Literature DB >> 22595503

CAD: how it works, how to use it, performance.

Daniele Regge1, Steve Halligan.   

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.
Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

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Year:  2012        PMID: 22595503     DOI: 10.1016/j.ejrad.2012.04.022

Source DB:  PubMed          Journal:  Eur J Radiol        ISSN: 0720-048X            Impact factor:   3.528


  8 in total

1.  Artificial intelligence in musculoskeletal oncological radiology.

Authors:  Matjaz Vogrin; Teodor Trojner; Robi Kelc
Journal:  Radiol Oncol       Date:  2020-11-10       Impact factor: 2.991

2.  CT colonography: effect of computer-aided detection of colonic polyps as a second and concurrent reader for general radiologists with moderate experience in CT colonography.

Authors:  Thomas Mang; Luca Bogoni; Vikram X Anand; Dass Chandra; Andrew J Curtin; Anna S Lev-Toaff; Gerardo Hermosillo; Ralph Noah; Vikas Raykar; Marcos Salganicoff; Robert Shaw; Susan Summerton; Rafel F R Tappouni; Helmut Ringel; Michael Weber; Matthias Wolf; Nancy A Obuchowski
Journal:  Eur Radiol       Date:  2014-05-10       Impact factor: 5.315

3.  A comparison of material decomposition techniques for dual-energy CT colonography.

Authors:  Radin A Nasirudin; Rie Tachibana; Janne J Näppi; Kai Mei; Felix K Kopp; Ernst J Rummeny; Hiroyuki Yoshida; Peter B Noël
Journal:  Proc SPIE Int Soc Opt Eng       Date:  2015-02-21

4.  Application of Pseudo-enhancement Correction to Virtual Monochromatic CT Colonography.

Authors:  Rie Tachibana; Janne J Näppi; Hiroyuki Yoshida
Journal:  Abdom Imaging (2014)       Date:  2014-09

5.  Artificial intelligence in musculoskeletal oncological radiology.

Authors:  Matjaz Vogrin; Teodor Trojner; Robi Kelc
Journal:  Radiol Oncol       Date:  2020-11-10       Impact factor: 2.991

6.  How well do practicing radiologists interpret the results of CAD technology? A quantitative characterization.

Authors:  Fallon Branch; K Matthew Williams; Isabella Noel Santana; Jay Hegdé
Journal:  Cogn Res Princ Implic       Date:  2022-06-20

7.  A generative flow-based model for volumetric data augmentation in 3D deep learning for computed tomographic colonography.

Authors:  Tomoki Uemura; Janne J Näppi; Yasuji Ryu; Chinatsu Watari; Tohru Kamiya; Hiroyuki Yoshida
Journal:  Int J Comput Assist Radiol Surg       Date:  2020-11-05       Impact factor: 2.924

Review 8.  Development of artificial intelligence technology in diagnosis, treatment, and prognosis of colorectal cancer.

Authors:  Feng Liang; Shu Wang; Kai Zhang; Tong-Jun Liu; Jian-Nan Li
Journal:  World J Gastrointest Oncol       Date:  2022-01-15
  8 in total

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