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A proof of concept for a deep learning system that can aid embryologists in predicting blastocyst survival after thaw.
Marsh, P; Radif, D; Rajpurkar, P; Wang, Z; Hariton, E; Ribeiro, S; Simbulan, R; Kaing, A; Lin, W; Rajah, A; Rabara, F; Lungren, M; Demirci, U; Ng, A; Rosen, M.
Affiliation
  • Marsh P; Center for Reproductive Health, Department of Medicine, University of California, San Francisco, USA.
  • Radif D; Department of Computer Science, Stanford University, Stanford, USA.
  • Rajpurkar P; Department of Computer Science, Stanford University, Stanford, USA.
  • Wang Z; Department of Computer Science, Stanford University, Stanford, USA.
  • Hariton E; Center for Reproductive Health, Department of Medicine, University of California, San Francisco, USA. hariton.md@gmail.com.
  • Ribeiro S; Center for Reproductive Health, Department of Medicine, University of California, San Francisco, USA.
  • Simbulan R; Center for Reproductive Health, Department of Medicine, University of California, San Francisco, USA.
  • Kaing A; Center for Reproductive Health, Department of Medicine, University of California, San Francisco, USA.
  • Lin W; Center for Reproductive Health, Department of Medicine, University of California, San Francisco, USA.
  • Rajah A; Center for Reproductive Health, Department of Medicine, University of California, San Francisco, USA.
  • Rabara F; Center for Reproductive Health, Department of Medicine, University of California, San Francisco, USA.
  • Lungren M; Center for Artificial Intelligence in Medicine & Imaging, Stanford University, Stanford, USA.
  • Demirci U; Canary Center for Cancer Early Detection, Stanford University, Stanford, USA.
  • Ng A; Department of Computer Science, Stanford University, Stanford, USA.
  • Rosen M; Center for Reproductive Health, Department of Medicine, University of California, San Francisco, USA.
Sci Rep ; 12(1): 21119, 2022 12 07.
Article in En | MEDLINE | ID: mdl-36477633
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Deep Learning Type of study: Prognostic_studies / Risk_factors_studies Language: En Journal: Sci Rep Year: 2022 Document type: Article Affiliation country: United States Country of publication: United kingdom
Fulltext
Add to My VHL
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Deep Learning Type of study: Prognostic_studies / Risk_factors_studies Language: En Journal: Sci Rep Year: 2022 Document type: Article Affiliation country: United States Country of publication: United kingdom