RESUMO
PURPOSE: The purpose of this study was to evaluate the performance of a deep learning algorithm in detecting abnormalities of thyroid cartilage from computed tomography (CT) examination. MATERIALS AND METHODS: A database of 515 harmonized thyroid CT examinations was used, of which information regarding cartilage abnormality was provided for 326. The process consisted of determining image abnormality and, from these preprocessed images, finding the best learning algorithm to appropriately characterize thyroid cartilage as normal or abnormal. CT images were cropped to be centered around the cartilage in order to focus on the relevant area. New images were generated from the originals by applying simple transformations in order to augment the database. Characterizations of cartilage abnormalities were made using transfer learning, by using the architecture of a pre-trained neural network called VGG16 and adapting the final layers to a binary classification problem. RESULTS: The best algorithm yielded an area under the receiving operator characteristic curve (AUC) of 0.72 on a sample of 82 thyroid test images. The sensitivity and specificity of the abnormality detection were 83% and 64% at the best threshold, respectively. Applying the model on another independent sample of 189 new thyroid images resulted in an AUC of 0.70. CONCLUSION: This study demonstrates the feasibility of using a deep learning-based abnormality detection system to evaluate thyroid cartilage from CT examinations. However, although promising results, the model is not yet able to match an expert's diagnosis.
