The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Artificial Intelligence and Acute Stroke Imaging
Open accessAbstract
Artificial intelligence technology is a rapidly expanding field with many applications in acute stroke imaging, including ischemic and hemorrhage subtypes. Early identification of acute stroke is critical for initiating prompt intervention to reduce morbidity and mortality. Artificial intelligence can help with various aspects of the stroke treatment paradigm, including infarct or hemorrhage detection, segmentation, classification, large vessel occlusion detection, Alberta Stroke Program Early CT Score grading, and prognostication. In particular, emerging artificial intelligence techniques such as convolutional neural networks show promise in performing these imaging-based tasks efficiently and accurately. The purpose of this review is twofold: first, to describe AI methods and available public and commercial platforms in stroke imaging, and second, to summarize the literature of current artificial intelligence-driven applications for acute stroke triage, surveillance, and prediction.
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Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Machine learning: Trends, perspectives, and prospects
Jordan, M. I., & Mitchell, T. M. (2015) · Science · doi:10.1126/science.aaa8415 · Cited by 9,955
Convolutional neural networks: an overview and application in radiology
Yamashita, R., Nishio, M., Do, R. K. G., & Togashi, K. (2018) · Insights into Imaging · doi:10.1007/s13244-018-0639-9 · Cited by 4,682 · cited 1×
“17 More training data, regularization, and batch normalization are ways to mitigate overfitting.”
BackgroundLogistic regression and Artificial Neural Network Classification models: A methodology review
Dreiseitl, S., & Ohno-Machado, L. (2002) · Journal of Biomedical Informatics · doi:10.1016/s1532-0464(03)00034-0 · Cited by 2,177
Stroke risk factors, genetics, and prevention
Boehme, A. K., Esenwa, C., & Elkind, M. S. V. (2017) · Circulation Research · doi:10.1161/circresaha.116.308398 · Cited by 1,952
Setio, A. A. A., Ciompi, F., Litjens, G., Gerke, P., Jacobs, C., van Riel, S. J., Wille, M. M. W., Naqibullah, M., Sanchez, C. I., & van Ginneken, B. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2536809 · Cited by 1,258
Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study
Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N. G., Venugopal, V. K., Mahajan, V., Rao, P., & Warier, P. (2018) · The Lancet · doi:10.1016/s0140-6736(18)31645-3 · Cited by 1,020
Automatic Detection of Cerebral Microbleeds From MR Images via 3D Convolutional Neural Networks
Dou, Q., Chen, H., Yu, L., Zhao, L., Qin, J., Wang, D., Mok, V. C., Shi, L., & Heng, P.-A. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2528129 · Cited by 679
Improving Computer-Aided Detection UsingConvolutional Neural Networks and Random View Aggregation
Roth, H. R., Lu, L., Liu, J., Yao, J., Seff, A., Cherry, K., Kim, L., & Summers, R. M. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2015.2482920 · Cited by 608 · cited 1×
“12 CNNs have already shown promise in the detection of pulmonary nodules, 13 colon cancer, 14 and cerebral microbleeds.”
BackgroundArbabshirani, M. R., Fornwalt, B. K., Mongelluzzo, G. J., Suever, J. D., Geise, B. D., Patel, A. A., & Moore, G. J. (2018) · npj Digital Medicine · doi:10.1038/s41746-017-0015-z · Cited by 443
Cited by
The Role of AI in Hospitals and Clinics: Transforming Healthcare in the 21st Century
Maleki Varnosfaderani, S., & Forouzanfar, M. (2024) · Bioengineering · doi:10.3390/bioengineering11040337 · Cited by 931
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
Boonstra, A., & Laven, M. (2022) · BMC Health Services Research · doi:10.1186/s12913-022-08070-7 · Cited by 87
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
Artificial Intelligence in Emergency Radiology: Where Are We Going?
Cellina, M., Cè, M., Irmici, G., Ascenti, V., Caloro, E., Bianchi, L., Pellegrino, G., D’Amico, N., Papa, S., & Carrafiello, G. (2022) · Diagnostics · doi:10.3390/diagnostics12123223 · Cited by 62
Metadata
- Authors
- Soun, J. E., Chow, D. S., Nagamine, M., Takhtawala, R. S., Filippi, C. G., Yu, W., & Chang, P. D.
- Venue
- American Journal of Neuroradiology
- Published
- 2020-11-26
- Volume
- 42
- Issue
- 1
- Pages
- 2-11
- ISSN
- 0195-6108 (ISSN-L)
- 1936-959X
- OpenAlex
- W3110356609
- Cited by
- 272
- References
- 11
- FWCI
- 16.13
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Soun, J. E., Chow, D. S., Nagamine, M., Takhtawala, R. S., Filippi, C. G., Yu, W., & Chang, P. D. (2021). Artificial Intelligence and Acute Stroke Imaging. American Journal of Neuroradiology. https://doi.org/10.3174/ajnr.a6883
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