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Updated: Jan 24, 2026

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
Published on: April 21, 2023
Using Artificial Intelligence to Revise ACR TI-RADS Risk Stratification of Thyroid Nodules: Diagnostic Accuracy and
Benjamin Wildman-Tobriner1, Mateusz Buda1, Jenny K Hoang1
1From the Department of Radiology, Duke University Hospital, 2301 Erwin Rd, Durham, NC 27701 (B.W.T., M.B., J.K.H., R.G.S., M.A.M.); Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, Mo (W.D.M., D.T.); and Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala (F.N.T.).
Artificial intelligence (AI) optimized the American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS), improving specificity for thyroid nodule risk stratification. This AI TI-RADS simplifies categorization and maintains high diagnostic performance, reducing unnecessary biopsies.
Failed At:
2026-06-19T13:38:16.548448+00:00
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