These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

184 related articles for article (PubMed ID: 32228880)

  • 1. Consistency and objectivity of automated embryo assessments using deep neural networks.
    Bormann CL; Thirumalaraju P; Kanakasabapathy MK; Kandula H; Souter I; Dimitriadis I; Gupta R; Pooniwala R; Shafiee H
    Fertil Steril; 2020 Apr; 113(4):781-787.e1. PubMed ID: 32228880
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Embryologist agreement when assessing blastocyst implantation probability: is data-driven prediction the solution to embryo assessment subjectivity?
    Fordham DE; Rosentraub D; Polsky AL; Aviram T; Wolf Y; Perl O; Devir A; Rosentraub S; Silver DH; Gold Zamir Y; Bronstein AM; Lara Lara M; Ben Nagi J; Alvarez A; Munné S
    Hum Reprod; 2022 Sep; 37(10):2275-2290. PubMed ID: 35944167
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Performance of a deep learning based neural network in the selection of human blastocysts for implantation.
    Bormann CL; Kanakasabapathy MK; Thirumalaraju P; Gupta R; Pooniwala R; Kandula H; Hariton E; Souter I; Dimitriadis I; Ramirez LB; Curchoe CL; Swain J; Boehnlein LM; Shafiee H
    Elife; 2020 Sep; 9():. PubMed ID: 32930094
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Development of an artificial intelligence-based assessment model for prediction of embryo viability using static images captured by optical light microscopy during IVF.
    VerMilyea M; Hall JMM; Diakiw SM; Johnston A; Nguyen T; Perugini D; Miller A; Picou A; Murphy AP; Perugini M
    Hum Reprod; 2020 Apr; 35(4):770-784. PubMed ID: 32240301
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Should we freeze it? Agreement on fate of borderline blastocysts is poor and does not improve with a modified blastocyst grading system.
    Hammond ER; Foong AKM; Rosli N; Morbeck DE
    Hum Reprod; 2020 May; 35(5):1045-1053. PubMed ID: 32358601
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Inter-observer and intra-observer agreement between embryologists during selection of a single Day 5 embryo for transfer: a multicenter study.
    Storr A; Venetis CA; Cooke S; Kilani S; Ledger W
    Hum Reprod; 2017 Feb; 32(2):307-314. PubMed ID: 28031323
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Embryo ranking agreement between embryologists and artificial intelligence algorithms.
    Zaninovic N; Sierra JT; Malmsten JE; Rosenwaks Z
    F S Sci; 2024 Feb; 5(1):50-57. PubMed ID: 37820865
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Improving embryo selection using a computer-automated time-lapse image analysis test plus day 3 morphology: results from a prospective multicenter trial.
    Conaghan J; Chen AA; Willman SP; Ivani K; Chenette PE; Boostanfar R; Baker VL; Adamson GD; Abusief ME; Gvakharia M; Loewke KE; Shen S
    Fertil Steril; 2013 Aug; 100(2):412-9.e5. PubMed ID: 23721712
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Automatic grading of human blastocysts from time-lapse imaging.
    Kragh MF; Rimestad J; Berntsen J; Karstoft H
    Comput Biol Med; 2019 Dec; 115():103494. PubMed ID: 31630027
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Automatic characterization of human embryos at day 4 post-insemination from time-lapse imaging using supervised contrastive learning and inductive transfer learning techniques.
    Payá E; Bori L; Colomer A; Meseguer M; Naranjo V
    Comput Methods Programs Biomed; 2022 Jun; 221():106895. PubMed ID: 35609359
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Interobserver and intraobserver variation in day 3 embryo grading.
    Baxter Bendus AE; Mayer JF; Shipley SK; Catherino WH
    Fertil Steril; 2006 Dec; 86(6):1608-15. PubMed ID: 17074349
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Using the Eeva Test™ adjunctively to traditional day 3 morphology is informative for consistent embryo assessment within a panel of embryologists with diverse experience.
    Diamond MP; Suraj V; Behnke EJ; Yang X; Angle MJ; Lambe-Steinmiller JC; Watterson R; Athayde Wirka K; Chen AA; Shen S
    J Assist Reprod Genet; 2015 Jan; 32(1):61-8. PubMed ID: 25331427
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Deep learning as a predictive tool for fetal heart pregnancy following time-lapse incubation and blastocyst transfer.
    Tran D; Cooke S; Illingworth PJ; Gardner DK
    Hum Reprod; 2019 Jun; 34(6):1011-1018. PubMed ID: 31111884
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Development of an artificial intelligence model for predicting the likelihood of human embryo euploidy based on blastocyst images from multiple imaging systems during IVF.
    Diakiw SM; Hall JMM; VerMilyea MD; Amin J; Aizpurua J; Giardini L; Briones YG; Lim AYX; Dakka MA; Nguyen TV; Perugini D; Perugini M
    Hum Reprod; 2022 Jul; 37(8):1746-1759. PubMed ID: 35674312
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Deep learning enables robust assessment and selection of human blastocysts after in vitro fertilization.
    Khosravi P; Kazemi E; Zhan Q; Malmsten JE; Toschi M; Zisimopoulos P; Sigaras A; Lavery S; Cooper LAD; Hickman C; Meseguer M; Rosenwaks Z; Elemento O; Zaninovic N; Hajirasouliha I
    NPJ Digit Med; 2019; 2():21. PubMed ID: 31304368
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Development and evaluation of inexpensive automated deep learning-based imaging systems for embryology.
    Kanakasabapathy MK; Thirumalaraju P; Bormann CL; Kandula H; Dimitriadis I; Souter I; Yogesh V; Kota Sai Pavan S; Yarravarapu D; Gupta R; Pooniwala R; Shafiee H
    Lab Chip; 2019 Dec; 19(24):4139-4145. PubMed ID: 31755505
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Should there be an "AI" in TEAM? Embryologists selection of high implantation potential embryos improves with the aid of an artificial intelligence algorithm.
    Fitz VW; Kanakasabapathy MK; Thirumalaraju P; Kandula H; Ramirez LB; Boehnlein L; Swain JE; Curchoe CL; James K; Dimitriadis I; Souter I; Bormann CL; Shafiee H
    J Assist Reprod Genet; 2021 Oct; 38(10):2663-2670. PubMed ID: 34535847
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Time-lapse imaging of preimplantation embryos.
    Conaghan J
    Semin Reprod Med; 2014 Mar; 32(2):134-40. PubMed ID: 24515908
    [TBL] [Abstract][Full Text] [Related]  

  • 19. A hybrid artificial intelligence model leverages multi-centric clinical data to improve fetal heart rate pregnancy prediction across time-lapse systems.
    Duval A; Nogueira D; Dissler N; Maskani Filali M; Delestro Matos F; Chansel-Debordeaux L; Ferrer-Buitrago M; Ferrer E; Antequera V; Ruiz-Jorro M; Papaxanthos A; Ouchchane H; Keppi B; Prima PY; Regnier-Vigouroux G; Trebesses L; Geoffroy-Siraudin C; Zaragoza S; Scalici E; Sanguinet P; Cassagnard N; Ozanon C; De La Fuente A; Gómez E; Gervoise Boyer M; Boyer P; Ricciarelli E; Pollet-Villard X; Boussommier-Calleja A
    Hum Reprod; 2023 Apr; 38(4):596-608. PubMed ID: 36763673
    [TBL] [Abstract][Full Text] [Related]  

  • 20. How should we choose the 'best' embryo? A commentary on behalf of the British Fertility Society and the Association of Clinical Embryologists.
    Bolton VN; Leary C; Harbottle S; Cutting R; Harper JC
    Hum Fertil (Camb); 2015 Sep; 18(3):156-64. PubMed ID: 26313607
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 10.