Predicting pregnancy rate following multiple embryo transfers using algorithms developed through static image analysis.
Reprod Biomed Online
; 34(5): 473-479, 2017 May.
Article
en En
| MEDLINE
| ID: mdl-28236600
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.
Palabras clave
Texto completo:
1
Bases de datos:
MEDLINE
Asunto principal:
Algoritmos
/
Índice de Embarazo
/
Transferencia de Embrión
Tipo de estudio:
Prognostic_studies
/
Risk_factors_studies
Límite:
Adult
/
Female
/
Humans
/
Pregnancy
Idioma:
En
Revista:
Reprod Biomed Online
Asunto de la revista:
MEDICINA REPRODUTIVA
Año:
2017
Tipo del documento:
Article