The method adopted by using the newly developed blueprinting method and relating it to item analysis results has positive impact on the validity and reliability of students' performance results and their satisfaction in relation to intended learning outcomes.
Background The scope and productivity of artificial intelligence applications in health science and medicine, particularly in medical imaging, are rapidly progressing, with relatively recent developments in big data and deep learning and increasingly powerful computer algorithms. Accordingly, there are a number of opportunities and challenges for the radiological community. Purpose To provide review on the challenges and barriers experienced in diagnostic radiology on the basis of the key clinical applications of machine learning techniques. Material and Methods Studies published in 2010–2019 were selected that report on the efficacy of machine learning models. A single contingency table was selected for each study to report the highest accuracy of radiology professionals and machine learning algorithms, and a meta-analysis of studies was conducted based on contingency tables. Results The specificity for all the deep learning models ranged from 39% to 100%, whereas sensitivity ranged from 85% to 100%. The pooled sensitivity and specificity were 89% and 85% for the deep learning algorithms for detecting abnormalities compared to 75% and 91% for radiology experts, respectively. The pooled specificity and sensitivity for comparison between radiology professionals and deep learning algorithms were 91% and 81% for deep learning models and 85% and 73% for radiology professionals (p < 0.000), respectively. The pooled sensitivity detection was 82% for health-care professionals and 83% for deep learning algorithms (p < 0.005). Conclusion Radiomic information extracted through machine learning programs form images that may not be discernible through visual examination, thus may improve the prognostic and diagnostic value of data sets.
Objectives
Crohn’s disease (CD) is a condition that can occur in any part of the gastrointestinal tract, although usually forms in the colon and terminal ileum. Magnetic resonance imaging (MRI) has become a beneficial modality in the evaluation of small bowel activity. This study reports on a systematic review and meta-analysis of magnetic resonance enterography for the prediction of CD activity and evaluation of outcomes and possible complications.
Methods
Following the PRISMA guidelines, a total of 25 low-risk studies on established CD were selected, based on a QUADAS-II score of ≥ 9.
Results
A sensitivity of 90% was revealed in a pooled analysis of the 19 studies, with heterogeneity of χ2 = 81.83 and I2 of 80.3%. Also, a specificity of 89% was calculated, with heterogeneity of χ2 = 65.12 and I2 of 70.0%.
Conclusion
It was concluded that MRI provides an effective alternative to CT enterography in the detection of small bowel activity in CD patients under supervision of radiologist for assessment of disease activity and its complications. Its advantages include the avoidance of radiation exposure and good diagnostic accuracy.
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