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Wyświetlanie 1-20 z 98
Tytuł:
PENet—a scalable deep-learning model for automated diagnosis of pulmonary embolism using volumetric CT imaging
Autorzy:
Huang, Shih-ChengAff1, Aff2
Kothari, Tanay
Banerjee, ImonAff1, Aff2, Aff4, Aff5
Chute, Chris
Ball, Robyn L.
Borus, Norah
Huang, Andrew
Patel, Bhavik N.
Rajpurkar, Pranav
Irvin, Jeremy
Dunnmon, Jared
Bledsoe, Joseph
Shpanskaya, Katie
Dhaliwal, Abhay
Zamanian, RohamAff8, Aff9
Ng, Andrew Y.
Lungren, Matthew P.Aff1, Aff2, Aff5
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Źródło:
npj Digital Medicine. 3(1)
Czasopismo naukowe
Szczegóły
Tytuł:
Author Correction: PENet—a scalable deep-learning model for automated diagnosis of pulmonary embolism using volumetric CT imaging
Autorzy:
Huang, Shih-ChengAff1, Aff2
Kothari, Tanay
Banerjee, ImonAff1, Aff2, Aff4, Aff5
Chute, Chris
Ball, Robyn L.
Borus, Norah
Huang, Andrew
Patel, Bhavik N.
Rajpurkar, Pranav
Irvin, Jeremy
Dunnmon, Jared
Bledsoe, Joseph
Shpanskaya, Katie
Dhaliwal, Abhay
Zamanian, RohamAff8, Aff9
Ng, Andrew Y.
Lungren, Matthew P.Aff1, Aff2, Aff5
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Źródło:
npj Digital Medicine. 3(1)
Czasopismo naukowe
Szczegóły
Tytuł:
Impact of a deep learning assistant on the histopathologic classification of liver cancer
Autorzy:
Kiani, Amirhossein
Uyumazturk, Bora
Rajpurkar, Pranav
Wang, Alex
Gao, Rebecca
Jones, Erik
Yu, Yifan
Langlotz, Curtis P.Aff3, Aff4
Ball, Robyn L.
Montine, Thomas J.Aff3, Aff5
Martin, Brock A.
Berry, Gerald J.
Ozawa, Michael G.
Hazard, Florette K.
Brown, Ryanne A.
Chen, Simon B.
Wood, Mona
Allard, Libby S.
Ylagan, Lourdes
Ng, Andrew Y.
Shen, JeanneAff3, Aff5
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Źródło:
npj Digital Medicine. 3(1)
Czasopismo naukowe
Szczegóły
Tytuł:
Incorporating machine learning and social determinants of health indicators into prospective risk adjustment for health plan payments.
Autorzy:
Irvin JA; Department of Computer Science, Stanford University, 353 Serra Mall, Stanford, CA, 94305, USA. .
Kondrich AA; Department of Computer Science, Stanford University, 353 Serra Mall, Stanford, CA, 94305, USA.
Ko M; Department of Statistics, Stanford University, Stanford, USA.
Rajpurkar P; Department of Computer Science, Stanford University, 353 Serra Mall, Stanford, CA, 94305, USA.
Haghgoo B; Department of Computer Science, Stanford University, 353 Serra Mall, Stanford, CA, 94305, USA.
Landon BE; Department of Healthcare Policy, Harvard Medical School, Boston, USA.; Center for Primary Care, Harvard Medical School, Boston, USA.
Phillips RL; Center for Professionalism & Value in Health Care, American Board of Family Medicine Foundation, Lexington, USA.
Petterson S; Robert Graham Center, American Academy of Family Physicians, Leawood, USA.
Ng AY; Department of Computer Science, Stanford University, 353 Serra Mall, Stanford, CA, 94305, USA.
Basu S; Center for Primary Care, Harvard Medical School, Boston, USA.; Research and Analytics, Collective Health, San Francisco, USA.; School of Public Health, Imperial College London, London, England.
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Źródło:
BMC public health [BMC Public Health] 2020 May 01; Vol. 20 (1), pp. 608. Date of Electronic Publication: 2020 May 01.
Typ publikacji:
Journal Article
MeSH Terms:
Health Promotion/*economics
Health Promotion/*statistics & numerical data
Insurance, Health/*economics
Insurance, Health/*statistics & numerical data
Machine Learning/*economics
Machine Learning/*statistics & numerical data
Social Determinants of Health/*economics
Social Determinants of Health/*statistics & numerical data
Adult ; Cost-Benefit Analysis ; Female ; Humans ; Male ; Middle Aged ; Prospective Studies ; Risk Adjustment
Czasopismo naukowe
Pełny tekst  Link otwiera się w nowym oknie Pełny tekstSzczegóły
Tytuł:
AppendiXNet: Deep Learning for Diagnosis of Appendicitis from A Small Dataset of CT Exams Using Video Pretraining.
Autorzy:
Rajpurkar P; Stanford University Department of Computer Science, Stanford, USA.
Park A; Stanford University Department of Computer Science, Stanford, USA.
Irvin J; Stanford University Department of Computer Science, Stanford, USA.
Chute C; Stanford University Department of Computer Science, Stanford, USA.
Bereket M; Stanford University Department of Computer Science, Stanford, USA.
Mastrodicasa D; Stanford University Department of Radiology, Stanford, USA.
Langlotz CP; Stanford University AIMI Center, Stanford, USA.
Lungren MP; Stanford University AIMI Center, Stanford, USA.
Ng AY; Stanford University Department of Computer Science, Stanford, USA.
Patel BN; Stanford University Department of Radiology, Stanford, USA. .; Stanford University AIMI Center, Stanford, USA. .
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Źródło:
Scientific reports [Sci Rep] 2020 Mar 03; Vol. 10 (1), pp. 3958. Date of Electronic Publication: 2020 Mar 03.
Typ publikacji:
Journal Article; Research Support, Non-U.S. Gov't
MeSH Terms:
Algorithms*
Deep Learning*
Appendicitis/*diagnosis
Appendicitis/*metabolism
Adult ; Cross-Sectional Studies ; Female ; Humans ; Male ; Middle Aged
Czasopismo naukowe
Pełny tekst  Link otwiera się w nowym oknie Pełny tekstSzczegóły
Tytuł:
Deep Learning–Assisted Diagnosis of Cerebral Aneurysms Using the HeadXNet Model.
Autorzy:
Park, Allison
Chute, Chris
Rajpurkar, Pranav
Lou, Joe
Ball, Robyn L.
Shpanskaya, Katie
Jabarkheel, Rashad
Kim, Lily H.
McKenna, Emily
Tseng, Joe
Ni, Jason
Wishah, Fidaa
Wittber, Fred
Hong, David S.
Wilson, Thomas J.
Halabi, Safwan
Basu, Sanjay
Patel, Bhavik N.
Lungren, Matthew P.
Ng, Andrew Y.
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Źródło:
JAMA Network Open; 6/7/2019, Vol. 2 Issue 6, pe195600-e195600, 1p
Czasopismo naukowe
Szczegóły
Tytuł:
Deep-learning-assisted diagnosis for knee magnetic resonance imaging: Development and retrospective validation of MRNet.
Autorzy:
Bien N; Department of Computer Science, Stanford University, Stanford, California, United States of America.
Rajpurkar P; Department of Computer Science, Stanford University, Stanford, California, United States of America.
Ball RL; Quantitative Sciences Unit, Department of Medicine, Stanford University, Stanford, California, United States of America.
Irvin J; Department of Computer Science, Stanford University, Stanford, California, United States of America.
Park A; Department of Computer Science, Stanford University, Stanford, California, United States of America.
Jones E; Department of Computer Science, Stanford University, Stanford, California, United States of America.
Bereket M; Department of Computer Science, Stanford University, Stanford, California, United States of America.
Patel BN; Department of Radiology, Stanford University, Stanford, California, United States of America.
Yeom KW; Department of Radiology, Stanford University, Stanford, California, United States of America.
Shpanskaya K; Department of Radiology, Stanford University, Stanford, California, United States of America.
Halabi S; Department of Radiology, Stanford University, Stanford, California, United States of America.
Zucker E; Department of Radiology, Stanford University, Stanford, California, United States of America.
Fanton G; Department of Orthopedic Surgery, Stanford University, Stanford, California, United States of America.
Amanatullah DF; Department of Orthopedic Surgery, Stanford University, Stanford, California, United States of America.
Beaulieu CF; Department of Radiology, Stanford University, Stanford, California, United States of America.
Riley GM; Department of Radiology, Stanford University, Stanford, California, United States of America.
Stewart RJ; Department of Radiology, Stanford University, Stanford, California, United States of America.
Blankenberg FG; Department of Radiology, Stanford University, Stanford, California, United States of America.
Larson DB; Department of Radiology, Stanford University, Stanford, California, United States of America.
Jones RH; Department of Radiology, Stanford University, Stanford, California, United States of America.
Langlotz CP; Department of Radiology, Stanford University, Stanford, California, United States of America.
Ng AY; Department of Computer Science, Stanford University, Stanford, California, United States of America.
Lungren MP; Department of Radiology, Stanford University, Stanford, California, United States of America.
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Źródło:
PLoS medicine [PLoS Med] 2018 Nov 27; Vol. 15 (11), pp. e1002699. Date of Electronic Publication: 2018 Nov 27 (Print Publication: 2018).
Typ publikacji:
Journal Article; Validation Study
MeSH Terms:
Deep Learning*
Anterior Cruciate Ligament Injuries/*diagnostic imaging
Diagnosis, Computer-Assisted/*methods
Image Interpretation, Computer-Assisted/*methods
Knee/*diagnostic imaging
Magnetic Resonance Imaging/*methods
Tibial Meniscus Injuries/*diagnostic imaging
Adult ; Automation ; Databases, Factual ; Female ; Humans ; Male ; Middle Aged ; Predictive Value of Tests ; Reproducibility of Results ; Retrospective Studies ; Young Adult
Czasopismo naukowe
Pełny tekst  Link otwiera się w nowym oknie Pełny tekstSzczegóły
Tytuł:
Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists.
Autorzy:
Rajpurkar P; Department of Computer Science, Stanford University, Stanford, California, United States of America.
Irvin J; Department of Computer Science, Stanford University, Stanford, California, United States of America.
Ball RL; Department of Medicine, Quantitative Sciences Unit, Stanford University, Stanford, California, United States of America.
Zhu K; Department of Computer Science, Stanford University, Stanford, California, United States of America.
Yang B; Department of Computer Science, Stanford University, Stanford, California, United States of America.
Mehta H; Department of Computer Science, Stanford University, Stanford, California, United States of America.
Duan T; Department of Computer Science, Stanford University, Stanford, California, United States of America.
Ding D; Department of Computer Science, Stanford University, Stanford, California, United States of America.
Bagul A; Department of Computer Science, Stanford University, Stanford, California, United States of America.
Langlotz CP; Department of Radiology, Stanford University, Stanford, California, United States of America.
Patel BN; Department of Radiology, Stanford University, Stanford, California, United States of America.
Yeom KW; Department of Radiology, Stanford University, Stanford, California, United States of America.
Shpanskaya K; Department of Radiology, Stanford University, Stanford, California, United States of America.
Blankenberg FG; Department of Radiology, Stanford University, Stanford, California, United States of America.
Seekins J; Department of Radiology, Stanford University, Stanford, California, United States of America.
Amrhein TJ; Department of Radiology, Duke University, Durham, North Carolina, United States of America.
Mong DA; Department of Radiology, University of Colorado, Denver, Colorado, United States of America.
Halabi SS; Department of Radiology, Stanford University, Stanford, California, United States of America.
Zucker EJ; Department of Radiology, Stanford University, Stanford, California, United States of America.
Ng AY; Department of Computer Science, Stanford University, Stanford, California, United States of America.
Lungren MP; Department of Radiology, Stanford University, Stanford, California, United States of America.
Pokaż więcej
Źródło:
PLoS medicine [PLoS Med] 2018 Nov 20; Vol. 15 (11), pp. e1002686. Date of Electronic Publication: 2018 Nov 20 (Print Publication: 2018).
Typ publikacji:
Comparative Study; Journal Article; Research Support, Non-U.S. Gov't; Validation Study
MeSH Terms:
Clinical Competence*
Deep Learning*
Radiologists*
Diagnosis, Computer-Assisted/*methods
Pneumonia/*diagnostic imaging
Radiographic Image Interpretation, Computer-Assisted/*methods
Radiography, Thoracic/*methods
Humans ; Predictive Value of Tests ; Reproducibility of Results ; Retrospective Studies
Czasopismo naukowe
Pełny tekst  Link otwiera się w nowym oknie Pełny tekstSzczegóły
Wyświetlanie 1-20 z 98

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