Artificial intelligence in stroke imaging: Current and future perspectives

Yedavalli, Vivek; Tong, Elizabeth; Martin, Dann; Yeom, Kristen W.; Forkert, Nils D.

doi:10.1016/j.clinimag.2020.09.005

Cited by 52 publications

(27 citation statements)

References 75 publications

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“…It should be mentioned that this is not the first study to use predictive modeling approaches for an in silico evaluation [44]. Fiehler et al used a similar machine learning model based on CTP datasets to evaluate the effectiveness of a new recanalization device together with a specific intermediate catheter compared to intravenous tPA [15].…”

Section: Discussionmentioning

confidence: 99%

Treatment Efficacy Analysis in Acute Ischemic Stroke Patients Using In Silico Modeling Based on Machine Learning: A Proof-of-Principle

et al. 2021

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Interventional neuroradiology is characterized by engineering- and experience-driven device development with design improvements every few months. However, clinical validation of these new devices requires lengthy and expensive randomized controlled trials. This contribution proposes a machine learning-based in silico study design to evaluate new devices more quickly with a small sample size. Acute diffusion- and perfusion-weighted MRI, segmented one-week follow-up imaging, and clinical variables were available for 90 acute ischemic stroke patients. Three treatment option-specific random forest models were trained to predict the one-week follow-up lesion segmentation for (1) patients successfully recanalized using intra-arterial mechanical thrombectomy, (2) patients successfully recanalized using intravenous thrombolysis, and (3) non-recanalizing patients as an analogue for conservative treatment for each patient in the sample, independent of the true group membership. A repeated-measures analysis of the three predicted follow-up lesions for each patient revealed significantly larger lesions for the non-recanalizing group compared to the successful intravenous thrombolysis treatment group, which in turn showed significantly larger lesions compared to the successful mechanical thrombectomy treatment group (p < 0.001). A groupwise comparison of the true follow-up lesions for the three treatment options showed the same trend but did not reach statistical significance (p = 0.19). We conclude that the proposed machine learning-based in silico trial design leads to clinically feasible results and can support new efficacy studies by providing additional power and potential early intermediate results.

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Section: Discussionmentioning

confidence: 99%

Treatment Efficacy Analysis in Acute Ischemic Stroke Patients Using In Silico Modeling Based on Machine Learning: A Proof-of-Principle

et al. 2021

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“…Given the emerging evidence that earlier stroke treatment via MT improves outcomes, analysis of the impact of AI tools, such as RAPID-CTA, on stroke workflow metrics, such as CTA-to-groin puncture time, would have clinical utility. A number of investigators have investigated ways in which AI can be used in the setting of stroke detection, triage, evaluation, and risk stratification [30][31][32][33][34][35][36][37][38][39][40][41][42][43] with some techniques having prognostic value in predicting outcomes from therapy. 41,42 One of the major goals of AI is active radiology worklist reprioritization, which would automatically alert the radiologist to the presence of an imaging study requiring emergent interpretation.…”

Section: Background and Significancementioning

confidence: 99%

Positive predictive value and stroke workflow outcomes using automated vessel density (RAPID-CTA) in stroke patients: One year experience

Adhya

Eisenmenger

et al. 2021

Neuroradiol J

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Purpose Several new techniques have emerged for detecting anterior circulation large vessel occlusion by quantifying relative vessel density including RAPID-CTA, potentially allowing for faster triage and decreased time to mechanical thrombectomy. We present our one-year experience on positive predictive value of RAPID-CTA for the detection of large vessel occlusion in patients presenting with stroke symptoms and its effect on treatment time and clinical outcomes. Materials and methods Three hundred and ten patients presenting with stroke symptoms with relative vessel density <60% on RAPID-CTA were included (average age 70 years, 145 male, 165 female). Examinations were considered positive if there was evidence of large vessel occlusion or high grade stenosis. Computed tomography angiography to groin puncture time was calculated during one-year time intervals before and after RAPID-CTA installation. Ninety-day Modified Rankin Scale scores were obtained for patients in each cohort. Results Of the 310 patients, 270 had large vessel occlusion or high grade stenosis (87% positive predictive value), with 161 having large vessel occlusion. Using 45% relative vessel density threshold, 129/161 large vessel occlusion were detected (80% sensitivity) and 163/172 examinations were positive (95% positive predictive value). Computed tomography angiography to groin puncture time was significantly lower after deployment of RAPID-CTA (93 min vs 68 min, p<0.05). Average 90 day modified Rankin Scale score was lower in the RAPID-CTA group with a higher percentage of patients with functional independence, although the data was not statistically significant. Conclusion RAPID-CTA had high positive predictive value for large vessel occlusion with a 45% relative vessel density threshold, which could facilitate active worklist reprioritization. Time to treatment was significantly lower and clinical outcomes were improved after deployment of RAPID-CTA.

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“…Deep learning algorithms have been effective in image recognition and may be instrumental in the improvement of decision-making in the clinical setting [8,9]. For example, Titano et al demonstrated that a DL algorithm was capable of interpreting and triaging urgent neurological findings on head computed tomographies (CTs) 150 times faster than humans in the setting of intracranial hemorrhage (ICH), stroke, and hydrocephalus [10], and similar studies have shown encouraging results in stroke imaging and stroke care [11,12]. Physical robotic systems, such as the da Vinci robotic system (Intuitive Surgical Inc., Sunnyvale, CA, USA), have also become broadly accepted and proven to boost surgical precision, especially in abdominal surgery and prostatectomies.…”

Section: Introductionmentioning

confidence: 99%

Robotics and Artificial Intelligence in Endovascular Neurosurgery

Bravo¹,

Wali²,

Hirshman³

et al. 2022

Cureus

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The use of artificial intelligence (AI) and robotics in endovascular neurosurgery promises to transform neurovascular care. We present a review of the recently published neurosurgical literature on artificial intelligence and robotics in endovascular neurosurgery to provide insights into the current advances and applications of this technology.The PubMed database was searched for "neurosurgery" OR "endovascular" OR "interventional" AND "robotics" OR "artificial intelligence" between January 2016 and August 2021. A total of 1296 articles were identified, and after applying the inclusion and exclusion criteria, 38 manuscripts were selected for review and analysis. These manuscripts were divided into four categories: 1) robotics and AI for the diagnosis of cerebrovascular pathology, 2) robotics and AI for the treatment of cerebrovascular pathology, 3) robotics and AI for training in neuroendovascular procedures, and 4) robotics and AI for clinical outcome optimization.The 38 articles presented include 23 articles on AI-based diagnosis of cerebrovascular disease, 10 articles on AI-based treatment of cerebrovascular disease, two articles on AI-based training techniques for neuroendovascular procedures, and three articles reporting AI prediction models of clinical outcomes in vascular disorders of the brain. Innovation with robotics and AI focus on diagnostic efficiency, optimizing treatment and interventional procedures, improving physician procedural performance, and predicting clinical outcomes with the use of artificial intelligence and robotics. Experimental studies with robotic systems have demonstrated safety and efficacy in treating cerebrovascular disorders, and novel microcatheterization techniques may permit access to deeper brain regions. Other studies show that preprocedural simulations increase overall physician performance. Artificial intelligence also shows superiority over existing statistical tools in predicting clinical outcomes.The recent advances and current usage of robotics and AI in the endovascular neurosurgery field suggest that the collaboration between physicians and machines has a bright future for the improvement of patient care. The aim of this work is to equip the medical readership, in particular the neurosurgical specialty, with tools to better understand and apply findings from research on artificial intelligence and robotics in endovascular neurosurgery.

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Artificial intelligence in stroke imaging: Current and future perspectives

Cited by 52 publications

References 75 publications

Treatment Efficacy Analysis in Acute Ischemic Stroke Patients Using In Silico Modeling Based on Machine Learning: A Proof-of-Principle

Treatment Efficacy Analysis in Acute Ischemic Stroke Patients Using In Silico Modeling Based on Machine Learning: A Proof-of-Principle

Positive predictive value and stroke workflow outcomes using automated vessel density (RAPID-CTA) in stroke patients: One year experience

Robotics and Artificial Intelligence in Endovascular Neurosurgery

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