Artificial intelligence and acute stroke imaging
JE Soun, DS Chow, M Nagamine… - American Journal …, 2021 - Am Soc Neuroradiology
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 imaging, including ischemic and hemorrhage subtypes. Early identification of acute …
[HTML][HTML] Machine learning in action: stroke diagnosis and outcome prediction
The application of machine learning has rapidly evolved in medicine over the past decade.
In stroke, commercially available machine learning algorithms have already been …
In stroke, commercially available machine learning algorithms have already been …
[HTML][HTML] Precise diagnosis of intracranial hemorrhage and subtypes using a three-dimensional joint convolutional and recurrent neural network
Objectives To evaluate the performance of a novel three-dimensional (3D) joint
convolutional and recurrent neural network (CNN-RNN) for the detection of intracranial …
convolutional and recurrent neural network (CNN-RNN) for the detection of intracranial …
Expert-level detection of acute intracranial hemorrhage on head computed tomography using deep learning
Computed tomography (CT) of the head is used worldwide to diagnose neurologic
emergencies. However, expertise is required to interpret these scans, and even highly …
emergencies. However, expertise is required to interpret these scans, and even highly …
Deep learning based noise reduction for brain MR imaging: tests on phantoms and healthy volunteers
M Kidoh, K Shinoda, M Kitajima, K Isogawa… - Magnetic resonance in …, 2020 - jstage.jst.go.jp
Purpose: To test whether our proposed denoising approach with deep learning-based
reconstruction (dDLR) can effectively denoise brain MR images. Methods: In an initial …
reconstruction (dDLR) can effectively denoise brain MR images. Methods: In an initial …
Deep learning–assisted diagnosis of cerebral aneurysms using the HeadXNet model
Importance Deep learning has the potential to augment clinician performance in medical
imaging interpretation and reduce time to diagnosis through automated segmentation. Few …
imaging interpretation and reduce time to diagnosis through automated segmentation. Few …
Deep learning for hemorrhagic lesion detection and segmentation on brain CT images
L Li, M Wei, BO Liu, K Atchaneeyasakul… - IEEE journal of …, 2020 - ieeexplore.ieee.org
Stroke is an acute cerebral vascular disease that is likely to cause long-term disabilities and
death. Immediate emergency care with accurate diagnosis of computed tomographic (CT) …
death. Immediate emergency care with accurate diagnosis of computed tomographic (CT) …
Artificial intelligence system approaching neuroradiologist-level differential diagnosis accuracy at brain MRI
Background Although artificial intelligence (AI) shows promise across many aspects of
radiology, the use of AI to create differential diagnoses for rare and common diseases at …
radiology, the use of AI to create differential diagnoses for rare and common diseases at …
[HTML][HTML] Accurate and efficient intracranial hemorrhage detection and subtype classification in 3D CT scans with convolutional and long short-term memory neural …
In this paper, we present our system for the RSNA Intracranial Hemorrhage Detection
challenge, which is based on the RSNA 2019 Brain CT Hemorrhage dataset. The proposed …
challenge, which is based on the RSNA 2019 Brain CT Hemorrhage dataset. The proposed …
[HTML][HTML] Intracranial hemorrhage segmentation using a deep convolutional model
MD Hssayeni, MS Croock, AD Salman, HF Al-Khafaji… - Data, 2020 - mdpi.com
Traumatic brain injuries may cause intracranial hemorrhages (ICH). ICH could lead to
disability or death if it is not accurately diagnosed and treated in a time-sensitive procedure …
disability or death if it is not accurately diagnosed and treated in a time-sensitive procedure …