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Computer-aided Diagnosis in Chest Radiography: Results of Large-Scale Observer Tests at the 1996–2001 RSNA Scientific Assemblies¹

¹ From the Kurt Rossmann Laboratories for Radiologic Image Research, Department of Radiology, MC-2026, University of Chicago, 5841 S Maryland Ave, Chicago, IL 60637. Recipient of an Excellence in Design award for an education exhibit at the 2001 RSNA scientific assembly. Received July 19, 2002; revision requested August 22 and received September 12; accepted September 23. Supported by grant CA62625 from the U.S. Public Health Service. Address correspondence to H.A. (e-mail: habe@uchicago.edu).

View larger version (65K): [in a new window]   Figure 1.  Demonstration of the user interface for the nodule detection scheme. A chest radiograph is shown with the CAD results presented as arrows; the arrow in the upper right lung indicates a false-positive finding, whereas the arrow in the lower left lung indicates a true nodule. On the upper right side of the screen, there are control buttons for adjusting the window setting or zooming the image. A "confidence bar" for entering the observer’s confidence level is on the lower right side.

View larger version (35K): [in a new window]   Figure 2.  Average ROC curves for the radiologists in nodule detection without and with CAD. The radiologists’ performance improved significantly (P < .001) with CAD.

View larger version (138K): [in a new window]   Figure 3.  Demonstration of the user interface for the temporal subtraction scheme for detection of interval changes. The interface, which consists of chest radiographs, control buttons, and confidence bars, is similar to that of the nodule detection program. Three chest radiographs are displayed: a current radiograph (upper right), a previous radiograph (upper left), and a temporal subtraction image (lower right). A confidence bar is placed at the bottom of each lung in the current chest image. A newly developed nodule stands out as a black focus (arrow) on the grayish background.

View larger version (37K): [in a new window]   Figure 4.  Average ROC curves for the radiologists in nodule detection without and with temporal subtraction (TS) images. The radiologists’ performance improved significantly (P < .001) with temporal subtraction.

View larger version (122K): [in a new window]   Figure 5.  Demonstration of the user interface for detection of interstitial disease. Using the bar on the right side of the screen, observers indicated their level of confidence as to whether interstitial disease was present in the lungs. A chest radiograph is shown with CAD indicators.

View larger version (33K): [in a new window]   Figure 6.  Average ROC curves for the radiologists in detection of interstitial lung disease without and with CAD. The radiologists’ performance improved significantly (P < .01) with CAD.

View larger version (133K): [in a new window]   Figure 7.  Demonstration of the user interface for differential diagnosis of interstitial disease. Eleven diseases are listed on the upper right side of the screen, next to which the ANN outputs are shown as numerical values and bar graphs. On the lower right side, the chief symptom and clinical parameters are shown. Control buttons are located at the top. EG = eosinophilic granuloma, IPF = idiopathic pulmonary fibrosis, Lym Ca = lymphangitic carcinomatosis, PCP = Pneumocystis carinii pneumonia, RCC = renal cell carcinoma, TBC = tuberculosis.

View larger version (117K): [in a new window]   Figure 8.  Demonstration of the user interface for distinction between benign and malignant pulmonary nodules. In addition to an entire chest radiograph, a nodule is shown within a magnified image on the left side. The CAD result is presented above the magnified nodule as a numerical value. A confidence bar and control buttons are placed below the magnified nodule and above the entire chest radiograph, respectively.

View larger version (36K): [in a new window]   Figure 9.  Average ROC curves for the radiologists in differentiation between benign and malignant pulmonary nodules without and with CAD. The radiologists’ performance improved significantly (P < .001) with CAD.