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Building and Exploitation of Learning Curves to Train Radiographer Students in X-Ray CT Image Postprocessing
Institution:1. Section Imagerie Médicale et Radiologie Thérapeutique, Lycée Jean Rostand, Académie de Strasbourg, Strasbourg, France;2. Comité scientifique de l’Association Française du Personnel Paramédical d’Electroradiologie Médicale (AFPPE), Montrouge, Paris, France;3. Pôle d’imagerie médicale, Hôpital de Hautepierre, Hôpitaux Universitaires de Strasbourg, Strasbourg, France;4. ICube – UMR 7357, CNRS, Université de Strasbourg, Strasbourg, France;1. Division of Interventional Radiology, Department of Radiology, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl., Box 1234, New York, NY 10029.;2. Division of Interventional Radiology, Piedmont Radiology, Atlanta, Georgia.;1. Department of Cardiovascular Medicine, William Beaumont Hospital, 3601 W. 13 Mile Road, Royal Oak, MI, USA;2. Department of Radiology, William Beaumont Hospital, 3601 W. 13 Mile Road, Royal Oak, MI, USA;3. Department of Research Administration, William Beaumont Hospitals, 3601 W. 13 Mile Road, Royal Oak, MI, USA;4. Department of Diagnostic Radiology, Massachusetts General Hospital, Boston, MA, USA;5. Department of Cardiac Imaging, King Abdul-Aziz Cardiac Center, Riyadh, Kingdom of Saudi Arabia;6. Department of Radiology, Naval Medical Center, San Diego, CA, USA;7. Department of Cardiovascular Medicine, William Beaumont Hospital, Troy, MI, USA;8. Division of Radiology, Imaging Institute, Cleveland Clinic, Cleveland, OH, USA;9. Department of Radiology, New York University, New York, NY, USA;10. Cardiac Imaging, Minneapolis Heart Institute, Minneapolis, MN, USA;11. Department of Cardiology, Medical University of South Carolina, Charleston, SC, USA;12. Department Radiology, Medical University of South Carolina, Charleston, SC, USA;13. University of Minnesota, Minneapolis, MN, USA;1. Division of Vascular and Endovascular Surgery, Department of Surgery, University of Virginia Health System, Charlottesville, VA;2. Division of Cardiothoracic Surgery, Department of Surgery, University of Washington, Seattle, WA;1. Laboratory of Production Engineering (LGP), EA 1905, ENIT-INPT, University of Toulouse, 47, avenue d’Azereix, BP 1629, 65016 Tarbes cedex, France;2. Center for IT Research, Langston University, OK 73050, USA;3. Aalto University School of Electrical Engineering, PL 13000, 00076 Aalto, Espoo, Finland;4. Center for Food and Taste sciences (CSGA) – UMR 6265 CNRS – UMR 1324 INRA – University of Burgundy, 9E, boulevard Jeanne-d’Arc, 21000 Dijon, France;1. Section of Interventional Radiology, Department of Radiology, Northwestern University, 676 North St. Clair, Suite 800, Chicago, IL 60611;2. Division of Hematology and Oncology, Department of Medicine, Northwestern University, 676 North St. Clair, Suite 800, Chicago, IL 60611
Abstract:IntroductionThis study aims to construct learning curves related to the realization of standardized postprocessing by radiographer students and to discuss their exploitation and interest.Materials and MethodsThis study was carried out in 21 French students in their 3rd year of training. Two postprocessing protocols in CT (#1 traumatic shoulder; #2 petrous bone) were repeated 15 times by each student. Each achievement was timed to obtain overall learning curves. The realization accuracy was also assessed for each student at each repetition.ResultsThe learning rates for the two protocols are 63% and 56%, respectively. The number of repetitions to reach the reference time for each protocol is 11 and 12, respectively. In both protocols, the standard deviations are significantly reduced and stabilized during repetitions. The mean accuracy progresses more quickly in protocol #1.DiscussionThe measured learning rates reflect a rapid learning process for each protocol. The analysis of the standard deviations shows that students have reached a homogeneous level. The average times and accuracies measured during the last repetitions show that the group has reached a high level of performance. Building learning curves helps students measure their progress and motivates them.ConclusionObtaining learning curves allows trainers/supervisors to qualify the learning difficulty of a task while motivating students/radiographers. The use of learning curves is inline with the competency-based training paradigm.
Keywords:Educational techniques  learning curves  postprocessing image processing (computer-assisted)  X-ray computed tomography  radiographer  technologist  courbes d’apprentissage  post-traitement  scanner X  manipulateur en électroradiologie médicale
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