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.
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.
Authors: K Yamashita; T Yoshiura; H Arimura; F Mihara; T Noguchi; A Hiwatashi; O Togao; Y Yamashita; T Shono; S Kumazawa; Y Higashida; H Honda Journal: AJNR Am J Neuroradiol Date: 2008-04-03 Impact factor: 3.825
Authors: Andreas Christe; Alan A Peters; Dionysios Drakopoulos; Johannes T Heverhagen; Thomas Geiser; Thomai Stathopoulou; Stergios Christodoulidis; Marios Anthimopoulos; Stavroula G Mougiakakou; Lukas Ebner Journal: Invest Radiol Date: 2019-10 Impact factor: 6.016