The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
Artificial intelligence to diagnose ischemic stroke and identify large vessel occlusions: a systematic review
Abstract
Background and purpose: Acute stroke caused by large vessel occlusions (LVOs) requires emergent detection and treatment by endovascular thrombectomy. However, radiologic LVO detection and treatment is subject to variable delays and human expertise, resulting in morbidity. Imaging software using artificial intelligence (AI) and machine learning (ML), a branch of AI, may improve rapid frontline detection of LVO strokes. This report is a systematic review of AI in acute LVO stroke identification and triage, and characterizes LVO detection software. Methods: A systematic review of acute stroke diagnostic-focused AI studies from January 2014 to February 2019 in PubMed, Medline, and Embase using terms: ‘artificial intelligence’ or ‘machine learning or deep learning’ and ‘ischemic stroke’ or ‘large vessel occlusion’ was performed. Results: Variations of AI, including ML methods of random forest learning (RFL) and convolutional neural networks (CNNs), are used to detect LVO strokes. Twenty studies were identified that use ML. Alberta Stroke Program Early CT Score (ASPECTS) commonly used RFL, while LVO detection typically used CNNs. Image feature detection had greater sensitivity with CNN than with RFL, 85% versus 68%. However, AI algorithm performance metrics use different standards, precluding ideal objective comparison. Four current software platforms incorporate ML: Brainomix (greatest validation of AI for ASPECTS, uses CNNs to automatically detect LVOs), General Electric, iSchemaView (largest number of perfusion study validations for thrombectomy), and Viz.ai (uses CNNs to automatically detect LVOs, then automatically activates emergency stroke treatment systems). Conclusions: AI may improve LVO stroke detection and rapid triage necessary for expedited treatment. Standardization of performance assessment is needed in future studies.
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Cites
Goyal, M., Menon, B. K., van Zwam, W. H., Dippel, D. W. J., Mitchell, P. J., Demchuk, A. M., Dávalos, A., Majoie, C. B. L. M., van der Lugt, A., de Miquel, M. A., et al. (2016) · The Lancet · doi:10.1016/s0140-6736(16)00163-x · Cited by 7,815
Endovascular Therapy for Ischemic Stroke with Perfusion-Imaging Selection
Campbell, B. C. V., Mitchell, P. J., Kleinig, T. J., Dewey, H. M., Churilov, L., Yassi, N., Yan, B., Dowling, R. J., Parsons, M. W., Oxley, T. J., et al. (2015) · New England Journal of Medicine · doi:10.1056/nejmoa1414792 · Cited by 5,819
Thrombectomy 6 to 24 Hours after Stroke with a Mismatch between Deficit and Infarct
Nogueira, R. G., Jadhav, A. P., Haussen, D. C., Bonafe, A., Budzik, R. F., Bhuva, P., Yavagal, D. R., Ribo, M., Cognard, C., Hanel, R. A., et al. (2018) · New England Journal of Medicine · doi:10.1056/nejmoa1706442 · Cited by 5,629
Stent-Retriever Thrombectomy after Intravenous t-PA vs. t-PA Alone in Stroke
Saver, J. L., Goyal, M., Bonafe, A., Diener, H.-C., Levy, E. I., Pereira, V. M., Albers, G. W., Cognard, C., Cohen, D. J., Hacke, W., et al. (2015) · New England Journal of Medicine · doi:10.1056/nejmoa1415061 · Cited by 5,193
Thrombectomy for Stroke at 6 to 16 Hours with Selection by Perfusion Imaging
Albers, G. W., Marks, M. P., Kemp, S., Christensen, S., Tsai, J. P., Ortega-Gutierrez, S., McTaggart, R. A., Torbey, M. T., Kim-Tenser, M., Leslie-Mazwi, T., et al. (2018) · New England Journal of Medicine · doi:10.1056/nejmoa1713973 · Cited by 4,930
Orrù, G., Pettersson-Yeo, W., Marquand, A. F., Sartori, G., & Mechelli, A. (2012) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2012.01.004 · Cited by 1,012
del Zoppo, G. J., Saver, J. L., Jauch, E. C., & Adams, H. P. (2009) · Stroke · doi:10.1161/strokeaha.109.192535 · Cited by 829
Lansberg, M. G., Straka, M., Kemp, S., Mlynash, M., Wechsler, L. R., Jovin, T. G., Wilder, M. J., Lutsep, H. L., Czartoski, T. J., Bernstein, R. A., et al. (2012) · The Lancet Neurology · doi:10.1016/s1474-4422(12)70203-x · Cited by 784
Tan, I. Y. L., Demchuk, A. M., Hopyan, J., Zhang, L., Gladstone, D., Wong, K., Martin, M., Symons, S. P., Fox, A. J., & Aviv, R. I. (2009) · American Journal of Neuroradiology · doi:10.3174/ajnr.a1408 · Cited by 735
Real‐time diffusion‐perfusion mismatch analysis in acute stroke
Straka, M., Albers, G. W., & Bammer, R. (2010) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.22338 · Cited by 401
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How to improve access to medical imaging in low- and middle-income countries
Frija, G., Blažić, I., Frush, D. P., Hierath, M., Kawooya, M., Donoso-Bach, L., & Brkljačić, B. (2021) · eClinicalMedicine · doi:10.1016/j.eclinm.2021.101034 · Cited by 310
A reimbursement framework for artificial intelligence in healthcare
Abràmoff, M. D., Roehrenbeck, C., Trujillo, S., Goldstein, J., Graves, A. S., Repka, M. X., & Silva III, E. “. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00621-w · Cited by 117
Artificial intelligence in emergency radiology: A review of applications and possibilities
Katzman, B. D., van der Pol, C. B., Soyer, P., & Patlas, M. N. (2023) · Diagnostic and Interventional Imaging · doi:10.1016/j.diii.2022.07.005 · Cited by 97
Artificial Intelligence for Clinical Decision Support in Acute Ischemic Stroke: A Systematic Review
Akay, E. M. Z., Hilbert, A., Carlisle, B. G., Madai, V. I., Mutke, M. A., & Frey, D. (2023) · Stroke · doi:10.1161/strokeaha.122.041442 · Cited by 77
Yeo, M., Tahayori, B., Kok, H. K., Maingard, J., Kutaiba, N., Russell, J., Thijs, V., Jhamb, A., Chandra, R. V., Brooks, M., et al. (2021) · Journal of NeuroInterventional Surgery · doi:10.1136/neurintsurg-2020-017099 · Cited by 65
Metadata
- Authors
- Murray, N. M., Unberath, M., Hager, G. D., & Hui, F. K.
- Venue
- Journal of NeuroInterventional Surgery
- Publisher
- BMJ
- Published
- 2019-10-08
- Volume
- 12
- Issue
- 2
- Pages
- 156-164
- ISSN
- 1759-8478 (ISSN-L)
- 1759-8486
- OpenAlex
- W2979307665
- Cited by
- 308
- References
- 31
- FWCI
- 7.23
- Type
- journal article
- Language
- en
- Open access
- closed
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