Zobrazeno 1 - 10
of 60
pro vyhledávání: '"Jørgen Berntsen"'
Autor:
Jacob Theilgaard Lassen, Mikkel Fly Kragh, Jens Rimestad, Martin Nygård Johansen, Jørgen Berntsen
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-9 (2023)
Abstract This work describes the development and validation of a fully automated deep learning model, iDAScore v2.0, for the evaluation of human embryos incubated for 2, 3, and 5 or more days. We trained and evaluated the model on an extensive and di
Externí odkaz:
https://doaj.org/article/70e12fafb43940a3aca04a4f6ca57aa8
Autor:
Keiichi Kato, Satoshi Ueno, Jørgen Berntsen, Motoki Ito, Kiyoe Shimazaki, Kazuo Uchiyama, Tadashi Okimura
Publikováno v:
Reproductive Biology and Endocrinology, Vol 19, Iss 1, Pp 1-11 (2021)
Abstract Background The KIDScore™ Day 5 (KS-D5) model, version 3, is a general morphokinetic prediction model (Vitrolife, Sweden) for fetal heartbeat prediction after embryo transfer that was developed based on a large data set that included implan
Externí odkaz:
https://doaj.org/article/447fde7a67f5444dbf1e661ed8b4d069
Publikováno v:
PLoS ONE, Vol 17, Iss 2, p e0262661 (2022)
Assessing and selecting the most viable embryos for transfer is an essential part of in vitro fertilization (IVF). In recent years, several approaches have been made to improve and automate the procedure using artificial intelligence (AI) and deep le
Externí odkaz:
https://doaj.org/article/43c4764505ea42b7a38f10478e9d7f3b
Autor:
Keiichi Kato, Satoshi Ueno, Jørgen Berntsen, Mikkel Fly Kragh, Tadashi Okimura, Tomoko Kuroda
Publikováno v:
Reproductive BioMedicine Online. 46:274-281
Does embryo categorization by existing artificial intelligence (AI), morphokinetic or morphological embryo selection models correlate with blastocyst euploidy?A total of 834 patients (mean maternal age 40.5 ± 3.4 years) who underwent preimplantation
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0d5a7c6384bbb80367ee5f9363503a6e
https://doi.org/10.1016/j.rbmo.2022.09.010
https://doi.org/10.1016/j.rbmo.2022.09.010
Publikováno v:
Journal of Assisted Reproduction and Genetics. 39:2089-2099
Propose Does an annotation-free embryo scoring system based on deep learning and time-lapse sequence images correlate with live birth (LB) and neonatal outcomes? Methods Patients who underwent SVBT cycles (3010 cycles, mean age: 39.3 ± 4.0). Scores
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2205150a14a1f2bd770d1d37ceb28fc1
https://doi.org/10.1007/s10815-022-02562-5
https://doi.org/10.1007/s10815-022-02562-5
Autor:
Danilo Cimadomo, Viviana Chiappetta, Federica Innocenti, Gaia Saturno, Marilena Taggi, Anabella Marconetto, Valentina Casciani, Laura Albricci, Roberta Maggiulli, Giovanni Coticchio, Aisling Ahlström, Jørgen Berntsen, Mark Larman, Andrea Borini, Alberto Vaiarelli, Filippo Maria Ubaldi, Laura Rienzi
Publikováno v:
Journal of Clinical Medicine
Volume 12
Issue 5
Pages: 1806
Volume 12
Issue 5
Pages: 1806
Preimplantation genetic testing for aneuploidies (PGT-A) is arguably the most effective embryo selection strategy. Nevertheless, it requires greater workload, costs, and expertise. Therefore, a quest towards user-friendly, non-invasive strategies is
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f26d0f0acbc0dacf5d816d6632df02e5
Autor:
Jørgen Berntsen, Salustiano Ribeiro, Molly M. Quinn, Cristina Hickman, Mitchell P. Rosen, Philip Marsh, Rhodel Simbulan
Publikováno v:
Human Reproduction. 37:226-234
STUDY QUESTION Do embryos from sibling oocytes assigned to distinct single-step media culture systems demonstrate differences in early embryo development, morphokinectics or aneuploidy rates? SUMMARY ANSWER Embryo quality, morphokinetic parameters an
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::532f9d6e88ace299b76e49c9c2729ce0
https://doi.org/10.1093/humrep/deab253
https://doi.org/10.1093/humrep/deab253
Autor:
Mikkel Fly Fly Kragh, Jens Rimestad, Jacob Theilgaard Lassen, Martin Johansen, Francesca Bahr, Jørgen Berntsen
Publikováno v:
Fertility and Sterility. 118:e115-e116
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::be15a252a552aecdf18db129b58c9474
https://doi.org/10.1016/j.fertnstert.2022.08.343
https://doi.org/10.1016/j.fertnstert.2022.08.343
Autor:
Jacob Theilgaard Lassen, Mikkel Fly Kragh, Jens Rimestad, Martin Nygård Johansen, Jørgen Berntsen
This work describes the development and validation of a fully automated deep learning model, iDAScore v2.0, for the evaluation of human embryos incubated for 2, 3, and 5 or more days. We trained and evaluated the model on an extensive and diverse dat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e626f15cdd80c6420e24c1b9ac83a459
Publikováno v:
Kragh, M F, Rimestad, J, Lassen, J T, Berntsen, J & Karstoft, H 2022, ' Predicting embryo viability based on self-supervised alignment of time-lapse videos ', IEEE Transactions on Medical Imaging, vol. 41, no. 2, pp. 465-475 . https://doi.org/10.1109/TMI.2021.3116986
With self-supervised learning, both labeled and unlabeled data can be used for representation learning and model pretraining. This is particularly relevant when automating the selection of a patient's fertilized eggs (embryos) during a fertility trea
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b0146de3fc9a84d0cbfaf646db6e8952
https://pubmed.ncbi.nlm.nih.gov/34596537
https://pubmed.ncbi.nlm.nih.gov/34596537