Development of a Personalized Training System Using the Lung Image Database Consortium and Image Database Resource Initiative Database

作者：Lin, Hongli^*; Wang, Weisheng; Luo, Jiawei; Yang, Xuedong

来源：Academic Radiology, 2014, 21(12): 1614-1622.

DOI：10.1016/j.acra.2014.07.020

• Personalized radiology education
• LIDC/IDRI
• Content-Boosted Collaborative Filtering
• lung nodule

Rationale and Objectives: The aim of this study was to develop a personalized training system using the Lung Image Database Consortium (LIDC) and Image Database resource Initiative (IDRI) Database, because collecting, annotating, and marking a large number of appropriate computed tomography (CT) scans, and providing the capability of dynamically selecting suitable training cases based on the performance levels of trainees and the characteristics of cases are critical for developing a efficient training system.
Materials and Methods: A novel approach is proposed to develop a personalized radiology training system for the interpretation of lung nodules in CT scans using the Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI) database, which provides a Content-Boosted Collaborative Filtering (CBCF) algorithm for predicting the difficulty level of each case of each trainee when selecting:suitable cases to meet individual needs, and a diagnostic simulation tool to enable trainees to analyze and diagnose lung nodules with the help of an image processing tool and a nodule retrieval tool.
Results: Preliminary evaluation of the system shows that developing a personalized training system for interpretation of lung nodules is needed and useful to enhance the professional skills of trainees.
Conclusions: The approach of developing personalized training systems using the LIDC/IDRL database is a feasible solution to the challenges of constructing specific training program in terms of cost and training efficiency.

• 出版日期2014-12
• 单位湖南大学