Development and Multicenter, Multiprotocol Validation of Neural Network for Aberrant Right Subclavian Artery Detection.

Autor:	Won SY; Department of Radiology and Center for Imaging Sciences, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.; Department of Radiology, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Korea., Shin I; Department of Radiology, Kyung Hee University Hospital, Kyung Hee University School of Medicine, Seoul, Korea.; Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea., Kim EY; Department of Radiology and Center for Imaging Sciences, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea., Lee SK; Department of Radiology, Yonsei University College of Medicine, Seoul, Korea., Yoon Y; Bright Data LLC, Yongin, Korea. brightdata21@gmail.com., Sohn B; Department of Radiology and Center for Imaging Sciences, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.; Department of Radiology, Yonsei University College of Medicine, Seoul, Korea. beomseoksohn@gmail.com.
Jazyk:	angličtina
Zdroj:	Yonsei medical journal [Yonsei Med J] 2024 Sep; Vol. 65 (9), pp. 527-533.
DOI:	10.3349/ymj.2023.0590
Abstrakt:	Purpose: This 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 Methods: A 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. Results: The 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. Conclusion: We developed a CNN-based deep learning model that detects ARSA using CT. Our model showed high performance in the multicenter validation. Competing Interests: The authors have no potential conflicts of interest to disclose. (© Copyright: Yonsei University College of Medicine 2024.)
Databáze:	MEDLINE
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