Literature DB >> 18212233

Diagnostic accuracy and reading time to detect intracranial aneurysms on MR angiography using a computer-aided diagnosis system.

Shingo Kakeda1, Yukunori Korogi, Hidetaka Arimura, Toshinori Hirai, Shigehiko Katsuragawa, Takatoshi Aoki, Kunio Doi.   

Abstract

OBJECTIVE: The objective of our study was to determine whether the use of a computer-aided diagnosis (CAD) system can shorten the reading time while maintaining the diagnostic performance of MR angiography for the detection of intracranial aneurysms.
MATERIALS AND METHODS: Fifty maximum-intensity-projection MR angiograms in 50 patients (16 intracranial aneurysms and 34 negative cases) were used for this observer performance study. Sixteen radiologists--eight neuroradiologists and eight less experienced radiologists--participated in the observer studies and interpreted the MR angiograms without and with CAD output images using an independent test method. The reading times without and with CAD were compared separately for the aneurysm and negative cases. The observers' performances were evaluated using receiver operating characteristic (ROC) analysis. We analyzed separately the data obtained from neuroradiologists and from less experienced radiologists.
RESULTS: For all observers, the mean area under the ROC curve (Az) with CAD was improved compared with that without CAD (0.903 vs 0.851, respectively; p = 0.109), and the mean reading time per case was reduced significantly by 18.1 seconds (28.5%) (from 63.4 to 45.3 seconds, p < 0.05). When CAD output images were available, the mean A(z) for the less experienced radiologists was significantly improved (0.911 vs 0.787, p < 0.05), but not for the neuroradiologists. The mean reading time of the less experienced radiologists with CAD was significantly shorter than that of the neuroradiologists without CAD (39.8 vs 54.5 seconds, p < 0.05).
CONCLUSION: The use of a CAD system for the detection of intracranial aneurysms on MR angiography can shorten the reading time while improving diagnostic performance for less experienced radiologists.

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Year:  2008        PMID: 18212233     DOI: 10.2214/AJR.07.2642

Source DB:  PubMed          Journal:  AJR Am J Roentgenol        ISSN: 0361-803X            Impact factor:   3.959


  4 in total

Review 1.  Artificial Intelligence in the Management of Intracranial Aneurysms: Current Status and Future Perspectives.

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

2.  Computer-aided diagnosis improves detection of small intracranial aneurysms on MRA in a clinical setting.

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

3.  Computer-Assisted Detection of Cerebral Aneurysms in MR Angiography in a Routine Image-Reading Environment: Effects on Diagnosis by Radiologists.

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

4.  Deep Learning-Based Software Improves Clinicians' Detection Sensitivity of Aneurysms on Brain TOF-MRA.

Authors:  B Sohn; K-Y Park; J Choi; J H Koo; K Han; B Joo; S Y Won; J Cha; H S Choi; S-K Lee
Journal:  AJNR Am J Neuroradiol       Date:  2021-08-12       Impact factor: 4.966

  4 in total

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