Intelligent image prefetching for supporting radiologists' primary reading: a decision-rule inductive learning approach

• Published in: IEEE transactions on systems, man and cybernetics. Part A, Systems and humans Vol. 35; no. 2; pp. 261 - 274
• Main Authors: Chih-Ping Wei, Paul Jen-Hwa Hu, Sheng, O.R.L., Yen-Hsien Lee
• Format: Journal Article
• Language: English
• Published: IEEE 01-03-2005
• Subjects: Algorithms; Artificial intelligence; Backpropagation algorithms; Biomedical imaging; Classification; CN2 algorithm; data mining; decision rule induction; image prefetching; Immune system; information technology (IT) applications and management in health care; Knowledge bases (artificial intelligence); knowledge discovery; Learning; Management; Neural networks; Organizations; patient-image management; Patients; Performance evaluation; Prefetching; Radio spectrum management; Radiology
• ISSN: 1083-4427; 1558-2426
• DOI: 10.1109/TSMCA.2005.843384

The expanded role of radiology in clinical medicine and its emerging digital practice have made patient-image management a growing concern for health-care organizations. A fundamental aspect of patient-image management is to provide a radiologist with convenient access to prior images relevant to his or her reading of a recently taken radiological examination. For confirmation or evaluation purposes, radiologists often reference relevant prior images of the same patient when interpreting the images of a current examination. To alleviate the time and physical requirements on radiologists, many health-care organizations have taken a prefetching strategy for meeting their patient-image reference needs. Radiologists' patient-image reference knowledge understandably may exhibit subtle individual variations and dynamically evolves over time, thus making the artificial intelligence-based inductive learning approach appealing. Central to patient-image prefetching is a knowledge base of which knowledge elements need continual update and individual customization. In this study, we extended a decision rule induction technique (i.e., CN2 algorithm) to address the challenging characteristics of the targeted learning. We experimentally evaluated the extended algorithm using the learning performances achieved by backpropagation neural network as benchmarks. Overall, our evaluation results suggest that the extended algorithm exhibited satisfactory learning effectiveness and, at the same time, showed desirable noise tolerance, immunity to missing data, and robustness in relation to limited training data.