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Chandrabhatla, A.S.; Kuo, E.A.; Sokolowski, J.D.; Kellogg, R.T.; Park, M.; Mastorakos, P. Artificial Intelligence and Machine Learning in Stroke Care. Encyclopedia. Available online: https://encyclopedia.pub/entry/45680 (accessed on 15 November 2024).
Chandrabhatla AS, Kuo EA, Sokolowski JD, Kellogg RT, Park M, Mastorakos P. Artificial Intelligence and Machine Learning in Stroke Care. Encyclopedia. Available at: https://encyclopedia.pub/entry/45680. Accessed November 15, 2024.
Chandrabhatla, Anirudha S., Elyse A. Kuo, Jennifer D. Sokolowski, Ryan T. Kellogg, Min Park, Panagiotis Mastorakos. "Artificial Intelligence and Machine Learning in Stroke Care" Encyclopedia, https://encyclopedia.pub/entry/45680 (accessed November 15, 2024).
Chandrabhatla, A.S., Kuo, E.A., Sokolowski, J.D., Kellogg, R.T., Park, M., & Mastorakos, P. (2023, June 15). Artificial Intelligence and Machine Learning in Stroke Care. In Encyclopedia. https://encyclopedia.pub/entry/45680
Chandrabhatla, Anirudha S., et al. "Artificial Intelligence and Machine Learning in Stroke Care." Encyclopedia. Web. 15 June, 2023.
Artificial Intelligence and Machine Learning in Stroke Care
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This entry is adapted from the peer-reviewed paper 10.3390/jcm12113755

Stroke is an emergency for which delays in treatment can lead to significant loss of neurological function and be fatal. Technologies that increase the speed and accuracy of stroke diagnosis or assist in post-stroke rehabilitation can improve patient outcomes. No resource exists that comprehensively assesses artificial intelligence/machine learning (AI/ML)-enabled technologies indicated for the management of ischemic and hemorrhagic stroke.

machine learning artificial intelligence stroke

1. Introduction

Stroke is a neurological emergency and the fifth leading cause of death in the United States [1][2][3]. Established clinical interventions exist for many stroke subtypes such as large vessel occlusion (LVO) and intracranial hemorrhage (ICH). Prompt treatment is one of the more important factors in maximizing the preservation of neurological function. Notably, each minute of treatment delay results in significant neuronal death and the loss of 4.2 days of healthy life [4].
Tools to improve the speed and accuracy of stroke diagnosis and treatment could improve patient outcomes. Artificial intelligence/machine learning (AI/ML) will play a large role in developing such tools. AI/ML in healthcare is growing at 40% per year, and its adoption has the potential to cut USD 150 billion in healthcare costs by 2026 [5]. Recognizing the potential AI/ML has to improve healthcare, the United States Food and Drug Administration (FDA) has developed new protocols to assess the safety and efficacy of AI/ML-enabled health technologies [6]. AI/ML-enabled algorithms have been leveraged for various clinical applications such as detecting liver fibrosis [7], analyzing EKGs [8], monitoring Parkinson’s [9], diagnosing glaucoma [10], and classifying lung cancer [11]. The FDA has approved 22 AI/ML-enabled technologies for indications specifically related to stroke diagnosis and rehabilitation. Existing literature reviews in this area have broadly evaluated AI/ML algorithms that have largely been developed for research purposes [12][13][14][15][16]. No study to date has comprehensively evaluated the real-world clinical performance of clinically available, FDA-approved devices indicated for the diagnosis and management of stroke.

2. Large Vessel Occlusion (LVO) Identification in Acute Ischemic Stroke

An important application of AI/ML is the automated detection of large vessel occlusions. Viz ContaCT, commercially known as Viz LVO, was the first FDA-approved, AI/ML-enabled technology indicated for stroke and uses a convolutional neural network (CNN) as the underlying algorithm to detect LVOs from CT angiography (CTA). In data submitted to the FDA, Viz LVO displayed an area under the receiver operating curve (AUC) of 0.91 and reduced time from scan reading to specialist notification from 58 to 7 min [17], indicating improvement of clinical workflow efficiency. Others found similar increases in efficiency when using Viz LVO, reporting decreased transfer and stroke team notification times [18][19], as well as lengths of stay in the neurological ICU [19]. Assessment of Viz LVO’s performance has shown negative predictive values (NPV) ranging from 79 to 99% and sensitivities between 81 and 88%, with relatively fast run times (~3 min) and consistent performance across different vascular structures [20][21]. Notably, Viz LVO is an application within the broader Viz.ai platform, which includes tissue perfusion analysis on CTP and ICH identification on CT of the head.
RapidAI is a technology platform similar to Viz.ai. In addition to LVO identification on CTA (RAPID-CTA, RAPID-LVO), RapidAI includes software to analyze CT perfusion (RAPID-CTP) and MRI (RAPID-MRI) images for stroke triaging [22]. Though RAPID-LVO has a reported NPV range of 97–99% [23] and sensitivity ranging from 80–94%, there is a wide range of reported positive predictive values (PPV). Importantly, the PPV is 14% when identifying LVOs in the M2 segment of the MCA [23]. This is in contrast to Viz LVO’s reported lower bound PPV of 65%, which did not vary significantly across ICA, M1-MCA, and M2-MCA [21]. Variations in and relatively low PPVs highlight the use of these platforms as initial screening tools (given their high sensitivities and negative predictive values) that require subsequent expert confirmation to determine the presence of LVO. Use of both RAPID and Viz LVO has improved clinical workflows/outcomes (e.g., reducing CT-to-groin puncture times) with similar run times of ~3 min per scan [18][19][24].
Newer technologies for LVO identification include CINA-LVO [25] and HALO [26], which have shown promising performance in the few studies that have assessed their functionality. CINA has demonstrated relatively strong performance (PPV of 86–99%, NPV of 64–99%) across LVO anatomy [27][28]. The limited data for HALO reports an NPV of 91% and a PPV of 47%; however, performance varied based on the anatomical location of the LVO, with the lowest performance in M2 LVOs [29].

3. CT Head (CTH) Analysis (ASPECTS Score) in Acute Ischemic Stroke

Assessing the extent of irreversible ischemic damage to guide treatment decisions is equally important as identifying suspected LVOs. The Alberta Stroke Program Early CT Score (ASPECTS) is one widely used method for accomplishing this task. While diffusion-weighted MR imaging provides the most accurate information regarding acute infarction, CTH is more readily available in the acute setting. FDA-approved Rapid ASPECTS determines ASPECTS from CTs in patients with known MCA or ICA occlusions, but not for primary interpretation of CT images. In addition, the technology is only intended for use on GE Lightspeed VCT Scanners [30]. Overall, many have shown a strong correlation between ASPECTS determined manually by experts (e.g., neuroradiologists), which is currently the gold standard, and those calculated by Rapid ASPECTS [31][32][33]. Some even report superior performance by Rapid ASPECTS in analyzing imaging obtained soon after symptom onset [34][35]. Rapid ASPECTS’ individual impact on clinical efficiency and patient outcomes has not yet been studied. However, use of the broader RapidAI mobile app, which includes Rapid ASPECTS functionality, decreased door-to-groin puncture times and improved subsequent NIH stroke scale scores [36].

4. CT Perfusion (CTP) Analysis in Acute Ischemic Stroke

Another class of FDA-approved, AI/ML-enabled technologies for the management of stroke includes technologies that analyze CTP or MR perfusion images to assess the core and penumbra volumes and predict final infarct volumes. CTP can demonstrate ischemic tissue, which consists of non-salvageable tissue and at-risk tissue that could be rescued with successful reperfusion. CTP analysis provides specific parameters, including cerebral blood volume (CBV), cerebral blood flow (CBF), and mean transit time (MTT). Rapid-CTP is a comprehensively studied tool for CTP analysis within the broader RAPID platform and performs well in estimating final infarct volumes, with high accuracy and relatively strong correlations to the gold standard (e.g., human estimates of volumes) [37][38][39][40][41][42]. Vitrea CT Brain Perfusion was approved by the FDA in November 2018 to quantify cerebral blood flow and predict final infarct volumes [43]. Many groups have found Vitrea outperforms Rapid-CTP with respect to final infarct volume predictions [44][45][46], with the gold standard determined by human interpretation of DWI/FLAIR imaging. FastStroke/CT Perfusion 4D is a similar technology that not only predicts ischemic core volume but also assesses the quantity and quality of collateral perfusion [47][48]. Similar to Vitrea CT, FastStroke/CT Perfusion 4D performed comparably to Rapid-CTP (intraclass correlation coefficient of 0.95) [49], and its additional capability to assess collateral circulation improved accuracy in predicting good outcomes [50]. Icobrain CTP uses a CNN to estimate penumbra volumes and cerebral blood flow, both of which have strong correlations to expert assessments by radiologists [51][52]. Viz CTP is a similar software that performed well in predicting final infarct volume (r = ~0.6) [53]. While the above software solutions are well-characterized, there are no studies demonstrating improved time-to-reperfusion. Solutions such as Augmented Vascular Analysis [54] and Neuro.AI Algorithm [55] are yet to be independently assessed in the literature.

5. Intracranial Hemorrhage (ICH) Identification

Technologies indicated for the detection of ICH generally performed better than those indicated for LVO detection. BriefCase was the first FDA-approved, AI/ML-enabled technology for the identification of ICH from non-contrast head CT [56]. BriefCase’s CNN-based algorithm [57] has shown strong performance by reducing outpatient scan interpretation delays by 90% (604 min reduction) and inpatient delays by 10% (38 min reduction) [58]. Cases flagged by BriefCase as suspicious for ICH had an average turnaround time of 73 min, versus 132 min for non-flagged cases [59]. Recent studies assessing BriefCase have reported NPVs of 96–99% and PPVs of 72–96% [58][60][61]. A main driver of false negatives was ICH anatomy (e.g., under the calvaria), while false positives were driven by tumors and calcifications [62][63].
CINA-ICH has similar reported performance in ICH detection compared to BriefCase. NPVs ranged from 92–99%, PPVs from 80–97%, and the algorithm had a sensitivity of 72% when identifying relatively small-volume bleeds (volume less than 5 mL) [27][64]. CINA has additional subclassification functionality (e.g., differentiating between subarachnoid and intraventricular hemorrhage) with a sensitivity of at least 90% [27]. CuraRad-ICH, on the other hand, had subclassification sensitivities between 61 and 99% [65][66], though the software was studied on a larger sample of scans and has specificities roughly comparable to those of CINA.
Rapid-ICH [67], with PPV, NPV, accuracy, sensitivity, and specificity of at least 95% [68], and HealthICH [69], with an AUC of 0.96 [70], are two other technologies indicated for ICH detection. Some FDA-approved technologies for ICH detection have yet to be studied independently in the literature. These include Accipiolx [71], DeepCT [72], NinesAI [73], qER [74], and Viz ICH [75].

6. Rehabilitation

Tools for post-stroke rehabilitation require further development, especially given the poor natural recovery that is often seen with stroke [56]. There is a need for technologies that can extend the therapeutic window for patients and/or enable neurological recovery.
An Israeli-based company, BrainQ, is developing a non-invasive brain-computer interface (BCI) device that leverages extremely low frequency and low intensity electromagnetic fields (ELF-EMF) to promote post-stroke recovery [76][77]. After a stroke, patients often have abnormal neural oscillatory patterns, and exposure to tuned EMFs can influence these oscillations [78], thereby promoting periods of neuroplasticity [79][80]. BrainQ’s technology uses ML to extract motor-related spectral features from electrophysiology measurements (EEG, MEG/EMG) [81] and then translates these into a specific ELF-EMF treatment for patients [82].
BrainQ received FDA breakthrough status in February 2021 based on results from a pilot trial of 25 patients with a history of sub-acute ischemic stroke. Patients who received 40 min of ELF-EMF treatment 5 days a week for 8 weeks had superior recovery compared to the sham group as assessed by multiple metrics (e.g., NIH stroke score) and did not report any adverse events [83]. BrainQ has planned a double-blind national clinical trial across up to 20 inpatient rehabilitation facilities in the United States [84]. A previous BrainQ clinical trial was terminated due to the COVID-19 pandemic [85].
IpsiHand Upper Extremity Rehabilitation System (IpsiHand), granted breakthrough status by the FDA in April 2021, is the first FDA-approved device to use BCI technology to facilitate motor rehabilitation in patients who are more than 6 months post-stroke. The device uses an EEG electrode headset to translate neural activity of movement intent from the uninjured brain hemisphere into physical movements of a robotic exoskeleton worn around the impaired hand, wrist, and forearm [86]. A study of ten chronic hemiparetic stroke survivors with upper-limb impairment showed significant improvement in arm functionality after 12 weeks of IpsiHand therapy, with only minor side effects (e.g., skin redness) [87]. A randomized clinical trial is needed to assess whether use of IpsiHand alone proves more beneficial for upper extremity function versus traditional physical therapy. IpsiHand has the potential to enhance functional recovery with convenient, in-home post-stroke rehabilitation.

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Subjects: Neuroimaging; Medical Informatics; Neurosciences
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Anirudha S. Chandrabhatla
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Elyse A. Kuo
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Jennifer D. Sokolowski
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Ryan T. Kellogg
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Min Park
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Panagiotis Mastorakos
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Update Date: 16 Jun 2023
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