J Neuroradiology
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Artificial intelligence (AI) is having a disruptive and transformative effect on clinical medicine. Prompt clinical diagnosis and imaging are critical for minimizing the morbidity and mortality associated with ischemic strokes. Clinicians must understand the current strengths and limitations of AI to provide optimal patient care. ⋯ In this review, we explain the basic concept of AI and machine learning. This review is without coding and mathematical details and targets the clinicians involved in stroke management without any computer or mathematics' background. Here the AI application in ischemic stroke is summarized and classified into stroke imaging (automated diagnosis of brain infarction, automated ASPECT score calculation, infarction segmentation), prognosis prediction, and patients' selection for treatment.
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Cervical spine injury is common in the setting of blunt trauma and there is consensus that cervical spine CT (CSCT) is the image modality of choice for initial evaluation for blunt trauma related injuries of the cervical spine. However, there is disagreement in the literature with regards to further evaluation of blunt trauma patients with cervical spine MRI (CSMRI) after negative CSCT when there is persistent clinical concern for occult trauma related injury. The purpose of this study is to examine the utility of CSMRI for detection of occult injury in blunt trauma patients after negative CSCT. ⋯ CSMRI detected significant injuries following negative CSCT in 31% (81/259) of patients. There were 15 cord contusions/infarcts, 9 bone contusions/fractures, 7 spinal canal hemorrhages and 66 soft tissue injuries. Upper extremity neurological deficit had greatest positive predictive value (PPV) for detection of CT-occult injury on CSMRI of 43% (23/53), followed by equivocal CSCT findings (38%, 18/47), presence of extra-cervical injuries (34%, 20/58), midline cervical tenderness (20%, 17/85), and isolated lower extremity neurological deficit (0%, 0/16). CSMRI is recommended following negative CSCT in the evaluation of blunt cervical spine trauma when appropriate clinical concerns are present.
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There are instances in which an estimate of the brain volume should be obtained from MRI in clinical practice. Our objective is to calculate cross-sectional robustness of a convolutional neural network (CNN) based software (Entelai Pic) for brain volume estimation and compare it to traditional software such as FreeSurfer, CAT12 and FSL in healthy controls (HC). ⋯ Based on robustness and processing times, our CNN-based model is suitable for cross-sectional volumetry on clinical practice.