PURPOSE: To retrospectively evaluate the effect of computer-aided detection (CAD) on radiologists' performance in detection of intracranial aneurysms with magnetic resonance (MR) angiography. MATERIALS AND METHODS: The institutional review board approved this study and did not require patient informed consent. Fifty maximum intensity projection MR angiograms in 50 patients were used for observer performance study. The group included 22 patients (age range, 43-86 years; mean, 60.2 years; 6 men and 16 women) with intracranial aneurysms and 28 patients (age range, 32-80 years; mean, 58.8 years; 10 men and 18 women) without aneurysms. The MR angiograms were obtained with three-dimensional time-of-flight 1.5-T MR imaging. Fifteen radiologists, including eight neuroradiologists and seven general radiologists, participated in the observer performance test. They interpreted the angiograms first without and then with the aid of the computer output by using an automated computerized scheme. The observers' performance without and with the computer output was evaluated with receiver operating characteristic analysis. RESULTS: For all 15 observers, average area under the receiver operating characteristic curve (A(z)) value for detection of aneurysms was increased significantly from 0.931 to 0.983 (P = .001) when they used the computer output. A(z) values for general radiologists and neuroradiologists increased from 0.894 to 0.983 (P = .022) and from 0.963 to 0.984 (P = .014), respectively. Improvement in the performance of general radiologists in terms of the A(z) value was much greater than that of neuroradiologists. Performance of general radiologists with CAD (A(z) = 0.983) slightly exceeded that of neuroradiologists without CAD (A(z) = 0.963) (P = .048). CONCLUSION: CAD improved neuroradiologists' and general radiologists' performance for detection of intracranial aneurysms with MR angiography; improvement was greater for general radiologists than it was for neuroradiologists.
PURPOSE: To retrospectively evaluate the effect of computer-aided detection (CAD) on radiologists' performance in detection of intracranial aneurysms with magnetic resonance (MR) angiography. MATERIALS AND METHODS: The institutional review board approved this study and did not require patient informed consent. Fifty maximum intensity projection MR angiograms in 50 patients were used for observer performance study. The group included 22 patients (age range, 43-86 years; mean, 60.2 years; 6 men and 16 women) with intracranial aneurysms and 28 patients (age range, 32-80 years; mean, 58.8 years; 10 men and 18 women) without aneurysms. The MR angiograms were obtained with three-dimensional time-of-flight 1.5-T MR imaging. Fifteen radiologists, including eight neuroradiologists and seven general radiologists, participated in the observer performance test. They interpreted the angiograms first without and then with the aid of the computer output by using an automated computerized scheme. The observers' performance without and with the computer output was evaluated with receiver operating characteristic analysis. RESULTS: For all 15 observers, average area under the receiver operating characteristic curve (A(z)) value for detection of aneurysms was increased significantly from 0.931 to 0.983 (P = .001) when they used the computer output. A(z) values for general radiologists and neuroradiologists increased from 0.894 to 0.983 (P = .022) and from 0.963 to 0.984 (P = .014), respectively. Improvement in the performance of general radiologists in terms of the A(z) value was much greater than that of neuroradiologists. Performance of general radiologists with CAD (A(z) = 0.983) slightly exceeded that of neuroradiologists without CAD (A(z) = 0.963) (P = .048). CONCLUSION: CAD improved neuroradiologists' and general radiologists' performance for detection of intracranial aneurysms with MR angiography; improvement was greater for general radiologists than it was for neuroradiologists.
Authors: Anja J Reimann; Ilias Tsiflikas; Harald Brodoefel; Michael Scheuering; Daniel Rinck; Andreas F Kopp; Claus D Claussen; Martin Heuschmid Journal: Int J Cardiovasc Imaging Date: 2008-09-28 Impact factor: 2.357
Authors: Z Shi; B Hu; U J Schoepf; R H Savage; D M Dargis; C W Pan; X L Li; Q Q Ni; G M Lu; L J Zhang Journal: AJNR Am J Neuroradiol Date: 2020-03-12 Impact factor: 3.825
Authors: I L Štepán-Buksakowska; J M Accurso; F E Diehn; J Huston; T J Kaufmann; P H Luetmer; C P Wood; X Yang; D J Blezek; R Carter; C Hagen; D Hořínek; A Hejčl; M Roček; B J Erickson Journal: AJNR Am J Neuroradiol Date: 2014-06-12 Impact factor: 3.825
Authors: S Miki; N Hayashi; Y Masutani; Y Nomura; T Yoshikawa; S Hanaoka; M Nemoto; K Ohtomo Journal: AJNR Am J Neuroradiol Date: 2016-02-18 Impact factor: 3.825
Authors: Xiaojiang Yang; Daniel J Blezek; Lionel T E Cheng; William J Ryan; David F Kallmes; Bradley J Erickson Journal: J Digit Imaging Date: 2009-11-24 Impact factor: 4.056