• So Yeon Won, Ilah Shin, Eung Yeop Kim, Seung-Koo Lee, Youngno Yoon, and Beomseok Sohn.
• Department of Radiology and Center for Imaging Sciences, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
• Yonsei Med. J. 2024 Sep 1; 65 (9): 527533527-533.

PurposeThis study aimed to develop and validate a convolutional neural network (CNN) that automatically detects an aberrant right subclavian artery (ARSA) on preoperative computed tomography (CT) for thyroid cancer evaluation.Materials And MethodsA total of 556 CT with ARSA and 312 CT with normal aortic arch from one institution were used as the training set for model development. A deep learning model for the classification of patch images for ARSA was developed using two-dimension CNN from EfficientNet. The diagnostic performance of our model was evaluated using external test sets (112 and 126 CT) from two institutions. The performance of the model was compared with that of radiologists for detecting ARSA using an independent dataset of 1683 consecutive neck CT.ResultsThe performance of the model was achieved using two external datasets with an area under the curve of 0.97 and 0.99, and accuracy of 97% and 99%, respectively. In the temporal validation set, which included a total of 20 patients with ARSA and 1663 patients without ARSA, radiologists overlooked 13 ARSA cases. In contrast, the CNN model successfully detected all the 20 patients with ARSA.ConclusionWe developed a CNN-based deep learning model that detects ARSA using CT. Our model showed high performance in the multicenter validation.© Copyright: Yonsei University College of Medicine 2024.

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