Challenges of implementing computer-aided diagnostic models for neuroimages in a clinical setting
Advances in artificial intelligence have cultivated a strong interest in develo** and
validating the clinical utilities of computer-aided diagnostic models. Machine learning for …
validating the clinical utilities of computer-aided diagnostic models. Machine learning for …
[HTML][HTML] Randomized controlled trials of artificial intelligence in clinical practice: systematic review
Background The number of artificial intelligence (AI) studies in medicine has exponentially
increased recently. However, there is no clear quantification of the clinical benefits of …
increased recently. However, there is no clear quantification of the clinical benefits of …
Brain tumor analysis using deep learning and VGG-16 ensembling learning approaches
A brain tumor is a distorted tissue wherein cells replicate rapidly and indefinitely, with no
control over tumor growth. Deep learning has been argued to have the potential to …
control over tumor growth. Deep learning has been argued to have the potential to …
Prospective evaluation of artificial intelligence triage of intracranial hemorrhage on noncontrast head CT examinations
BACKGROUND. Retrospective studies evaluating artificial intelligence (AI) algorithms for
intracranial hemorrhage (ICH) detection on noncontrast CT (NCCT) have shown promising …
intracranial hemorrhage (ICH) detection on noncontrast CT (NCCT) have shown promising …
Utilization of artificial intelligence–based intracranial hemorrhage detection on emergent noncontrast CT images in clinical workflow
M Seyam, T Weikert, A Sauter, A Brehm… - Radiology: Artificial …, 2022 - pubs.rsna.org
Authors implemented an artificial intelligence (AI)–based detection tool for intracranial
hemorrhage (ICH) on noncontrast CT images into an emergent workflow, evaluated its …
hemorrhage (ICH) on noncontrast CT images into an emergent workflow, evaluated its …
Accuracy of artificial intelligence for the detection of intracranial hemorrhage and chronic cerebral microbleeds: A systematic review and pooled analysis
Background Artificial intelligence (AI)-driven software has been developed and become
commercially available within the past few years for the detection of intracranial hemorrhage …
commercially available within the past few years for the detection of intracranial hemorrhage …
Accuracy of automated computer-aided diagnosis for stroke imaging: a critical evaluation of current evidence
There is increasing interest in computer applications, using artificial intelligence
methodologies, to perform health care tasks previously performed by humans, particularly in …
methodologies, to perform health care tasks previously performed by humans, particularly in …
Examination-Level Supervision for Deep Learning–based Intracranial Hemorrhage Detection on Head CT Scans
Purpose To compare the effectiveness of weak supervision (ie, with examination-level labels
only) and strong supervision (ie, with image-level labels) in training deep learning models …
only) and strong supervision (ie, with image-level labels) in training deep learning models …
Grade classification of tumors from brain magnetic resonance images using a deep learning technique
S Srinivasan, PSM Bai, SK Mathivanan… - Diagnostics, 2023 - mdpi.com
To improve the accuracy of tumor identification, it is necessary to develop a reliable
automated diagnostic method. In order to precisely categorize brain tumors, researchers …
automated diagnostic method. In order to precisely categorize brain tumors, researchers …
Development and external validation of a deep learning algorithm to identify and localize subarachnoid hemorrhage on CT scans
A Thanellas, H Peura, M Lavinto, T Ruokola, M Vieli… - Neurology, 2023 - AAN Enterprises
Background and Objectives In medical imaging, a limited number of trained deep learning
algorithms have been externally validated and released publicly. We hypothesized that a …
algorithms have been externally validated and released publicly. We hypothesized that a …