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在RSNA 2022年椎骨折检测挑战赛中获胜算法的性能

Ghee Rye Lee1, Adam E Flanders1, Tyler Richards1

  • 1From the Department of Radiology, Ohio State University Wexner Medical Center, 395 W 12th Ave, Columbus, OH 43210 (G.R.L., L.M.P.); Department of Radiology, Thomas Jefferson University, Philadelphia, Pa (A.E.F.); Department of Radiology, University of Utah School of Medicine, Salt Lake City, Utah (T.R.); Dasalnova, Diagnósticos da América, São Paulo, Brazil (F.K.); Department of Diagnostic Imaging, Universidade Federal de São Paulo, São Paulo, Brazil (F.K.); Department of Medical Imaging, Unity Health Toronto, University of Toronto, Toronto, Canada (E.C., H.M.L.); The Jackson Laboratory, Bar Harbor, Maine (R.L.B.); and Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, Calif (J.T.).

Radiology. Artificial intelligence
|January 3, 2024
PubMed
概括

人工智能算法在CT扫描上检测椎骨折 (CSFx) 的高性能,超过了以前的模型. 需要进一步的研究来评估它们的临床适用性.

关键词:
人工智能算法的人工智能算法这就是为什么CTCTCTCTCTCT宫脊柱的部 脊柱的部断裂检测检测器可以检测到断裂.头部 / 部 / 部机器学习 机器学习

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