RESUMO
Rationale and objectives We sought to incorporate a new teaching module into the traditional medical student radiology clerkship, to improve the necessary skills for future referring physicians. Materials and methods A new required and graded module was introduced in 2014 into the radiology clerkship in year three of medical school: the Mystery Case. Each student was provided a unique and undifferentiated case from a dedicated teaching file containing de-identified images and requisition data. Students were expected to complete three serial tasks over one week: 1) prepare a voice recognition-derived, structured radiological report utilizing appropriate and relevant vocabulary; 2) discuss pertinent additional clinical information; and 3) discuss appropriate follow-up imaging, in addition to information on how to best prepare patients for these potential patient exams (e.g., with or without contrast, bowel preparation, and length of study). Students were provided written examples and dedicated class instruction with interactive discussions covering specific cases and associated related cases through random pairing with radiology resident and attending mentors. At the close of the week, students gave brief oral presentations of their cases and submitted the tasks for a written evaluation. Upon completion of the clerkship, the students completed a Likert-type six-item survey to evaluate the perceived improvement in select skills. Results The survey was completed by 82% (54/66) of the enrolled students, with 85% finding the Mystery Case an effective use of time. Medical students perceived an improved awareness of the patient care process (77%), awareness of the medical imaging resources available (89%), ability to understand a radiology report (74%), and ability to advise patients (69%). Conclusion Introduction of the Mystery Case as a graded exercise in the medical school radiology clerkship was perceived by students as effective use of time, with an improvement in the skills essential for future referring physicians.
RESUMO
An automated method for evaluating the image quality of calcified plaques with respect to motion artifacts in noncontrast-enhanced cardiac computed tomography (CT) images is introduced. This method involves using linear regression (LR) and artificial neural network (ANN) regression models for predicting two patient-specific, region-of-interest-specific, reconstruction-specific and temporal phase-specific image quality indices. The first is a plaque motion index, which is derived from the actual trajectory of the calcified plaque and is represented on a continuous scale. The second is an assessability index, which reflects the degree to which a calcified plaque is affected by motion artifacts, and is represented on an ordinal five-point scale. Two sets of assessability indices were provided independently by two radiologists experienced in evaluating cardiac CT images. Inputs for the regression models were selected from 12 features characterizing the dynamic, morphological, and intensity-based properties of the calcified plaques. Whereas LR-velocity (LR-V) used only a single feature (three-dimensional velocity), the LR-multiple (LR-M) and ANN regression models used the same subset of these 12 features selected through stepwise regression. The regression models were parameterized and evaluated using a database of simulated calcified plaque images from the dynamic NCAT phantom involving nine heart rate/multi-sector gating combinations and 40 cardiac phases covering two cardiac cycles. Six calcified plaques were used for the plaque motion indices and three calcified plaques were used for both sets of assessability indices. In one configuration, images from the second cardiac cycle were used for feature selection and regression model parameterization, whereas images from the first cardiac cycle were used for testing. With this configuration, repeated measures concordance correlation coefficients (CCCs) and associated 95% confidence intervals for the LR-V, LR-M, and ANN were 0.817 [0.785, 0.848], 0.894 [0.869, 0.916], and 0.917 [0.892, 0.936] for the plaque motion indices. For the two sets of assess-ability indices, CCC values for the ANN model were 0.843 [0.791, 0.877] and 0.793 [0.747, 0.828]. These two CCC values were statistically greater than the CCC value of 0.689 [0.648, 0.727], which was obtained by comparing the two sets of assessability indices with each other. These preliminary results suggest that the variabilities of assessability indices provided by regression models can lie within the variabilities of the indices assigned by independent observers. Thus, the potential exists for using regression models and assessability indices for determining optimal phases for cardiac CT image interpretation.
