E-052 Artificial neural network ct perfusion prediction of ischemic core. (22nd July 2019)
- Record Type:
- Journal Article
- Title:
- E-052 Artificial neural network ct perfusion prediction of ischemic core. (22nd July 2019)
- Main Title:
- E-052 Artificial neural network ct perfusion prediction of ischemic core
- Authors:
- Kasasbeh, A
Christensen, S
Lansberg, M - Abstract:
- Abstract : Background and purpose: Computer Tomography Perfusion (CTP) is a useful tool in the evaluation of acute ischemic stroke, where it can provide an estimate of the ischemic core and the ischemic penumbra. The optimal CTP parameters to identify the ischemic core remain undetermined. Methods: We utilized Artificial Neural Networks (ANNs) to optimally predict the ischemic core in acute stroke patients, using diffusion-weighted imaging as the gold standard. We first designed an ANN based on CTP data alone and next designed an ANN based on clinical and CTP data. Results: The ANN based on CTP data predicted the ischemic core with a mean absolute error of 13.8 ml (SD 13.6 ml) compared to DWI. The area under the receiver operator characteristic curve (AUC) was 0.85. At the optimal threshold, the sensitivity for predicting the ischemic core was 0.90 and the specificity was 0.62. Combining CTP data with clinical data available at time of presentation resulted in the same mean absolute error (13.8 ml) but lower SD (12.4 ml). Furthermore, the AUC, sensitivity, and specificity were 0.87, 0.91, and 0.65, respectively. The maximal Dice coefficient was 0.48 in the ANN based on CTP data exclusively. Conclusions: An artificial neural network that integrates clinical and CTP data predicts the ischemic core with accuracy. Disclosures: A. Kasasbeh: None. S. Christensen: 2; C; Dr. Søren Christensen is an equity shareholders in iSchemaView and perform consulting work for iSchemaView. 4; C;Abstract : Background and purpose: Computer Tomography Perfusion (CTP) is a useful tool in the evaluation of acute ischemic stroke, where it can provide an estimate of the ischemic core and the ischemic penumbra. The optimal CTP parameters to identify the ischemic core remain undetermined. Methods: We utilized Artificial Neural Networks (ANNs) to optimally predict the ischemic core in acute stroke patients, using diffusion-weighted imaging as the gold standard. We first designed an ANN based on CTP data alone and next designed an ANN based on clinical and CTP data. Results: The ANN based on CTP data predicted the ischemic core with a mean absolute error of 13.8 ml (SD 13.6 ml) compared to DWI. The area under the receiver operator characteristic curve (AUC) was 0.85. At the optimal threshold, the sensitivity for predicting the ischemic core was 0.90 and the specificity was 0.62. Combining CTP data with clinical data available at time of presentation resulted in the same mean absolute error (13.8 ml) but lower SD (12.4 ml). Furthermore, the AUC, sensitivity, and specificity were 0.87, 0.91, and 0.65, respectively. The maximal Dice coefficient was 0.48 in the ANN based on CTP data exclusively. Conclusions: An artificial neural network that integrates clinical and CTP data predicts the ischemic core with accuracy. Disclosures: A. Kasasbeh: None. S. Christensen: 2; C; Dr. Søren Christensen is an equity shareholders in iSchemaView and perform consulting work for iSchemaView. 4; C; Dr. Søren Christensen is an equity shareholders in iSchemaView and perform consulting work for iSchemaView. M. Lansberg: None. … (more)
- Is Part Of:
- Journal of neurointerventional surgery. Volume 11(2019)Supplement 1
- Journal:
- Journal of neurointerventional surgery
- Issue:
- Volume 11(2019)Supplement 1
- Issue Display:
- Volume 11, Issue 1 (2019)
- Year:
- 2019
- Volume:
- 11
- Issue:
- 1
- Issue Sort Value:
- 2019-0011-0001-0000
- Page Start:
- A75
- Page End:
- A75
- Publication Date:
- 2019-07-22
- Subjects:
- Nervous system -- Surgery -- Periodicals
Cerebrovascular disease -- Surgery -- Periodicals
617.48 - Journal URLs:
- http://www.bmj.com/archive ↗
http://jnis.bmj.com/ ↗ - DOI:
- 10.1136/neurintsurg-2019-SNIS.127 ↗
- Languages:
- English
- ISSNs:
- 1759-8478
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 18894.xml