Application of an artificial neural network to high-resolution CT: usefulness in differential diagnosis of diffuse lung disease.
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.


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.


CONCLUSIONS
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.
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.
Aya
A
Fukushima
Kazuto
K
Ashizawa
Tetsuji
T
Yamaguchi
Naohiro
N
Matsuyama
Hideyuki
H
Hayashi
Isao
I
Kida
Yoshihiro
Y
Imafuku
Akiko
A
Egawa
Seigo
S
Kimura
Kenji
K
Nagaoki
Sumihisa
S
Honda
Shigehiko
S
Katsuragawa
Kunio
K
Doi
Kuniaki
K
Hayashi
D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D003937:Diagnosis, Differential; D005260:Female; D006801:Humans; D008171:Lung Diseases / Q000000981:diagnostic imaging* / Q000503:physiopathology; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D012372:ROC Curve; D015203:Reproducibility of Results; D012720:Severity of Illness Index; D014057:Tomography, X-Ray Computed / Q000379:methods*