title: Using signal detection theory to model changes in serial learning of radiological image interpretation.
abstract: Signal detection theory (SDT) parameters can describe a learner's ability to discriminate (d') normal from abnormal and the learner's criterion (λ) to under or overcall abnormalities. To examine the serial changes in SDT parameters with serial exposure to radiological cases. 46 participants were recruited for this study: 20 medical students (MED), 6 residents (RES), 12 fellows (FEL), 5 staff pediatric emergency physicians (PEM), and 3 staff radiologists (RAD). Each participant was presented with 234 randomly assigned ankle radiographs using a web-based application. Participants were given a clinical scenario and considered 3 views of the ankle. They classified each case as normal or abnormal. For abnormal cases, they specified the location of the abnormality. Immediate feedback included highlighting on the images and the official radiologist's report. The low experience group (MED, RES, FEL) showed steady improvement in discrimination ability with each case, while the high experience group (PEM, RAD) had higher and stable discrimination ability throughout the exercise. There was also a difference in the way the high and low experience groups balanced sensitivity and specificity (λ) with the low experience group tending to make more errors calling positive radiographs negative. This tendency was progressively less evident with each increase in expertise level. SDT metrics provide valuable insight on changes associated with learning radiograph interpretation, and may be used to design more effective instructional strategies for a given learner group.
authors.affiliation: Department of Pediatrics, The Hospital for Sick Children, University of Toronto, 555 University Avenue, Toronto, ON, M5G 1X8, Canada. boutis@pol.net
authors.forename: Kathy
authors.initials: K
authors.lastname: Boutis
authors.forename: Martin
authors.initials: M
authors.lastname: Pecaric
authors.forename: Brian
authors.initials: B
authors.lastname: Seeto
authors.forename: Martin
authors.initials: M
authors.lastname: Pusic
mesh_terms: D000704:Analysis of Variance; D000842:Ankle / Q000000981:diagnostic imaging; D016001:Confidence Intervals; D004501:Education, Medical / Q000379:methods; D004521:Educational Measurement; D004522:Educational Status; D005360:Fibula / Q000000981:diagnostic imaging; D006801:Humans; D007858:Learning; D017145:Models, Educational; D009518:New York; D009864:Ontario; D011856:Radiographic Image Enhancement / Q000295:instrumentation / Q000379:methods*; D011871:Radiology / Q000193:education*; D017603:Signal Detection, Psychological; D013337:Students, Medical; D013663:Teaching; D013977:Tibia / Q000000981:diagnostic imaging