title: Computer-aided diagnosis and artificial intelligence in clinical imaging.
abstract: Computer-aided diagnosis (CAD) is rapidly entering the radiology mainstream. It has already become a part of the routine clinical work for the detection of breast cancer with mammograms. The computer output is used as a "second opinion" in assisting radiologists' image interpretations. The computer algorithm generally consists of several steps that may include image processing, image feature analysis, and data classification via the use of tools such as artificial neural networks (ANN). In this article, we will explore these and other current processes that have come to be referred to as "artificial intelligence." One element of CAD, temporal subtraction, has been applied for enhancing interval changes and for suppressing unchanged structures (eg, normal structures) between 2 successive radiologic images. To reduce misregistration artifacts on the temporal subtraction images, a nonlinear image warping technique for matching the previous image to the current one has been developed. Development of the temporal subtraction method originated with chest radiographs, with the method subsequently being applied to chest computed tomography (CT) and nuclear medicine bone scans. The usefulness of the temporal subtraction method for bone scans was demonstrated by an observer study in which reading times and diagnostic accuracy improved significantly. An additional prospective clinical study verified that the temporal subtraction image could be used as a "second opinion" by radiologists with negligible detrimental effects. ANN was first used in 1990 for computerized differential diagnosis of interstitial lung diseases in CAD. Since then, ANN has been widely used in CAD schemes for the detection and diagnosis of various diseases in different imaging modalities, including the differential diagnosis of lung nodules and interstitial lung diseases in chest radiography, CT, and position emission tomography/CT. It is likely that CAD will be integrated into picture archiving and communication systems and will become a standard of care for diagnostic examinations in daily clinical work.
authors: Kurt Rossmann Laboratories for Radiologic Image Research, Department of Radiology, the University of Chicago, Chicago, IL 60637, USA.
authors: Junji
authors: J
authors: Shiraishi
authors: Qiang
authors: Q
authors: Li
authors: Daniel
authors: D
authors: Appelbaum
authors: Kunio
authors: K
authors: Doi
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001842:Bone and Bones / Q000000981:diagnostic imaging; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D017563:Lung Diseases, Interstitial / Q000000981:diagnostic imaging; D016571:Neural Networks, Computer; D049268:Positron-Emission Tomography; D011446:Prospective Studies; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D013382:Subtraction Technique / Q000295:instrumentation*; D014057:Tomography, X-Ray Computed; D051598:Whole Body Imaging