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A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
Published on: April 21, 2023
Generalizability and Diagnostic Performance of AI Models for Thyroid US
WenWen Xu1, XiaoHong Jia1, ZiHan Mei1
1From the Department of Ultrasound, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, 197 Ruijin Er Road, 200025, Shanghai, China (W.W.X., X.H.J., Z.H.M., W.W.Z., T.L., H.T.Z., Y.J.D., J.Q.Z.); Department of Scientific Research, Shanghai Aitrox Technology Corporation Limited, Shanghai, China (X.L.G., Y.L., C.C.F., K.Y.Z., Q.F., C.H.); Department of Ultrasound, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China (R.F.Z.); Department of Medical Ultrasound, Affiliated Hospital of Guizhou Medical University, Guiyang, China (Y.G., X.C.); Department of Medical Ultrasound, Yunnan Cancer Hospital & The Third Affiliated Hospital of Kunming Medical University, Kunming, China (X.M.L.); Department of Ultrasound, Yunnan Kungang Hospital, The Seventh Affiliated Hospital of Dali University, Anning, China (N.L.); Department of Ultrasound, Affiliated Hospital of Yan'an University, Yan'an, China (B.Y.B.); Department of Ultrasound, Tangdu Hospital, Fourth Military Medical University, Xi'an, China (Q.Y.L.); Department of Ultrasound, Shanxi Provincial People's Hospital, Taiyuan, China (J.P.Y.); Department of Ultrasound, Traditional Chinese Medical Hospital of Xinjiang Uygur Autonomous Region, Urumqi, Xinjiang Uygur Autonomous Region, China (H.Z.); Department of Ultrasound, Gansu Provincial Cancer Hospital, Lanzhou, China (L.G.); Department of Ultrasound, Jilin Central General Hospital, Jilin, China (B.G.); and College of Health Science and Technology, Shanghai Jiaotong University School of Medicine, Shanghai, China (J.Q.Z.).
Abstract:
Background Artificial intelligence (AI) models have improved US assessment of thyroid nodules; however, the lack of generalizability limits the application of these models. Purpose To develop AI models for segmentation and classification of thyroid nodules in US using diverse data sets from nationwide hospitals and multiple vendors, and to measure the impact of the AI models on diagnostic performance. Materials and Methods This retrospective study included consecutive patients with pathologically confirmed thyroid nodules who underwent US using equipment from 12 vendors at 208 hospitals across China from November 2017 to January 2019. The detection, segmentation, and classification models were developed based on the subset or complete set of images. Model performance was evaluated by precision and recall, Dice coefficient, and area under the receiver operating characteristic curve (AUC) analyses. Three scenarios (diagnosis without AI assistance, with freestyle AI assistance, and with rule-based AI assistance) were compared with three senior and three junior radiologists to optimize incorporation of AI into clinical practice. Results A total of 10 023 patients (median age, 46 years [IQR 37-55 years]; 7669 female) were included. The detection, segmentation, and classification models had an average precision, Dice coefficient, and AUC of 0.98 (95% CI: 0.96, 0.99), 0.86 (95% CI: 0.86, 0.87), and 0.90 (95% CI: 0.88, 0.92), respectively. The segmentation model trained on the nationwide data and classification model trained on the mixed vendor data exhibited the best performance, with a Dice coefficient of 0.91 (95% CI: 0.90, 0.91) and AUC of 0.98 (95% CI: 0.97, 1.00), respectively. The AI model outperformed all senior and junior radiologists (P < .05 for all comparisons), and the diagnostic accuracies of all radiologists were improved (P < .05 for all comparisons) with rule-based AI assistance. Conclusion Thyroid US AI models developed from diverse data sets had high diagnostic performance among the Chinese population. Rule-based AI assistance improved the performance of radiologists in thyroid cancer diagnosis. © RSNA, 2023 Supplemental material is available for this article.
