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The predictive value of MUSA features of adenomyosis on live birth is poor, using a machine learning algorithm

Alson, Sara LU orcid ; Björnsson, Ola LU ; Hansson, Stefan LU orcid ; Henic, Emir LU and Sladkevicius, Povilas LU orcid (2024) ISUOG 34th World Congress on Ultrasound in Obstetrics and Gynecology - Budapest In Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology 64(S1). p.88-89
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author
; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
morphological uterus sonographic assessment group (MUSA), Artificial intelligence, Machine Learning (ML), IVF / ICSI, live birth rate, Prediction model, XGBoost model, artificial reproductive treatment
in
Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology
volume
64
issue
S1
article number
OP11.06
pages
88 - 89
publisher
John Wiley & Sons Inc.
conference name
ISUOG 34th World Congress on Ultrasound in Obstetrics and Gynecology - Budapest
conference location
Budapest, Hungary
conference dates
2024-09-15 - 2024-09-18
external identifiers
  • scopus:85203730346
ISSN
1469-0705
DOI
10.1002/uog.27964
language
English
LU publication?
yes
additional info
Supplement: Abstracts of the 34th World Congress on Ultrasound in Obstetrics and Gynecology, 15–18 September 2024, Budapest, Hungary
id
b3218e21-64d1-4f77-9863-c1bd01e37f0d
date added to LUP
2024-10-01 12:54:01
date last changed
2024-10-02 08:29:16
@misc{b3218e21-64d1-4f77-9863-c1bd01e37f0d,
  author       = {{Alson, Sara and Björnsson, Ola and Hansson, Stefan and Henic, Emir and Sladkevicius, Povilas}},
  issn         = {{1469-0705}},
  keywords     = {{morphological uterus sonographic assessment group (MUSA); Artificial intelligence; Machine Learning (ML); IVF / ICSI; live birth rate; Prediction model; XGBoost model; artificial reproductive treatment}},
  language     = {{eng}},
  month        = {{09}},
  note         = {{Conference Abstract}},
  number       = {{S1}},
  pages        = {{88--89}},
  publisher    = {{John Wiley & Sons Inc.}},
  series       = {{Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology}},
  title        = {{The predictive value of MUSA features of adenomyosis on live birth is poor, using a machine learning algorithm}},
  url          = {{http://dx.doi.org/10.1002/uog.27964}},
  doi          = {{10.1002/uog.27964}},
  volume       = {{64}},
  year         = {{2024}},
}