Artificial Intelligence and Machine Learning in the Diagnosis and Management of Stroke: A Narrative Review of United States Food and Drug Administration-Approved Technologies
Abstract
:1. Introduction
2. Methods
2.1. Technology Search
2.2. Literature Search
3. Review of Literature
4. Large Vessel Occlusion (LVO) Identification in Acute Ischemic Stroke
5. CT Head (CTH) Analysis (ASPECTS Score) in Acute Ischemic Stroke
6. CT Perfusion (CTP) Analysis in Acute Ischemic Stroke
7. Intracranial Hemorrhage (ICH) Identification
Device | Author, Year | Level of Evidence | Dataset Characteristics | Sample Size (Scans) | AUC | PPV | NPV | Accuracy | Sensitivity | Specificity | Other Metrics/Comments |
---|---|---|---|---|---|---|---|---|---|---|---|
BriefCase | Ojeda et al., 2019 [63] | Retrospective | Proprietary, Multicenter | 7112 | - | 96% | 98% | 98% | 95% | 99% | BriefCase uses a CNN to analyze non-contrast CTs to detect and triage ICH. |
Wismüller et al., 2020 [65] | Randomized Clinical Trial | Proprietary, Single Center | 620 | - | - | - | 96% | 95% | 97% | Turn-around times for cases flagged by BriefCase (73 min) were significantly lower than those for non-flagged cases (132 min). | |
Ginat et al., 2020 [66] | Prospective | Proprietary, Single Center | 2011 | - | 74% | 98% | 93% | 89% | 94% | Accuracy was significantly higher for emergency (96.5%) vs. inpatient (89.4%) cases. False positives had various causes, including: (1) artifacts, (2) thick dura, (3) intra-arterial clot, (4) calcifications, and (5) tumors. | |
Rao et al., 2021 [69] | Retrospective | Proprietary, Single Center | 5585 | - | - | - | - | - | - | When applied to scans that radiologists reported as negative for ICH, BriefCase found 28 scans with ICH, of which 16 truly did. Subset analysis showed a false positive rate of 32%. | |
Ginat et al., 2021 [64] | Retrospective | Proprietary, Single Center | 8723 | - | 86% | 96% | - | 88% | 96% | Scan view delay for cases flagged by the software decreased by 37 min for inpatients and 604 min for outpatients. In the ER, time reduction was most prominent during the 9 p.m. to 3 a.m. and 10 a.m. to 12 p.m. periods, and especially during the weekend. | |
Voter et al., 2021 [67] | Retrospective | Proprietary, Single Center | 3605 | - | 81% | 99% | 96% * | 92% | 98% | Neuroradiologists and the software agreed 97% of the time. Prior neurosurgery decreased model performance. | |
Kundisch et al., 2021 [68] | Retrospective | Proprietary, Multicenter | 4946 | - | 72% * | 99% * | 97% * | 88% * | 98% * | Software detected 29 additional ICHs (0.59%) in the cohort. False negative rate was 12.4% compared to the radiologist rate of 10.9%. Anatomical variations (e.g., calcifications) were difficult for the algorithm to analyze. | |
CINA | McLouth et al., 2021 [27] | Retrospective | Proprietary, Multicenter | 814 | - | 80–97% | 92–99% | 96% | 91% | 97% | True positive rates (sensitivity) for ICH subclassification were >90%. ICH < 5 mL had a sensitivity of 72%. |
Rava et al., 2021 [70] | Retrospective | Proprietary, Single Center | 302 | - | 85% | 98% | 94% | 93% | 93% | 95% of ICH volumes were correctly triaged. 88% of non-ICH cases were correctly classified as ICH negative. | |
CuraRad-ICH | Ye et al., 2019 [71] | Retrospective | Proprietary, Multicenter | 2836 | 0.8–1.0 | - | - | 75–99% | 61–99% | 82–99% | Algorithm was evaluated for binary classification (ICH vs. no ICH) and multi-type classification (CPH, SAH, EDH, SDH, IVH). |
Guo et al., 2020 [72] | Retrospective | Proprietary, Multicenter | 1176 | 0.85–0.99 | - | - | 90–98% | 78–97% | 92–100% | Algorithm was evaluated for binary classification (ICH vs. no ICH) and multi-type classification (CPH, SAH, EDH, SDH, IVH). | |
Rapid ICH | Heit et al., 2021 [74] | Retrospective | Proprietary, Multicenter | 308 | - | 96% | 95% | 95% * | 96% | 95% | |
HealthICH | Bar et al., 2018 [76] | Retrospective | Proprietary, Multicenter | 1426 | 0.96 | - | - | - | - | - | |
Accipiolx | |||||||||||
DeepCT | |||||||||||
NinesAI | |||||||||||
QER | |||||||||||
Viz ICH |
8. Rehabilitation
9. Discussion
10. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Device | Company Name | Headquarters | FDA Approval Number | Type of Approval | Indication | Date of Approval |
---|---|---|---|---|---|---|
ContaCT (Viz LVO) | Viz.ai | San Francisco, CA, USA | DEN170073 | 513(f)(2)(De Novo) | Analyze acute CTA to identify LVO | February 2018 |
Viz CTP | Viz.ai | San Francisco, CA, USA | K180161 | 510(k) | Analyze brain tissue perfusion parameters on CTP | April 2018 |
BriefCase | Aidoc Medical | Israel | K180647 | 510(k) | ICH detection in non-contrast CT | August 2018 |
Accipiolx | MaxQ Al | Israel | K182177 | 510(k) | ICH detection in non-contrast CT | October 2018 |
Vitrea CT Brain Perfusion | Vital Images | Minnetonka, MN, USA | K181247 | 510(k) | Visualize apparent blood perfusion in brain tissue affected by acute stroke on CT | November 2018 |
RAPID * | iSchemaView | Golden, CO, USA | K182130 | 510(k) | Identification of CTP, CTA, and MRI images consistent with stroke | December 2018 |
HealthICH | Zebra Medical Vision (now Nanox AI) | Israel | K190424 | 510(k) | Aid clinical assessment of non-contrast head CT with features suggestive of ICH | June 2019 |
DeepCT | Deep01 Limited | Taiwan | K182875 | 510(k) | ICH detection in non-contrast CT | July 2019 |
Icobrain-CTP | Icometrix | Belgium | K192962 | 510(k) | Image analysis of brain CT perfusion scans | February 2020 |
Rapid ICH | iSchemaView | Golden, CO, USA | K193087 | 510(k) | ICH detection in non-contrast CT | March 2020 |
CuraRad-ICH | CuraCloud | Seattle, WA, USA | K192167 | 510(k) | ICH detection in non-contrast CT | April 2020 |
NinesAI | Nines | Palo Alto, VA, USA | K193351 | 510(k) | ICH detection in non-contrast CT | April 2020 |
CINA ** | AVICENNA.AI | France | K200855 | 510(k) | ICH in head CT and LVO in head CT angiography | June 2020 |
Rapid ASPECTS | iSchemaView | Golden, CO, USA | K200760 | 510(k) | ASPECT scoring in patients with known MCA or ICA occlusions | June 2020 |
QER | Qure.Ai Technologies | India | K200921 | 510(k) | ICH detection in non-contrast CT | June 2020 |
Augmented Vascular Analysis | See-Mode Technologies | Singapore | K201369 | 510(k) | Predict stroke risk from vascular ultrasound | September 2020 |
HALO | NICo-Lab B.V. | Amsterdam | K200873 | 510(k) | LVO identification in anterior circulation (ICA, M1 or M2) from CT angiogram | November 2020 |
FastStroke, CT Perfusion 4D | GE Medical Systems SCS | France | K193289 | 510(k) | CT Perfusion 4D: Perfusion abnormalities from contrast CTFastStroke: Stroke detection from CT (e.g., non-constast, angiogram) | November 2020 |
Neuro.Al Algorithm | TeraRecon | Durham, NC, USA | K200750 | 510(k) | Detect changes in brain perfusion from CT or MRI | November 2020 |
BrainQ | BrainQ | Israel | - | Breakthrough status | Reduce disability post-stroke | February 2021 |
Viz ICH | Viz.ai | San Francisco, CA, USA | K210209 | 510(k) | Analyze acute non-contrast CT of brain, notify specialist of suspected ICH | March 2021 |
IpsiHand Upper Extremity Rehabilitation System | Neurolutions | Santa Cruz, CA, USA | - | Breakthrough status | Post-stroke rehabilitation | April 2021 |
Device | Author, Year | Level of Evidence | Dataset Characteristics | Sample Size (Scans) | AUC | PPV | NPV | Accuracy | Sensitivity | Specificity | Other Metrics/Comments |
---|---|---|---|---|---|---|---|---|---|---|---|
Viz LVO | Hassan et al., 2020 [22] | Prospective | Proprietary, Single Center | 43 | - | - | - | - | - | - | Viz LVO reduced median CTA time at primary center to door-in at comprehensive center by an average of 22.5 min. Neuro-ICU stays were reduced by 2.5 days. |
Yahav-Dovrat et al., 2021 [23] | Prospective | Proprietary, Single Center | 1167 | - | 65% | 99% | 94% | 81% | 96% | - | |
Morey et al., 2021 [21] | Retrospective | Proprietary, Single Center | 55 | - | - | - | - | - | - | Viz LVO reduced median door-to-neurovascular team notification time from 40 to 25 min. | |
Rodrigues et al., 2021 [24] | Retrospective | Proprietary, Single Center | 610 | 0.88 | 93% | 79% | 88% | 88% | 89% | Algorithm had similar performance across ICA-T, MCA-M1, and MCA-M2 occlusions. Mean run time was ~3 min. | |
RAPID (LVO) | Adhya et al., 2021 [25] | Retrospective | Proprietary, Multicenter | 310 | - | 23–75% | - | - | 80% | - | CT to groin puncture time was lower after implementation of RAPID (93 min vs. 68 min). |
Amukotuwa et al., 2019 [26] | Retrospective | Proprietary, Single Center | 477 | 0.77–0.86 | 14–58% | 97–99% | - | 86–94% | 68–81% | Median scan analysis time was roughly 160 s. | |
CINA | McLouth et al., 2021 [27] | Retrospective | Proprietary, Multicenter | 378 | - | 86–98% | 98–99% | 98% | 98% | 98% | In the detection of LVO subtypes (i.e., at distal internal carotid artery, middle cerebral artery M1 segment, proximal middle cerebral artery M2 segment, distal middle cerebral artery M2 segment), the CINA algorithm demonstrated an accuracy of 97%, sensitivity of 94.3%, and specificity of 97.4%. |
Rava et al., 2021 [28] | Retrospective | Proprietary, Single Center | 303 | - | 99% | 64% | 81% | 73% | 98% | Scan processing time was ~70 s. The algorithm identified ICA, M1 MCA, and M2 MCA occlusions. | |
HALO | Luijten et al., 2021 [29] | Prospective | MR CLEAN registry & PRESTO study, Multicenter | 1756 | 0.75 | 47% | 91% | 76% * | 72-89% | 78% | Performance varied considerably based on location of occlusion. |
Rapid ASPECTS | Lasocha et al., 2020 [30] | Retrospective | Proprietary, Single Center | 100 | - | - | - | - | - | - | Exact ASPECT score agreement between RAPID and manual methods was poor, but crossing of threshold for reperfusion therapy was characterized by an 80% match. |
Hoelter et al., 2020 [31] | Retrospective | Proprietary, Single Center | 131 | 0.73 | - | - | - | - | - | Correlation between ASPECT scores of experts and RAPID was high (r = 0.78) | |
Maegerlein et al., 2019 [32] | Retrospective | Proprietary, Single Center | 100 | - | - | - | - | - | - | In acute stroke of the middle cerebral artery, RAPID-calculated ASPECT score had better agreement with predefined consensus scores than neuroradiologists overall (κ = 0.9 vs. κ = 0.57, respectively), and particularly in the time interval of 1 to 4 h between symptom onset and imaging. | |
Al-Kawaz et al., 2021 [33] | Retrospective | Proprietary, Single Center | 64 | - | - | - | - | - | - | Use of the RAPID mobile app (which includes Rapid ASPECTS functionality) decreased door to groin puncture times by 33 min compared to patients treated pre-app and improved scores on National Institutes of Health Stroke Scale 24 h after procedure (12.1 vs. 8.0) and at discharge (11.8 vs. 7.8) | |
Albers et al., 2019 [34] | Retrospective | GAMES-RP trial, Multicenter | 65 | - | - | - | 73% | - | - | RAPID ASPECTS was more accurate than clinicians (73% vs. 56%) in identifying early ischemia on DWI. | |
Mansour et al., 2020 [35] | Retrospective | Proprietary, Single Center | 122 | - | - | - | - | - | - | Automated ASPECT score by the algorithm performed equally to scoring by neuroradiologists (κ = 0.8). |
Device | Author, Year | Level of Evidence | Dataset Characteristics | Sample Size (Scans) | AUC | PPV | NPV | Accuracy | Sensitivity | Specificity | Other Metrics/Comments |
---|---|---|---|---|---|---|---|---|---|---|---|
Vitrea CT Brain Perfusion | Rava et al., 2020 [47] | Retrospective | Proprietary, Single Center | 105 | - | - | - | - | - | - | In estimating infarct volume, Spearman correlation coefficient between Vitrea and DWI/FLAIR ranged from 0.71 to 0.77. Vitrea outperformed RAPID. |
Rava et al., 2020 [48] | Retrospective | Proprietary, Single Center | 107 | - | - | - | - | - | - | In estimating infarct volume, Spearman correlation coefficient between different algorithms within Vitrea (i.e., Bayesian and Singular Value Decomposition) and FLAIR MRI was 0.98 vs. 0.76-0.87 between RAPID and FLAIR MRI. | |
Rava et al., 2021 [49] | Retrospective | Proprietary, Single Center | 63 | - | 63–72% | - | - | - | - | - | |
Rava et al., 2021 [59] | Retrospective | Proprietary, Single Center | 108 | - | - | - | 96–98% | 60–62% | 98–99% | Vitrea overestimated infarct volume, but provided the most accurate penumbra assessment for patients treated conservatively. | |
Ichikawa et al., 2021 [60] | Retrospective | Proprietary, Single Center | 36 | - | - | - | - | - | - | Vitrea’s Bayesian algorithm had better delineation of abnormal perfusion areas and estimation of infarct volume compared to the SVD implementation. | |
RAPID (CTP) | Hokkinen et al., 2021 [40] | Retrospective | Proprietary, Single Center | 89 | - | - | - | - | - | - | In patients presenting 6 to 24 hours from onset of symptoms, CTP-RAPID’s estimate of infarct volume correlated with follow-up imaging (r = 0.82). Correlation decreased (r = 0.58) in patients presenting 0 to 6 hours after symptom onset. |
Wouters et al., 2021 [42] | Randomized Controlled Trial | MR CLEAN trial & CRISP study, Multicenter | 127 | - | - | - | - | - | - | A new deep learning CNN model outperformed RAPID in predicting final infarct volume. | |
Potreck et al., 2021 [43] | Retrospective | Simulation | 53 | - | - | - | - | - | - | Head motion during CT perfusion acquisition can impact infarct core estimates. | |
Bouslama et al., 2021 [44] | Retrospective | Proprietary, Single Center | 479 | - | - | - | - | - | - | RAPID had moderate correlation with final infarct volumes (r = 0.42–0.44). | |
Siegler et al., 2020 [61] | Retrospective | Multi-site registry, Multicenter | 410 | 0.69 | - | - | - | 62% | 72% | Stroke mimics can show abnormalities on RAPID CT analysis. | |
Kim et al., 2019 [45] | Prospective | Proprietary, Single Center | 296 | - | - | - | 89–100% | - | - | Interclass correlation between RAPID and manual measurements of infarct volume were 0.98, with RAPID underestimating volumes by ~2 mL on average. | |
FastStroke/CT Perfusion 4D | Verdolotti et al., 2020 [51] | Retrospective | Proprietary, Single Center | 86 | - | - | - | - | - | - | Algorithm is comparable in efficacy to the status quo in evaluating collateral circulation, but has simpler workflows and faster turnaround times, making use easier for radiologists. |
Ospel et al., 2021 [53] | Prospective | PRove-IT cohort study, Multicenter | 285 | 0.63–0.76 | - | - | - | - | - | Time-variant multiphase CTA (mCTA) maps produced by the software improved prediction of good outcomes and performed comparably to conventional mCTA in predicting infarct volume. | |
Liu et al., 2021 [52] | Retrospective | Proprietary, Single Center | 82 | - | - | - | - | - | - | CT Perfusion 4D had ICC of 0.95 compared to RAPID in predicting core volumes. The algorithm also performed well for volumes ≤ 70 mL | |
Icobrain-CTP | de la Rosa et al., 2021 [54] | Retrospective | Public ISLES18 stroke database | 156 | - | - | - | - | - | - | Icobrain uses a CNN that does not need user input in the form of thresholding to assess perfusion. Estimations of penumbra volume using CBF, CBV, and MTT had strong correlation with assessments by radiologists. |
de la Rosa et al., 2021 [55] | Retrospective | Public ISLES18 stroke database | 156 | 0.86 | - | - | - | - | - | Icobrain performed comparably to expert estimates of cerebral blood flow based on 4D CTP scans. | |
Viz CTP | Pisani et al., 2021 [56] | Prospective | Proprietary database otherwise unspecified | 242 | - | - | - | - | - | - | Viz CTP performed well in predicting final infarct volume (r = 0.601). |
Augmented Vascular Analysis | |||||||||||
Neuro.Al Algorithm |
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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 the Diagnosis and Management of Stroke: A Narrative Review of United States Food and Drug Administration-Approved Technologies. J. Clin. Med. 2023, 12, 3755. https://doi.org/10.3390/jcm12113755
Chandrabhatla AS, Kuo EA, Sokolowski JD, Kellogg RT, Park M, Mastorakos P. Artificial Intelligence and Machine Learning in the Diagnosis and Management of Stroke: A Narrative Review of United States Food and Drug Administration-Approved Technologies. Journal of Clinical Medicine. 2023; 12(11):3755. https://doi.org/10.3390/jcm12113755
Chicago/Turabian StyleChandrabhatla, Anirudha S., Elyse A. Kuo, Jennifer D. Sokolowski, Ryan T. Kellogg, Min Park, and Panagiotis Mastorakos. 2023. "Artificial Intelligence and Machine Learning in the Diagnosis and Management of Stroke: A Narrative Review of United States Food and Drug Administration-Approved Technologies" Journal of Clinical Medicine 12, no. 11: 3755. https://doi.org/10.3390/jcm12113755
APA StyleChandrabhatla, A. S., Kuo, E. A., Sokolowski, J. D., Kellogg, R. T., Park, M., & Mastorakos, P. (2023). Artificial Intelligence and Machine Learning in the Diagnosis and Management of Stroke: A Narrative Review of United States Food and Drug Administration-Approved Technologies. Journal of Clinical Medicine, 12(11), 3755. https://doi.org/10.3390/jcm12113755