Looking into the future: a machine learning powered prediction model for oocyte return rates after cryopreservation
Fouks Y, Bortoletto P, Chang J, Penzias A, Vaughan D, Sakkas D. Looking into the future: a machine learning powered prediction model for oocyte return rates after cryopreservation. Reproductive BioMedicine Online 2024, 50: 104432. PMID: 39642702, DOI: 10.1016/j.rbmo.2024.104432.Peer-Reviewed Original ResearchSociety for Assisted Reproductive TechnologyDiminished ovarian reserveStored oocytesUS fertility clinicsAge-related infertilityAssisted Reproductive TechnologyFertility clinicsLikelihood of patientsOocyte cryopreservationUnexplained infertilityInfertility diagnosisOvarian reservePatient agePatient demographicsTreatment indicationsPatient counselingReproductive technologyFertility diagnosisPatientsOocytesGender-relatedInfertilitySelf-financingClinicDiagnosisUtilization of Cryopreserved Oocytes in Patients With Poor Ovarian Response After Planned Oocyte Cryopreservation
Fouks Y, Sakkas D, Bortoletto P, Penzias A, Seidler E, Vaughan D. Utilization of Cryopreserved Oocytes in Patients With Poor Ovarian Response After Planned Oocyte Cryopreservation. JAMA Network Open 2024, 7: e2349722. PMID: 38165675, PMCID: PMC10762568, DOI: 10.1001/jamanetworkopen.2023.49722.Peer-Reviewed Original ResearchConceptsPoor ovarian responseOvarian responseOocyte cryopreservationCryopreserved oocytesCohort studyNormal ovarian responseNormal responder groupOvarian stimulation cyclesHistory of endometriosisBody mass indexNumber of patientsNumber of oocytesTiming of patientsNormal respondersPOR groupEligible patientsUS fertility clinicsMass indexResponder groupStimulation cyclesHigher oddsMAIN OUTCOMEAge 35Patient's desirePatients
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