title: Predicting pregnancy rate following multiple embryo transfers using algorithms developed through static image analysis.
abstract: Single-embryo image assessment involves a high degree of inaccuracy because of the imprecise labelling of the transferred embryo images. In this study, we considered the entire transfer cycle to predict the implantation potential of embryos, and propose a novel algorithm based on a combination of local binary pattern texture feature and Adaboost classifiers to predict pregnancy rate. The first step of the proposed method was to extract the features of the embryo images using the local binary pattern operator. After this, multiple embryo images in a transfer cycle were considered as one entity, and the pregnancy rate was predicted using three classifiers: the Real Adaboost, Gentle Adaboost, and Modest Adaboost. Finally, the pregnancy rate was determined via the majority vote rule based on classification results of the three Adaboost classifiers. The proposed algorithm was verified to have a good predictive performance and may assist the embryologist and clinician to select embryos to transfer and in turn improve pregnancy rate.
authors.affiliation: College of Information Science and Technology, Beijing Normal University, Beijing 100875, China. Electronic address: tianyun@bnu.edu.cn.
authors.forename: Yun
authors.initials: Y
authors.lastname: Tian
authors.affiliation: Peking University Cancer Hospital and Institute, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Beijing, 100142, China.
authors.forename: Wei
authors.initials: W
authors.lastname: Wang
authors.affiliation: College of Information Science and Technology, Beijing Normal University, Beijing 100875, China.
authors.forename: Yabo
authors.initials: Y
authors.lastname: Yin
authors.affiliation: Department of Obstetrics and Gynecology, Navy General Hospital, Beijing 100048, China.
authors.forename: Weizhou
authors.initials: W
authors.lastname: Wang
authors.affiliation: College of Information Science and Technology, Beijing Normal University, Beijing 100875, China.
authors.forename: Fuqing
authors.initials: F
authors.lastname: Duan
authors.affiliation: College of Information Science and Technology, Beijing Normal University, Beijing 100875, China.
authors.forename: Shifeng
authors.initials: S
authors.lastname: Zhao
mesh_terms: D000328:Adult; D000465:Algorithms; D004624:Embryo Transfer; D005260:Female; D006801:Humans; D011247:Pregnancy; D018873:Pregnancy Rate
keywords: IVF–ET; embryo image; potential prediction; transfer cycles