Abstract
SUMMARY: Intracranial aneurysms with subarachnoid hemorrhage lead to high morbidity and mortality. It is of critical importance to detect aneurysms, identify risk factors of rupture, and predict treatment response of aneurysms to guide clinical interventions. Artificial intelligence has received worldwide attention for its impressive performance in image-based tasks. Artificial intelligence serves as an adjunct to physicians in a series of clinical settings, which substantially improves diagnostic accuracy while reducing physicians’ workload. Computer-assisted diagnosis systems of aneurysms based on MRA and CTA using deep learning have been evaluated, and excellent performances have been reported. Artificial intelligence has also been used in automated morphologic calculation, rupture risk stratification, and outcomes prediction with the implementation of machine learning methods, which have exhibited incremental value. This review summarizes current advances of artificial intelligence in the management of aneurysms, including detection and prediction. The challenges and future directions of clinical implementations of artificial intelligence are briefly discussed.
ABBREVIATIONS:
- AI
- artificial intelligence
- AUC
- area under the curve
- CAD
- computer-assisted diagnostics
- DL
- deep learning
- FP
- false-positive
- ML
- machine learning
- SVM
- support vector machines
Footnotes
This work was supported by the National Key Research and Development Program of China (2017YFC0113400 for L.J.Z.) and Key Projects of the National Natural Science Foundation of China (81830057 for L.J.Z.).
Disclosures: Zhao Shi—RELATED: Grant: Key Projects of the National Natural Science Foundation of China (81830057 for L.J.Z.).* U. Joseph Schoepf—UNRELATED: Other: Dr Schoepf has received institutional research support and/or honoraria for speaking and consulting from Astellas Pharma, Bayer, Bracco, Elucid BioImaging, GE Healthcare, Guerbet, HeartFlow, and Siemens.* Xiuli Li—RELATED: Key Projects of the National Natural Science Foundation of China (81830057 for L.J.Z.).* Long Jiang Zhang—RELATED: Grant: National Key Research and Development Program of China; Key Projects of the National Natural Science Foundation of China, Comments: This work was supported by the National Key Research and Development Program of China (2017YFC0113400 for L.J.Z.) and Key Projects of the National Natural Science Foundation of China (81830057 for L.J.Z.).* *Money paid to the institution.
- © 2020 by American Journal of Neuroradiology
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