Building and Exploitation of Learning Curves to Train Radiographer Students in X-Ray CT Image Postprocessing.

INTRODUCTION This 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 METHODS This 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. RESULTS The 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. DISCUSSION The 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. CONCLUSION Obtaining 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.

[1]  Rolland Viau La motivation en contexte scolaire , 1994 .

[2]  R. Hatala,et al.  Learning Curves in Health Professions Education , 2015, Academic medicine : journal of the Association of American Medical Colleges.

[3]  O. Langeron,et al.  Évaluation de l'apprentissage d'un nouveau guide lumineux (trachlight™) pour l'intubation trachéale , 1997 .

[4]  J. Barsuk,et al.  Does Simulation-Based Medical Education With Deliberate Practice Yield Better Results Than Traditional Clinical Education? A Meta-Analytic Comparative Review of the Evidence , 2011, Academic medicine : journal of the Association of American Medical Colleges.

[5]  N. Abdala,et al.  Learning curve of radiology residents during training in fluoroscopy-guided facet joint injections , 2017, Radiologia brasileira.

[6]  Joann G. Elmore,et al.  When radiologists perform best: the learning curve in screening mammogram interpretation. , 2009, Radiology.

[7]  T. P. Wright,et al.  Factors affecting the cost of airplanes , 1936 .

[8]  Mohamad Y. Jaber,et al.  Learning Curves : Theory, Models, and Applications , 2011 .

[9]  J. Marty,et al.  Mise en place du masque Laryngé-Fastrach™ au sein d'un service médical d'urgence et de réanimation ☆ , 2006 .

[10]  G. Materazzi,et al.  Parathyroïdectomie vidéoassistée : courbe d’apprentissage , 2001 .

[11]  Jorge J. Moré,et al.  The Levenberg-Marquardt algo-rithm: Implementation and theory , 1977 .

[12]  Louis E. Yelle THE LEARNING CURVE: HISTORICAL REVIEW AND COMPREHENSIVE SURVEY , 1979 .

[13]  K. Lee,et al.  Initial Performance of Radiologists and Radiology Residents in Interpreting Low-Dose (2-mSv) Appendiceal CT. , 2015, AJR. American journal of roentgenology.

[14]  M. Pusic,et al.  How Much Practice Is Enough? Using Learning Curves to Assess the Deliberate Practice of Radiograph Interpretation , 2011, Academic medicine : journal of the Association of American Medical Colleges.

[15]  A F Monk,et al.  ASSESSMENT OF THE LEARNING CURVE IN HEALTH TECHNOLOGIES , 2000, International Journal of Technology Assessment in Health Care.