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Current Applications and Future Impact of Machine Learning in Radiology.
Choy, Garry; Khalilzadeh, Omid; Michalski, Mark; Do, Synho; Samir, Anthony E; Pianykh, Oleg S; Geis, J Raymond; Pandharipande, Pari V; Brink, James A; Dreyer, Keith J.
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  • Choy G; From the Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, Boston, Mass 02114 (G.C., O.K., M.M., S.D., A.E.S., O.S.P., P.V.P., J.A.B., K.J.D.); and Department of Radiology, University of Colorado School of Medicine, Aurora, Colo (J.R.G.).
  • Khalilzadeh O; From the Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, Boston, Mass 02114 (G.C., O.K., M.M., S.D., A.E.S., O.S.P., P.V.P., J.A.B., K.J.D.); and Department of Radiology, University of Colorado School of Medicine, Aurora, Colo (J.R.G.).
  • Michalski M; From the Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, Boston, Mass 02114 (G.C., O.K., M.M., S.D., A.E.S., O.S.P., P.V.P., J.A.B., K.J.D.); and Department of Radiology, University of Colorado School of Medicine, Aurora, Colo (J.R.G.).
  • Do S; From the Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, Boston, Mass 02114 (G.C., O.K., M.M., S.D., A.E.S., O.S.P., P.V.P., J.A.B., K.J.D.); and Department of Radiology, University of Colorado School of Medicine, Aurora, Colo (J.R.G.).
  • Samir AE; From the Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, Boston, Mass 02114 (G.C., O.K., M.M., S.D., A.E.S., O.S.P., P.V.P., J.A.B., K.J.D.); and Department of Radiology, University of Colorado School of Medicine, Aurora, Colo (J.R.G.).
  • Pianykh OS; From the Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, Boston, Mass 02114 (G.C., O.K., M.M., S.D., A.E.S., O.S.P., P.V.P., J.A.B., K.J.D.); and Department of Radiology, University of Colorado School of Medicine, Aurora, Colo (J.R.G.).
  • Geis JR; From the Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, Boston, Mass 02114 (G.C., O.K., M.M., S.D., A.E.S., O.S.P., P.V.P., J.A.B., K.J.D.); and Department of Radiology, University of Colorado School of Medicine, Aurora, Colo (J.R.G.).
  • Pandharipande PV; From the Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, Boston, Mass 02114 (G.C., O.K., M.M., S.D., A.E.S., O.S.P., P.V.P., J.A.B., K.J.D.); and Department of Radiology, University of Colorado School of Medicine, Aurora, Colo (J.R.G.).
  • Brink JA; From the Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, Boston, Mass 02114 (G.C., O.K., M.M., S.D., A.E.S., O.S.P., P.V.P., J.A.B., K.J.D.); and Department of Radiology, University of Colorado School of Medicine, Aurora, Colo (J.R.G.).
  • Dreyer KJ; From the Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, Boston, Mass 02114 (G.C., O.K., M.M., S.D., A.E.S., O.S.P., P.V.P., J.A.B., K.J.D.); and Department of Radiology, University of Colorado School of Medicine, Aurora, Colo (J.R.G.).
Radiology ; 288(2): 318-328, 2018 Aug.
Article en En | MEDLINE | ID: mdl-29944078
ABSTRACT
Recent advances and future perspectives of machine learning techniques offer promising applications in medical imaging. Machine learning has the potential to improve different steps of the radiology workflow including order scheduling and triage, clinical decision support systems, detection and interpretation of findings, postprocessing and dose estimation, examination quality control, and radiology reporting. In this article, the authors review examples of current applications of machine learning and artificial intelligence techniques in diagnostic radiology. In addition, the future impact and natural extension of these techniques in radiology practice are discussed.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Radiología / Sistemas de Información Radiológica / Aprendizaje Automático Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Radiology Año: 2018 Tipo del documento: Article
Texto completo
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Radiología / Sistemas de Información Radiológica / Aprendizaje Automático Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Radiology Año: 2018 Tipo del documento: Article