University of Twente Student Theses


Difficulty Measure for Radiology Cases with use of Item Analysis and Deep Learning

Benthem, M.H. van (2022) Difficulty Measure for Radiology Cases with use of Item Analysis and Deep Learning.

Full text not available from this repository.

Full Text Status:Access to this publication is restricted
Embargo date:19 October 2024
Abstract:A vast and extensive radiology education is fundamental for the diagnosis, monitoring, and treatment of lung cancer. Image synthesis, with Generative Adversarial Networks (GANs), can be a powerful tool in radiology education by its ability to diversify training cases for medical students and radiology residents. By controlling the image synthesis, images can be produced with a specific difficulty or complexity level that fits the student’s level. To achieve optimal personalization of education, the knowledge gap should be defined by a concrete measure. This research focuses on a measure of difficulty for the detection of lung nodules. Item analysis is a statistical method that can give an indication of the difficulty based on the responses of a group of individuals. To automate the calculation of a measure of difficulty, deep neural networks are used to perform item analysis on lung nodule cases. The method is validated by comparing the measure of difficulty with a subjective subtlety score given by experienced radiologists. The ordinal logistic regression analysis shows a statistically significant relationship between the calculated measure of difficulty and the subtlety scores of nodules. A measure of difficulty is defined that has the potential to be applied to image synthesis for the design of computer-assisted learning systems.
Item Type:Essay (Master)
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:50 technical science in general
Programme:Biomedical Engineering MSc (66226)
Link to this item:
Export this item as:BibTeX
HTML Citation
Reference Manager


Repository Staff Only: item control page