Artificial intelligence in stroke care: Deep learning or superficial insight?

摘要

The concept of artificial intelligence (AI) has recently permeated almost every sector of daily life, including the rapidly evolving technologies and datasets of health care delivery. Each mode or type of technology, such as medical imaging, iteratively evolves each year and the resulting inter-related or multimodal applications multiply exponentially [[1]Hinman J.D. Rost N.S. Leung T.W. et al.Principles of precision medicine in stroke.J Neurol Neurosurg Psychiatry. 2017; 88: 54-61Crossref PubMed Scopus (34) Google Scholar]. As health care technology, such as the electronic health record or diagnostic and therapeutic approaches expand, there is an ongoing demand for the continual process of leveraging, integrating and optimizing these synergistic advances. This modernization, or progressive refinement in optimizing the efficiency of existing technologies is devoted to eliminating efficiencies and maximally utilizing the information embedded in every ephemeral event in routine clinical care. Such modernization is expected and not innovative, per se. In clinical medicine, as in other spheres of daily life, digital data is now amassing in distributed electronic health records and potentially voluminous clinical, imaging, laboratory and other datasets [1Hinman J.D. Rost N.S. Leung T.W. et al.Principles of precision medicine in stroke.J Neurol Neurosurg Psychiatry. 2017; 88: 54-61Crossref PubMed Scopus (34) Google Scholar, 2Liebeskind D.S. Albers G.W. Crawford K. et al.Imaging in strokenet: realizing the potential of big data.Stroke. 2015; 46: 2000-2006Crossref PubMed Scopus (20) Google Scholar, 3Liebeskind D.S. Malhotra K. Hinman J.D. Imaging as the nidus of precision cerebrovascular health: A million brains initiative.JAMA Neurol. 2017; 74: 257-258Crossref PubMed Scopus (10) Google Scholar]. The recent expansion of imaging data in stroke is an ideal example, where data are universally acquired for all patients encountered, digitally preserved and thereby amenable to largescale computer algorithms for decades to come from around the world. Importantly, such informatics may yield insight far beyond the pace and extent of what we can accomplish as physicians in routine stroke care where every minute counts in patient outcomes. In this issue of EBioMedicine, Tang et al. provide an intriguing application of machine learning to MRI data in acute ischemic stroke to delineate the tissue fate of penumbral regions over time [[4]Tang T.Y. Jiao Y. Cui Y. et al.Development and validation of a penumbra-based predictive model for acute ischemic stroke patients.EBioMedicine. 2018; Summary Full Text Full Text PDF Scopus (7) Google Scholar]. Importantly, they demonstrate that the typical time-based administration of intravenous thrombolysis may be successfully applied irrespective of time from symptom onset when advanced imaging enables AI via machine learning. Tang et al. pooled the MRI data across seven centers, acquired within 9 h of symptom onset and focused on the identification of penumbral tissue as the target of intervention [[4]Tang T.Y. Jiao Y. Cui Y. et al.Development and validation of a penumbra-based predictive model for acute ischemic stroke patients.EBioMedicine. 2018; Summary Full Text Full Text PDF Scopus (7) Google Scholar]. Measuring the size of the penumbra is a key goal of stroke imaging as treatments target such opportunities to offset potentially irreversible ischemic brain injury. The authors defied rigid time windows for intravenous thrombolysis and leveraged more sophisticated strategies to identify the potentially reversible penumbral zones of ischemia that are threatened or at-risk of ischemic infarction. They studied a cohort of 155 individuals, including 84 in a training dataset. These populations are remarkably small in relationship to the expected task, yet they advantaged the power of independent voxel-based techniques to predict tissue fate in the brain after an ischemic stroke. Mismatch between hypoperfusion and