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Application of an Artificial Neural Network to High-Resolution CT: Usefulness in Differential Diagnosis of Diffuse Lung Disease

Aya Fukushima1, Kazuto Ashizawa1, Tetsuji Yamaguchi1, Naohiro Matsuyama1, Hideyuki Hayashi1, Isao Kida1, Yoshihiro Imafuku1, Akiko Egawa1, Seigo Kimura1, Kenji Nagaoki1, Sumihisa Honda2, Shigehiko Katsuragawa3, Kunio Doi4 and Kuniaki Hayashi1

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.



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  		Fig. 1. —Diagram shows basic structure of artificial neural network (ANN). Although only 13 hidden units are shown for illustration, ANN actually consists of 22 hidden units. HRCT = high-resolution CT, BOOP = bronchiolitis obliterans with organizing pneumonia, CMV = cytomegalovirus.

 


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  		Fig. 2. —CT images of 22-year-old man with sarcoidosis. These images can serve as examples of images used in this study. Two radiologists' ratings for this case are shown in Table 2.

 


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  		Fig. 3A. —Artificial neural network (ANN) output obtained by two radiologists' ratings of features for case shown in Figure 2. NSIP = nonspecific interstitial pneumonia, UIP = usual interstitial pneumonia, BOOP = bronchiolitis obliterans with organizing pneumonia, CEP = chronic eosinophilic pneumonia, ca = carcinomatosis, Tb. = tuberculosis, DPB = diffuse panbronchiolitis, PAP = pulmonary alveolar proteinosis, LAM = lymphangiomyomatosis, PCP = Pneumocystis carinii pneumonia, CMV = cytomegalovirus. Graphs show largest output values among 11 diseases correspond to correct diagnosis for observer B, a chest radiologist (A), Chest Imaging and observer G, a general radiologist (B).

 


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  		Fig. 3B. —Artificial neural network (ANN) output obtained by two radiologists' ratings of features for case shown in Figure 2. NSIP = nonspecific interstitial pneumonia, UIP = usual interstitial pneumonia, BOOP = bronchiolitis obliterans with organizing pneumonia, CEP = chronic eosinophilic pneumonia, ca = carcinomatosis, Tb. = tuberculosis, DPB = diffuse panbronchiolitis, PAP = pulmonary alveolar proteinosis, LAM = lymphangiomyomatosis, PCP = Pneumocystis carinii pneumonia, CMV = cytomegalovirus. Graphs show largest output values among 11 diseases correspond to correct diagnosis for observer B, a chest radiologist (A), Chest Imaging and observer G, a general radiologist (B).

 


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  		Fig. 4. —Graph illustrates range (black line) and average (white bar) of area under receiver operating characteristic curve (Az) values of artificial neural network (ANN) performance by eight radiologists for each disease. NSIP = nonspecific interstitial pneumonia, UIP = usual interstitial pneumonia, BOOP = bronchiolitis obliterans with organizing pneumonia, CEP = chronic eosinophilic pneumonia, ca. = carcinomatosis, Tb. = tuberculosis, DPB = diffuse panbronchiolitis, PAP = pulmonary alveolar proteinosis, LAM = lymphangiomyomatosis, PCP = Pneumocystis carinii pneumonia, CMV = cytomegalovirus.

 


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  		Fig. 5A. —Average areas under receiver operating characteristic curves (Az) for observers with and without artificial neural network (ANN) output. Graph shows Az values for chest radiologists.

 


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  		Fig. 5B. —Average areas under receiver operating characteristic curves (Az) for observers with and without artificial neural network (ANN) output. Graph shows Az values for general radiologists. Note that observer performance with artificial neural network (ANN) output improved significantly.

 


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  		Fig. 6. —Graph shows number of correctly diagnosed cases for which observers' rankings changed because of artificial neural network (ANN) output. Black bars indicate number of cases in which ANN output was beneficial; white bars, number of cases ANN in which output was detrimental. ANN output clearly improved performance of observers.

 

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