Improved diagnosis of breast implant rupture with sonographic findings and artificial neural networks.
Academic Article
Overview
abstract
RATIONALE AND OBJECTIVES: The authors evaluated the use of sonographic findings combined with artificial neural networks as an aid to the diagnosis of breast implant rupture. MATERIALS AND METHODS: From a database of 78 breast implants that were evaluated prospectively with sonography and then surgically removed, sonographic findings and surgical results were used to train and test backpropagation and radial basis function artificial neural networks by using the leave-one-out method. Receiver operating characteristic (ROC) curve analysis was used to compare the performance of the different neural networks with that of the radiologists involved. RESULTS: By using the ROC area index as a measure of performance, the artificial neural network (Az = 0.8744) outperformed the radiologists (Az = 0.8057), although not by a statistically significant difference (P = .09). The best-performing network used, in addition to the sonographic findings, the diagnosis of the radiologist as an input. This network (Az = 0.9245) outperformed both the radiologists and the "unaided" networks by a statistically significant margin (P = .02 for radiologists, P = .04 for the unaided network). The network performed remarkably well in those cases in which the radiologists classified the implant as indeterminate, predicting the correct diagnosis in 23 of 25 cases (92%). CONCLUSION: The results suggest that artificial neural networks in tandem with the unaided radiologic diagnosis can improve the accuracy rate in the detection of implant rupture based on sonographic findings. This "team" approach provided the best results.
