ABSTRACT
Artificial intelligence, specifically machine learning and deep learning, is a rapidly developing field in imaging sciences with the potential to improve the efficiency and effectiveness of radiologists. This review covers common technical terms and basic concepts in imaging artificial intelligence and briefly reviews the application of these techniques to general imaging as well as head and neck imaging. Artificial intelligence has the potential to contribute improvements to all areas of patient care, including image acquisition, processing, segmentation, automated detection of findings, integration of clinical information, quality improvement, and research. Numerous challenges remain, however, before widespread imaging clinical adoption and integration occur.
Subject(s)
Artificial Intelligence , Head and Neck Neoplasms/diagnostic imaging , Image Interpretation, Computer-Assisted/methods , Magnetic Resonance Imaging/methods , Tomography, X-Ray Computed/methods , HumansABSTRACT
Prior studies have shown that dose-escalated radiation therapy for prostate cancer improves clinical outcomes. However, this is associated with increased rectal toxicity. Hydrogel spacer for prostate cancer therapy is an effective way of decreasing rectal toxicity in the late post-therapeutic stages. In some occasions, the gel spacer may not be placed symmetrically between the rectum and prostate. There are several forms of a malpositioned spacer, including lateral displacement, rectal wall infiltration, and prostate capsule infiltration. This manuscript is aimed at evaluating appropriately positioned and malpositioned gel spacers, primarily via magnetic resonance imaging. There are limited educational imaging guides that address what radiologists should evaluate on post-spacer placement imaging. This pictorial review will specifically evaluate post-injection pitfalls such as asymmetry, rectal wall infiltration, and subcapsular injection.
