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Congress: ECR25
Poster Number: C-20821
Type: Poster: EPOS Radiologist (scientific)
Authorblock: A. Rabinowich1, N. Avisdris1, B. Specktor-Fadida2, J. Herzlich1, L. Joskowicz3, K. Krajden Haratz31, L. Hiersch1, L. Ben-Sira1, D. Ben Bashat1; 1Tel Aviv/IL, 2Haifa/IL, 3Jerusalem/IL
Disclosures:
Aviad Rabinowich: Nothing to disclose
Netanell Avisdris: Nothing to disclose
Bella Specktor-Fadida: Nothing to disclose
Jacky Herzlich: Nothing to disclose
Leo Joskowicz: Nothing to disclose
Karina Krajden Haratz3: Nothing to disclose
Liran Hiersch: Nothing to disclose
Liat Ben-Sira: Nothing to disclose
Dafna Ben Bashat: Nothing to disclose
Keywords: Foetal imaging, MR, Neural networks, Ultrasound, Intrauterine diagnosis, Outcomes analysis, Foetus, Outcomes
References

1. Nohuz E, Rivière O, Coste K, Vendittelli F. Prenatal identification of small-for-gestational age and risk of neonatal  morbidity and stillbirth. Ultrasound Obstet Gynecol. England; 2020;55(5):621–628.

2. Chauhan SP, Rice MM, Grobman WA, et al. Neonatal Morbidity of Small- and Large-for-Gestational-Age Neonates Born at Term in  Uncomplicated Pregnancies. Obstetrics and gynecology. 2017;130(3):511–519.

3. Martinez-Portilla RJ, Caradeux J, Meler E, Lip-Sosa DL, Sotiriadis A, Figueras F. Third-trimester uterine artery Doppler for prediction of adverse outcome in late small-for-gestational-age fetuses: systematic review and meta-analysis. Ultrasound Obstet Gynecol. England; 2020;55(5):575–585.

4. Schreiber V, Hurst C, da Silva Costa F, Stoke R, Turner J, Kumar S. Definitions matter: detection rates and perinatal outcome for infants classified  prenatally as having late fetal growth restriction using SMFM biometric vs ISUOG/Delphi consensus criteria. Ultrasound Obstet Gynecol. England; 2023;61(3):377–385.

5. Specktor-Fadida B, Link-Sourani D, Rabinowich A, et al. Deep learning-based segmentation of whole-body fetal MRI and fetal weight  estimation: assessing performance, repeatability, and reproducibility. Eur Radiol. Germany; 2023.

6. Zvi O Ben, Avisdris N, Yehuda B, et al. Automatic segmentation of fetal brain components from MRI using deep learning. ISMRM 2021.

7. Avisdris N, Rabinowich A, Fridkin D, et al. Automatic fetal fat quantification from MRI. International Workshop on Preterm, Perinatal and Paediatric Image Analysis. Springer, Cham; 2022. p. 25–37.

8. Specktor-Fadida, B., Link-Sourani, D., Ferster-Kveller, S., Ben-Sira, L., Miller, E., Ben-Bashat, D., & Joskowicz L. A Bootstrap Self-training Method for Sequence Transfer: State-of-the-Art Placenta Segmentation in fetal MRI. Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Perinatal Imaging, Placental and Preterm Image Analysis. Springer, Cham; 2021.

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