Effect of a computer-aided diagnosis system on clinicians' performance in detection of small acute intracranial hemorrhage on computed tomography.

Rationale and Objectives: To analyze the effect of a computer-aided diagnosis (CAD) system on clinicians' performance in detection of small acute intracranial hemorrhage (AIH) on computed tomography (CT). Materials and Methods: The authors have developed a CAD scheme that used both image processing...

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Published in:Academic Radiology Vol. 15; no. 3; pp. 290 - 300
Main Authors: Chan T, Huang HK, Chan, Tao, Huang, H K
Format: research Journal Article
Published: Elsevier B.V. Mar2008
Online Access:View this record in EBSCOhost
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      dt: Mar2008
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      pub: Elsevier B.V.
      place: New York, New York
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        atl: Effect of a computer-aided diagnosis system on clinicians' performance in detection of small acute intracranial hemorrhage on computed tomography.
      aug:
        au:
          Chan T
          Huang HK
          Chan, Tao
          Huang, H K
        affil: Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Hong Kong
      sug:
        subj:
          Clinical Competence
          Diagnosis, Computer Assisted
          Intracranial Hemorrhage Radiography
          Tomography, X-Ray Computed Methods
          Acute Disease
          Algorithms
          Emergency Medicine
          Image Interpretation, Computer Assisted
          Image Processing, Computer Assisted Methods
          Internship and Residency
          Knowledge Bases
          Observer Bias
          Pharmacokinetics
          ROC Curve
          Sensitivity and Specificity
          Specialties, Medical Education
          Human
      ab: Rationale and Objectives: To analyze the effect of a computer-aided diagnosis (CAD) system on clinicians' performance in detection of small acute intracranial hemorrhage (AIH) on computed tomography (CT). Materials and Methods: The authors have developed a CAD scheme that used both image processing techniques and anatomic knowledge based classification system to improve diagnosis of small AIH on CT. A multiple-reader, multiple-case receiver operating characteristic (ROC) study was performed. Twenty clinicians, including seven emergency physicians, seven radiology residents, and six radiology specialists were recruited as readers of 60 sets of brain CT, including 30 cases that show AIH smaller than 1 cm, and 30 controls. Each reader read the same 60 cases twice, first without, then with the prompts produced by the CAD system. The clinicians ranked their confidence in diagnosing a case of showing AIH, which produced the ROC curves. Results: Significantly improved performance is observed in emergency physicians, average area under the ROC curve (Az) increased from 0.8422 to 0.9294 (P = .0107) when they make the diagnosis without and with the support of CAD. Az for radiology residents increased from 0.9371 to 0.9762 (P = .0088). Az for radiology specialists increased from 0.9742 to 0.9868, but was statistically insignificant (P = .1755). Conclusions: CAD can improve the clinicians' performance in detecting AIH on CT. In particular, emergency physicians can benefit most from the CAD and improve their performance to a level approaching that of the average radiology residents.
      pubtype: Academic Journal
      doctype:
        research
        Journal Article
      ougenre: Article
    language: English
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