Stanley Parker,  Stanley Charles

Final Pr. ID: Poster #: EDU-010 (T)

In 2017, roughly 2 trillion (2,000,000,000,000) medical images were produced, reviewed, reported, archived, and used in the detection and management of disease. Based on historical trends, this number has doubled every 5 years and is accelerating. This explosive growth in imaging data has created major opportunities for the use of Artificial Intelligence (AI). The question is less whether radiologists, and technologists, will be replaced by AI (they will not) and more about whether we could survive without AI. Although intelligent algorithms have been used for some time in segments of the imaging field, new methods of machine learning, based particularly on “deep learning”, are much more powerful. Many of the deep learning publications today point to the promise of significant advances in efficiency, precision, reproducibility, and prognostic abilities.
If AI will not replace radiologists/technologists but rather augment them with tools to meet the rising demands for diagnostic imaging, then it is imperative that we have a basic understanding of the concepts and language that defines this area of knowledge. In the not so distant past the average technologist understood the basics of film processing but wouldn’t even recognize the words DICOM or EMR; we are now at that point of change with AI. Deep learning, machine learning, neural networks, ground truth, the list goes on. The goal of this presentation is to provide a basic framework of the concepts, terminology, and references to how AI has, and likely, will be employed in medical imaging, thus making us better practitioners and partners with this technology.
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Authors:  Stanley Parker , Stanley Charles

Keywords:  Artificial Intelligence, Medical Imaging, Technologist

White Stacy,  Shellikeri Sphoorti,  Sze Raymond

Final Pr. ID: Poster #: SCI-028

Background Leg length discrepancy studies are labor intensive, yet cognitively simple, studies that represent inefficient use of the pediatric radiologists’ time and expertise.
Objective The purpose of this study was to demonstrate that measuring and calculating leg length discrepancies do not require radiologist expertise. We hypothesized that radiology technologists could be trained to quantify leg length discrepancies and that their performance would be statistically equivalent to that of board-certified, fellowship trained pediatric radiologists. Read More

Authors:  White Stacy , Shellikeri Sphoorti , Sze Raymond

Keywords:  Technologist, Leg, Length

Elliott Lauren,  Alazraki Adina,  Milla Sarah

Final Pr. ID: Poster #: EDU-010 (R)

Fetal MRI is a unique method which allows clinicians to diagnose or exclude abnormalities during pregnancy. MR images are acquired when details from an ultrasound may not be sufficient to make a complete prenatal diagnosis or when there are concerns for additional findings. This method of imaging is optimal due to the diagnostic quality of the images and the absence of ionizing radiation. We present an educational exhibit to discuss the methodology in performing a fetal MRI as well as demonstrating common findings in fetal MRI. Read More

Authors:  Elliott Lauren , Alazraki Adina , Milla Sarah

Keywords:  Fetal, MRI, Technologist

Chin Nicole,  Weisel Melissa,  Alazraki Adina,  Milla Sarah

Final Pr. ID: Poster #: EDU-005 (R)

Whole body MR imaging is now widely utilized in the diagnosis and staging of pediatric patients with systemic disorders and diseases such as Chronic nonbacterial osteomyelitis (CNO), Neuroblastoma, Langerhans cell histocytosis (LCH) and fever of unknown origin (FUO). This technique allows the radiologist to visualize the entire body thereby, providing information regarding the full extent of disease allowing clinicians to direct the patient’s treatment. As an alternative to CT and Nuclear medicine, MR whole body imaging produces superior results without exposing the patient to ionizing radiation. We present an educational guide to practical positioning of the patient, coil positioning and protocol optimization. Read More

Authors:  Chin Nicole , Weisel Melissa , Alazraki Adina , Milla Sarah

Keywords:  Whole Body Imaging, MRI, Technologist

Mcenneny Jamie,  Morrison Jessica,  Mesi Erin,  Young Cody,  Martin Lisa

Final Pr. ID: Paper #: 083

Repeat imaging is often a result of unnecessary errors in 1 or more diagnostic categories. To minimize the number of Head and Abdominal CTs with suboptimal diagnostic quality, we set out to create a standardized list of criteria that assess the quality of the study. This list of standards and evaluation criteria successfully provided the technologists knowledge and insight into what our radiologists require to diagnose our pediatric patients. Read More

Authors:  Mcenneny Jamie , Morrison Jessica , Mesi Erin , Young Cody , Martin Lisa

Keywords:  QA, technologist