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Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
Published on: February 23, 2024
David K Eng1, Nishith B Khandwala1, Jin Long1
1From the Department of Computer Science, Stanford University, 300 N Pasteur Dr, Stanford, CA 94305 (D.K.E., N.B.K.); Departments of Pediatrics (J.L.) and Radiology (D.B.L., J.M.S., C.P.L., M.P.L., S.S.H.), Stanford University School of Medicine, Stanford, Calif; Department of Radiology, New York University School of Medicine, New York, NY (N.R.F., S.V.L., N.A.S., M.E.B.); Department of Radiology, Emory School of Medicine and Children's Healthcare of Atlanta, Atlanta, Ga (S.S.M.); Department of Radiology, MedStar Health and Georgetown University School of Medicine, Washington, DC (R.W.F., A.R.Z.); Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio (S.E.S., A.J.T., C.G.A.); Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, Pa (M.L.F., S.L.K., R.D.); Department of Radiology, Harvard Medical School and Boston Children's Hospital, Boston, Mass (K.E., S.P.P.); Department of Radiology, Yale School of Medicine, New Haven, Conn (B.J.D., C.T.S.); and Department of Radiology, Kansas University School of Medicine, Kansas City, Kan (B.M.E.).
Artificial intelligence (AI) algorithms improved skeletal age assessment accuracy and reduced interpretation times for radiologists in clinical practice. This AI diagnostic aid demonstrated better accuracy and efficiency compared to traditional methods.
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