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A Hybrid Approach for Sub-Acute Ischemic Stroke Lesion Segmentation Using Random Decision Forest and Gravitational Search Algorithm.
CONCLUSION: This paper provides a new hybrid GSA-RDF classifier technique to segment the ischemic stroke lesions in MR images. The experimental results demonstrate that the proposed technique has the Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Mean Bias Error (MBE) ranges are 16.5485 %, 7.2654 %, and 2.4585 %individually. The proposed RDF-GSA algorithm has better precision and execution when compared with the existing ischemic stroke segmentation method. PMID: 31975663 [PubMed - in process]
Source: Current Medical Imaging Reviews - January 26, 2020 Category: Radiology Tags: Curr Med Imaging Rev Source Type: research

Detection of early infarction signs with machine learning-based diagnosis by means of the Alberta Stroke Program Early CT score (ASPECTS) in the clinical routine
ConclusionFor ASPECTS assessment, the examined software may provide valid data in case of normal brain. It may enhance the work of neuroradiologists in clinical decision making. A final human check for plausibility is needed, particularly in patient groups with pre-existing cerebral changes.
Source: Neuroradiology - July 31, 2018 Category: Radiology 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

Machine learning studies on major brain diseases: 5-year trends of 2014 –2018
AbstractIn the recent 5  years (2014–2018), there has been growing interest in the use of machine learning (ML) techniques to explore image diagnosis and prognosis of therapeutic lesion changes within the area of neuroradiology. However, to date, the majority of research trend and current status have not been clearly il luminated in the neuroradiology field. More than 1000 papers have been published during the past 5 years on subject classification and prediction focused on multiple brain disorders. We provide a survey of 209 papers in this field with a focus on top ten active areas of research; i.e., Alzheimer’ s di...
Source: Japanese Journal of Radiology - November 29, 2018 Category: Radiology Source Type: research

Diverse Applications of Artificial Intelligence in Neuroradiology
Recent advances in artificial intelligence (AI) and deep learning (DL) hold promise to augment neuroimaging diagnosis for patients with brain tumors and stroke. Here, the authors review the diverse landscape of emerging neuroimaging applications of AI, including workflow optimization, lesion segmentation, and precision education. Given the many modalities used in diagnosing neurologic diseases, AI may be deployed to integrate across modalities (MR imaging, computed tomography, PET, electroencephalography, clinical and laboratory findings), facilitate crosstalk among specialists, and potentially improve diagnosis in patient...
Source: Neuroimaging Clinics - September 16, 2020 Category: Radiology Authors: Michael Tran Duong, Andreas M. Rauschecker, Suyash Mohan Source Type: research