Ten years of in vitro fertilization in Indonesia: Access to infertility care in a developing country.

OBJECTIVE: This research was conducted to assess access to assisted reproductive technologies (ART) and the current status of the in vitro fertilization (IVF) program that have been implemented in Indonesia over the last 10 years. METHODS: We established a retrospective cohort study and descriptive analysis of the current state of access to infertility care in Indonesia. The data were collected from all IVF centers, clinics, and hospitals in Indonesia from 2011 to 2020, including the number of IVF clinics, total ART cycles, retrieved fresh and frozen embryos, average age of IVF patients, IVF pregnancy rate, and causes of infertility. RESULTS: The number of reported fertility clinics in Indonesia has increased from 14 clinics in 2011 to 41 clinics by 2020. As many as 69 569 ART cycles were conducted over the past 10 years, of which 51 892 cycles used fresh embryos and 17 677 cycles used frozen embryos. The leading cause of consecutive infertility diagnosis was male infertility. Nearly half of the women who underwent IVF procedures (48.9%) were under 35 years old. The pregnancy rate outcome of women who underwent IVF ranged from 24.6% to 37.3%. CONCLUSION: Developments in ART in Indonesia have led to improvements in the ART cycles performed throughout the 10 year period. The identification of key areas that require improvement can provide an opportunity to enhance access to infertility care.

Embryo ploidy status classification through computer-assisted morphology assessment.

BACKGROUND: Preimplantation genetic testing for aneuploidy has been proven to be effective in determining the embryo's chromosomal or ploidy status. The test requires a biopsy of embryonic cells on day 3, 5, or 6 from which complete information on the chromosomes would be obtained. The main drawbacks of preimplantation genetic testing for aneuploidy include its relatively invasive approach and the lack of research studies on the long-term effects of preimplantation genetic testing for aneuploidy. OBJECTIVE: Computer-assisted predictive modeling through machine learning and deep learning algorithms has been proposed to minimize the use of invasive preimplantation genetic testing for aneuploidy. The capability to predict morphologic characteristics of embryo ploidy status creates a meaningful support system for decision-making before further treatment. STUDY DESIGN: Image processing is a component in developing a predictive model specialized in image classification through which a model is able to differentiate images based on unique features. Image processing is obtained through image augmentation to capture segmented embryos and perform feature extraction. Furthermore, multiple machine learning and deep learning algorithms were used to create prediction-based modeling, and all of the prediction models undergo similar model performance assessments to determine the best model prediction algorithm. RESULTS: An efficient artificial intelligence model that can predict embryo ploidy status was developed using image processing through a histogram of oriented gradient and then followed by principal component analysis. The gradient boosting algorithm showed an advantage against other algorithms and yielded an accuracy of 0.74, an aneuploid precision of 0.83, and an aneuploid predictive value (recall) of 0.84. CONCLUSION: This research study proved that machine-assisted technology perceives the embryo differently than human observation and determined that further research on in vitro fertilization is needed. The study finding serves as a basis for developing a better computer-assisted prediction model.