Improved prediction of clinical pregnancy using artificial intelligence with enhanced inner cell mass and trophectoderm images.

Autor: Kim HM; Kai Health, Seoul, South Korea., Ko T; Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul, South Korea.; Department of Biomedicine & Health Sciences, College of Medicine, The Catholic University of Korea, Seoul, South Korea.; CMC Institute for Basic Medical Science, The Catholic Medical Center of The Catholic University of Korea, Seoul, South Korea., Kang H; Kai Health, Seoul, South Korea., Choi S; M Fertility Clinic, Seoul, South Korea., Park JH; Miraewaheemang Hospital, IVF Clinic, Seoul, South Korea., Chung MK; Seoul Rachel Fertility Center, IVF Clinic, Seoul, South Korea., Kim M; Department of Obstetrics & Gynecology, Ajou University School of Medicine, Suwon, South Korea., Kim NY; HI Fertility Center, Seoul, South Korea., Lee HJ; Kai Health, Seoul, South Korea. hyejunlee@gmail.com.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2024 Feb 08; Vol. 14 (1), pp. 3240. Date of Electronic Publication: 2024 Feb 08.
DOI: 10.1038/s41598-024-52241-x
Abstrakt: This study aimed to assess the performance of an artificial intelligence (AI) model for predicting clinical pregnancy using enhanced inner cell mass (ICM) and trophectoderm (TE) images. In this retrospective study, we included static images of 2555 day-5-blastocysts from seven in vitro fertilization centers in South Korea. The main outcome of the study was the predictive capability of the model to detect clinical pregnancies (gestational sac). Compared with the original embryo images, the use of enhanced ICM and TE images improved the average area under the receiver operating characteristic curve for the AI model from 0.716 to 0.741. Additionally, a gradient-weighted class activation mapping analysis demonstrated that the enhanced image-trained AI model was able to extract features from crucial areas of the embryo in 99% (506/512) of the cases. Particularly, it could extract the ICM and TE. In contrast, the AI model trained on the original images focused on the main areas in only 86% (438/512) of the cases. Our results highlight the potential efficacy of using ICM- and TE-enhanced embryo images when training AI models to predict clinical pregnancy.
(© 2024. The Author(s).)
Databáze: MEDLINE
Externí odkaz:
Nepřihlášeným uživatelům se plný text nezobrazuje