Machine learning in time-lapse imaging to differentiate embryos from young vs old mice
Yang L, Leynes C, Pawelka A, Lorenzo I, Chou A, Lee B, Heaney J. Machine learning in time-lapse imaging to differentiate embryos from young vs old mice. Biology Of Reproduction 2024, 110: 1115-1124. PMID: 38685607, PMCID: PMC11180621, DOI: 10.1093/biolre/ioae056.Peer-Reviewed Original ResearchMaternal agePreimplantation genetic testingMorphokinetic parameters of embryosEarly embryo developmentMaternal miceNo significant differenceEmbryo transferTime-lapse microscopyGenetic testingAge-related phenotypesHuman embryosYoung donorsSignificant differenceNon-invasive approachEmbryo developmentCleavage stagesYounger counterpartsMiceNon-invasive technologyEmbryosAged embryosPhenotypeAgeTime-lapse imagingMorphokineticsDevelopment of a dynamic machine learning algorithm to predict clinical pregnancy and live birth rate with embryo morphokinetics
Yang L, Peavey M, Kaskar K, Chappell N, Zhu L, Devlin D, Valdes C, Schutt A, Woodard T, Zarutskie P, Cochran R, Gibbons W. Development of a dynamic machine learning algorithm to predict clinical pregnancy and live birth rate with embryo morphokinetics. F&S Reports 2022, 3: 116-123. PMID: 35789724, PMCID: PMC9250114, DOI: 10.1016/j.xfre.2022.04.004.Peer-Reviewed Original ResearchClinical pregnancy ratePregnancy rateClinical pregnancyAcademic fertility clinicRetrospective cohort analysisLive birth rateTertiary hospital settingLive birth outcomesPositive predictive valueBirth outcomesCohort analysisHospital settingFertility clinicsPredictive valueSecondary analysisPregnancyEmbryo morphokineticsBirth rateMorphokineticsTime-lapse microscopySimilar resultsPatientsClinic