at all. Following a stroke in regions of the brain responsible for motor activity, patients can lose their ability to control parts of their body.
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