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• W2885642017 abstract "Background Convolutional neural networks (CNNs) are advanced artificial intelligence algorithms well suited to image classification tasks with variable features. These have been used to great effect in various real‐world applications including handwriting recognition, face detection, image search, and fraud prevention. We sought to retrain a robust CNN with coronal computed tomography (CT) images to classify osteomeatal complex (OMC) occlusion and assess the performance of this technology with rhinologic data. Methods The Google Inception‐V3 CNN trained with 1.28 million images was used as the base model. Preoperative coronal sections through the OMC were obtained from 239 patients enrolled in 2 prospective chronic rhinosinusitis (CRS) outcomes studies, labeled according to OMC status, and mirrored to obtain a set of 956 images. Using this data, the classification layer of Inception‐V3 was retrained in Python using a transfer learning method to adapt the CNN to the task of interpreting sinonasal CT images. Results The retrained neural network achieved 85% classification accuracy for OMC occlusion, with a 95% confidence interval for algorithm accuracy of 78% to 92%. Receiver operating characteristic (ROC) curve analysis on the test set confirmed good classification ability of the CNN with an area under the ROC curve (AUC) of 0.87, significantly different than both random guessing and a dominant classifier that predicts the most common class ( p < 0.0001). Conclusion Current state‐of‐the‐art CNNs may be able to learn clinically relevant information from 2‐dimensional sinonasal CT images with minimal supervision. Future work will extend this approach to 3‐dimensional images in order to further refine this technology for possible clinical applications." @default.
• W2885642017 created "2018-08-22" @default.
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• W2885642017 date "2018-08-11" @default.
• W2885642017 modified "2023-10-16" @default.
• W2885642017 title "Automated classification of osteomeatal complex inflammation on computed tomography using convolutional neural networks" @default.
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• W2885642017 doi "https://doi.org/10.1002/alr.22196" @default.
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