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1 Department of Radiology and Radiation Oncology, Division of Radiological
Science, Nagasaki University Graduate School of Biomedical Sciences, 1-7-1,
Sakamoto, Nagasaki 852-8501, Japan.
2 Department of Radiation Epidemiology, Atomic Bomb Disease Institute, Nagasaki
University School of Medicine, 1-12-4 Sakamoto, Nagasaki 852-8501,
Japan.
3 General Research Center, Nippon Bunri University, Ichiki 1727, Oita 870-0397,
Japan.
4 Department of Radiology, Kurt Rossmann Laboratories for Radiologic Image
Research, The University of Chicago, 5841 S Maryland Ave., Chicago, IL
60637.
OBJECTIVE. The purpose of our study was to evaluate the diagnostic performance of an artificial neural network (ANN) in differentiating among certain diffuse lung diseases using high-resolution CT (HRCT) and the effect of ANN output on radiologists' diagnostic performance.
MATERIALS AND METHODS. We selected 130 clinical cases of diffuse lung disease. We used a single three-layer, feed-forward ANN with a back-propagation algorithm. The ANN was designed to differentiate among 11 diffuse lung diseases by using 10 clinical parameters and 23 HRCT features. Therefore, the ANN consisted of 33 input units and 11 output units. Subjective ratings for 23 HRCT features were provided independently by eight radiologists. All clinical cases were used for training and testing of the ANN by implementing a round-robin technique. In the observer test, a subset of 45 cases was selected from the database of 130 cases. HRCT images were viewed by eight radiologists first without and then with ANN output. The radiologists' performance was evaluated with receiver operating characteristic (ROC) analysis with a continuous rating scale.
RESULTS. The average area under the ROC curve for ANN performance obtained with all clinical parameters and HRCT features was 0.956. The diagnostic performance of four chest radiologists and four general radiologists was increased from 0.986 to 0.992 (p = 0.071) and 0.958 and 0.971 (p < 0.001), respectively, when they used the ANN output based on their own feature ratings.
CONCLUSION. The ANN can provide a useful output as a second opinion to improve general radiologists' diagnostic performance in the differential diagnosis of certain diffuse lung diseases using HRCT.
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