Optimizing oocyte yield: unveiling the ideal follicle sizes on the day of trigger using interpretable machine learning

S Hanassab; A  Abbara; Tom Kelsey; A Yeung; Artsiom Hramyka; T Alhamwi; R Salim; A Conmimos; G Trew; S Nelson; T Heinis; W Dhillo

doi:10.1016/j.fertnstert.2023.08.132

Optimizing oocyte yield: unveiling the ideal follicle sizes on the day of trigger using interpretable machine learning

S Hanassab, A Abbara, Tom Kelsey, A Yeung, Artsiom Hramyka, T Alhamwi, R Salim, A Conmimos, G Trew, S Nelson, T Heinis, W Dhillo

Research output: Contribution to journal › Abstract › peer-review

Original language	English
Pages (from-to)	e34-e35
Number of pages	2
Journal	Fertility and Sterility
Volume	120
Issue number	4 Supplement
Early online date	6 Oct 2023
DOIs	https://doi.org/10.1016/j.fertnstert.2023.08.132
Publication status	Published - 16 Oct 2023

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10.1016/j.fertnstert.2023.08.132

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Hanassab, S., Abbara, A., Kelsey, T., Yeung, A., Hramyka, A., Alhamwi, T., Salim, R., Conmimos, A., Trew, G., Nelson, S., Heinis, T., & Dhillo, W. (2023). Optimizing oocyte yield: unveiling the ideal follicle sizes on the day of trigger using interpretable machine learning. Fertility and Sterility, 120(4 Supplement), e34-e35. https://doi.org/10.1016/j.fertnstert.2023.08.132

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title = "Optimizing oocyte yield: unveiling the ideal follicle sizes on the day of trigger using interpretable machine learning",

author = "S Hanassab and A Abbara and Tom Kelsey and A Yeung and Artsiom Hramyka and T Alhamwi and R Salim and A Conmimos and G Trew and S Nelson and T Heinis and W Dhillo",

note = "Funding: The Department of Metabolism, Digestion, and Reproduction at Imperial College London is funded by grants from the MRC and NIHR. S.H. is supported by the UKRI CDT in AI for Healthcare http://ai4health. io (EP/S023283/1). A.A. is supported by an NIHR Clinician Scientist Award (CS-2018-18-ST2-002). W.S.D. is supported by an NIHR Senior Investigator Award (NIHR202371).",

year = "2023",

month = oct,

day = "16",

doi = "10.1016/j.fertnstert.2023.08.132",

language = "English",

volume = "120",

pages = "e34--e35",

journal = "Fertility and Sterility",

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Hanassab, S, Abbara, A, Kelsey, T, Yeung, A, Hramyka, A, Alhamwi, T, Salim, R, Conmimos, A, Trew, G, Nelson, S, Heinis, T & Dhillo, W 2023, 'Optimizing oocyte yield: unveiling the ideal follicle sizes on the day of trigger using interpretable machine learning', Fertility and Sterility, vol. 120, no. 4 Supplement, pp. e34-e35. https://doi.org/10.1016/j.fertnstert.2023.08.132

TY - JOUR

T1 - Optimizing oocyte yield

T2 - unveiling the ideal follicle sizes on the day of trigger using interpretable machine learning

AU - Hanassab, S

AU - Abbara, A

AU - Kelsey, Tom

AU - Yeung, A

AU - Hramyka, Artsiom

AU - Alhamwi, T

AU - Salim, R

AU - Conmimos, A

AU - Trew, G

AU - Nelson, S

AU - Heinis, T

AU - Dhillo, W

N1 - Funding: The Department of Metabolism, Digestion, and Reproduction at Imperial College London is funded by grants from the MRC and NIHR. S.H. is supported by the UKRI CDT in AI for Healthcare http://ai4health. io (EP/S023283/1). A.A. is supported by an NIHR Clinician Scientist Award (CS-2018-18-ST2-002). W.S.D. is supported by an NIHR Senior Investigator Award (NIHR202371).

PY - 2023/10/16

Y1 - 2023/10/16

U2 - 10.1016/j.fertnstert.2023.08.132

DO - 10.1016/j.fertnstert.2023.08.132

M3 - Abstract

SN - 0015-0282

VL - 120

SP - e34-e35

JO - Fertility and Sterility

JF - Fertility and Sterility

IS - 4 Supplement

ER -