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Incorporation Of A Machine Learning Algorithm With Object Detection Within The Thyroid Imaging Reporting And Data System Improves The Diagnosis Of Genetic Risk., Shuo Wang, Jiajun Xu, Aylin Tahmasebi, Kelly Daniels, Ji-Bin Liu, Joseph Curry, Elizabeth Cottrill, Andrej Lyshchik, John R Eisenbrey
Incorporation Of A Machine Learning Algorithm With Object Detection Within The Thyroid Imaging Reporting And Data System Improves The Diagnosis Of Genetic Risk., Shuo Wang, Jiajun Xu, Aylin Tahmasebi, Kelly Daniels, Ji-Bin Liu, Joseph Curry, Elizabeth Cottrill, Andrej Lyshchik, John R Eisenbrey
Department of Radiology Faculty Papers
Background: The role of next generation sequencing (NGS) for identifying high risk mutations in thyroid nodules following fine needle aspiration (FNA) biopsy continues to grow. However, ultrasound diagnosis even using the American College of Radiology's Thyroid Imaging Reporting and Data System (TI-RADS) has limited ability to stratify genetic risk. The purpose of this study was to incorporate an artificial intelligence (AI) algorithm of thyroid ultrasound with object detection within the TI-RADS scoring system to improve prediction of genetic risk in these nodules.
Methods: Two hundred fifty-two nodules from 249 patients that underwent ultrasound imaging and ultrasound-guided FNA with NGS with …