The predictive value of MUSA features of adenomyosis on live birth is poor, using a machine learning algorithm
(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
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/b3218e21-64d1-4f77-9863-c1bd01e37f0d
- author
- Alson, Sara LU ; Björnsson, Ola LU ; Hansson, Stefan LU ; Henic, Emir LU and Sladkevicius, Povilas LU
- organization
- publishing date
- 2024-09-09
- 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}}, }