title: CAD: how it works, how to use it, performance.
abstract: Computer-aided diagnosis (CAD) systems are software algorithms designed to assist radiologists (or other practitioners) in solving a diagnostic problem by using a visual prompt (or "CAD mark") to direct the observer towards potential pathology. CT colonography is a recent arrival to CAD, but could represent one of its most fruitful applications in the future. In contrast to other organs, where a variety of different pathologies are equally represented, significant colorectal pathologies other than polyps and cancer are relatively uncommon. As we shall see, this simplifies the diagnostic task for artificial intelligence developers and also for radiologists who, ultimately, must make the final decision. This review aims to present the current state-of-the-art for CAD applied to CT colonography. A brief overview of the technical essentials and of the diagnostic performance of CAD in isolation, is followed by an explanation of how CAD is used in day-to-day practice. The last section will deal with the most controversial issues affecting CAD performance in clinical practice, with a focus on the interaction between human and artificial intelligence.
authors.affiliation: Institute for Cancer Research and Treatment, Candiolo-Torino, Italy. daniele.regge@ircc.it
authors.forename: Daniele
authors.initials: D
authors.lastname: Regge
authors.forename: Steve
authors.initials: S
authors.lastname: Halligan
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D023881:Colonography, Computed Tomographic / Q000379:methods*; D015179:Colorectal Neoplasms / Q000000981:diagnostic imaging*; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity