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Integrating New Staff into Endovascular Stroke-Treatment Workflows in the COVID-19 Pandemic INTERVENTIONAL
SUMMARY: A health care crisis such as the coronavirus disease 2019 (COVID-19) pandemic requires allocation of hospital staff and resources on short notice. Thus, new and sometimes less experienced team members might join the team to fill in the gaps. This scenario can be particularly challenging in endovascular stroke treatment, which is a highly specialized task that requires seamless cooperation of numerous health care workers across various specialties and professions. This document is intended for stroke teams who face the challenge of integrating new team members into endovascular stroke-treatment workflows during the...
Source: American Journal of Neuroradiology - January 11, 2021 Category: Radiology Authors: Goyal, M., Kromm, J., Ganesh, A., Wira, C., Southerland, A., Sheth, K. N., Khosravani, H., Panagos, P., McNair, N., Ospel, J. M., On behalf of the AHA/ASA Stroke Council Science Subcommittees: Emergency Neurovascular Care (ENCC), the Cardiovascular and St Tags: INTERVENTIONAL Source Type: research

Prediction of Clinical Outcome in Patients with Large-Vessel Acute Ischemic Stroke: Performance of Machine Learning versus SPAN-100 FUNCTIONAL
CONCLUSIONS: Machine learning–based feature selection can identify parameters with higher performance in outcome prediction. Machine learning models with the best-performing features, especially advanced CTP data, had superior performance of the recovery outcome prediction for patients with stroke at admission in comparison with SPAN-100.
Source: American Journal of Neuroradiology - February 9, 2021 Category: Radiology Authors: Jiang, B., Zhu, G., Xie, Y., Heit, J. J., Chen, H., Li, Y., Ding, V., Eskandari, A., Michel, P., Zaharchuk, G., Wintermark, M. Tags: FUNCTIONAL Source Type: research

Automated ASPECTS on Noncontrast CT Scans in Patients with Acute Ischemic Stroke Using Machine Learning FUNCTIONAL
CONCLUSIONS: The proposed automated ASPECTS scoring approach shows reasonable ability to determine ASPECTS on NCCT images in patients presenting with acute ischemic stroke.
Source: American Journal of Neuroradiology - January 11, 2019 Category: Radiology Authors: Kuang, H., Najm, M., Chakraborty, D., Maraj, N., Sohn, S. I., Goyal, M., Hill, M. D., Demchuk, A. M., Menon, B. K., Qiu, W. Tags: FUNCTIONAL Source Type: research

Unsupervised Deep Learning for Stroke Lesion Segmentation on Follow-up CT Based on Generative Adversarial Networks FUNCTIONAL
CONCLUSIONS: An unsupervised generative adversarial network can be used to obtain automated infarct lesion segmentations with a moderate Dice similarity coefficient and good volumetric correspondence.
Source: American Journal of Neuroradiology - August 8, 2022 Category: Radiology Authors: van Voorst, H., Konduri, P. R., van Poppel, L. M., van der Steen, W., van der Sluijs, P. M., Slot, E. M. H., Emmer, B. J., van Zwam, W. H., Roos, Y. B. W. E. M., Majoie, C. B. L. M., Zaharchuk, G., Caan, M. W. A., Marquering, H. A., on behalf of the CONTR Tags: FUNCTIONAL Source Type: research

Automated Segmentation of Intracranial Thrombus on NCCT and CTA in Patients with Acute Ischemic Stroke Using a Coarse-to-Fine Deep Learning Model ADULT BRAIN
CONCLUSIONS: The proposed deep learning method can reliably detect and measure thrombi on NCCT and CTA in patients with acute ischemic stroke.
Source: American Journal of Neuroradiology - June 8, 2023 Category: Radiology Authors: Zhu, K., Bala, F., Zhang, J., Benali, F., Cimflova, P., Kim, B. J., McDonough, R., Singh, N., Hill, M. D., Goyal, M., Demchuk, A., Menon, B. K., Qiu, W. Tags: ADULT BRAIN Source Type: research

Tissue at Risk and Ischemic Core Estimation Using Deep Learning in Acute Stroke FUNCTIONAL
CONCLUSIONS: Deep learning models with fine-tuning lead to better performance for predicting tissue at risk and ischemic core, outperforming conventional thresholding methods.
Source: American Journal of Neuroradiology - June 10, 2021 Category: Radiology Authors: Yu, Y., Xie, Y., Thamm, T., Gong, E., Ouyang, J., Christensen, S., Marks, M. P., Lansberg, M. G., Albers, G. W., Zaharchuk, G. Tags: FUNCTIONAL Source Type: research

A Deep Learning-Based Approach to Reduce Rescan and Recall Rates in Clinical MRI Examinations ADULT BRAIN
CONCLUSIONS: Fast, automated deep learning–based image-quality rating can decrease rescan and recall rates, while rendering them technologist-independent. It was estimated that decreasing rescans and recalls from the technologists' values to the values of deep learning could save hospitals $24,000/scanner/year.
Source: American Journal of Neuroradiology - February 13, 2019 Category: Radiology Authors: Sreekumari, A., Shanbhag, D., Yeo, D., Foo, T., Pilitsis, J., Polzin, J., Patil, U., Coblentz, A., Kapadia, A., Khinda, J., Boutet, A., Port, J., Hancu, I. Tags: ADULT BRAIN Source Type: research

Parent Artery Reconstruction for Large or Giant Cerebral Aneurysms Using the Tubridge Flow Diverter: A Multicenter, Randomized, Controlled Clinical Trial (PARAT) INTERVENTIONAL
CONCLUSIONS: This trial showed an obviously higher rate of large and giant aneurysm obliteration with the Tubridge FD over Enterprise stent-assisted coiling. However, this higher obliteration rate came at the cost of a nonsignificantly higher rate of complications. Investigational site comparisons suggested that a learning curve for flow-diverter implantation should be recognized and factored into trial designs.
Source: American Journal of Neuroradiology - May 15, 2018 Category: Radiology Authors: Liu, J.- m., Zhou, Y., Li, Y., Li, T., Leng, B., Zhang, P., Liang, G., Huang, Q., Yang, P.- f., Shi, H., Zhang, J., Wan, J., He, W., Liang, C., Zhu, G., Xu, Y., Hong, B., Yang, X., Bai, W., Tian, Y., Zhang, H., Li, Z., Li, Q., Zhao, R., Fang, Y., Zhao, K. Tags: INTERVENTIONAL Source Type: research

Regarding "Automated Segmentation of Intracranial Thrombus on NCCT and CTA in Patients with Acute Ischemic Stroke Using a Coarse-to-Fine Deep Learning Model" letter
Source: American Journal of Neuroradiology - September 11, 2023 Category: Radiology Authors: Tortora, M., Pacchiano, F. Tags: letter Source Type: research