Literature DB >> 18562756

Usefulness of computer-aided diagnosis schemes for vertebral fractures and lung nodules on chest radiographs.

Satoshi Kasai1, Feng Li, Junji Shiraishi, Kunio Doi.   

Abstract

OBJECTIVE: We retrospectively evaluated the usefulness of computer-aided diagnosis (CAD) schemes to radiologist performance in the simultaneous detection of vertebral fractures and lung nodules on chest radiographs.
MATERIALS AND METHODS: We evaluated posteroanterior and lateral chest images of 21 patients with vertebral fractures, 31 patients with lung nodules, and 10 persons acting as controls. The total number of subjects was 60 because both lesions were present in four patients. Eighteen radiologists were asked to detect vertebral fractures and nodules simultaneously on posteroanterior and lateral images. The radiologists indicated their confidence level ratings regarding the presence or absence of lesions and the most likely location of each lesion on either posteroanterior or lateral images, first without and then with CAD output. The observers' performance was evaluated with use of receiver operating characteristic (ROC) and jackknife free-response ROC curves.
RESULTS: With the CAD scheme, the average area under the ROC curve for detection of vertebral fractures improved from 0.906 to 0.951 (p = 0.002). That for lung nodules also improved, but the improvement was not statistically significant (0.804-0.816, p = 0.297). The figure-of-merit values obtained with the jackknife free-response ROC program improved from 0.585 to 0.680 (p < 0.001) for vertebral fractures and from 0.622 to 0.650 (p = 0.017) for nodules, both results having statistical significance. Average sensitivity in the detection of lesions improved from 59.8% to 69.3% for vertebral fractures and from 64.9% to 67.6% for nodules.
CONCLUSION: In the detection of vertebral fractures and lung nodules on chest images, diagnostic accuracy among radiologists improves with the use of CAD.

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Year:  2008        PMID: 18562756     DOI: 10.2214/AJR.07.3091

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


  8 in total

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2.  Deep learning-based detection system for multiclass lesions on chest radiographs: comparison with observer readings.

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5.  Lung cancer differential diagnosis based on the computer assisted radiology: The state of the art.

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Authors:  Marcus A Badgeley; Manway Liu; Benjamin S Glicksberg; Mark Shervey; John Zech; Khader Shameer; Joseph Lehar; Eric K Oermann; Michael V McConnell; Thomas M Snyder; Joel T Dudley
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Review 8.  The Added Effect of Artificial Intelligence on Physicians' Performance in Detecting Thoracic Pathologies on CT and Chest X-ray: A Systematic Review.

Authors:  Dana Li; Lea Marie Pehrson; Carsten Ammitzbøl Lauridsen; Lea Tøttrup; Marco Fraccaro; Desmond Elliott; Hubert Dariusz Zając; Sune Darkner; Jonathan Frederik Carlsen; Michael Bachmann Nielsen
Journal:  Diagnostics (Basel)       Date:  2021-11-26
  8 in total

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