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8. Deep learning mediated single time-point image-based prediction of embryo developmental outcome at the cleavage stage

Author

Kanakasabapathy, Manoj Kumar, Thirumalaraju, Prudhvi, Bormann, Charles L, Gupta, Raghav, Pooniwala, Rohan, Kandula, Hemanth, Souter, Irene, Dimitriadis, Irene, and Shafiee, Hadi

Subjects
Quantitative Biology - Tissues and Organs, Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

In conventional clinical in-vitro fertilization practices embryos are transferred either at the cleavage or blastocyst stages of development. Cleavage stage transfers, particularly, are beneficial for patients with relatively poor prognosis and at fertility centers in resource-limited settings where there is a higher chance of developmental failure in embryos in-vitro. However, one of the major limitations of embryo selections at the cleavage stage is the availability of very low number of manually discernable features to predict developmental outcomes. Although, time-lapse imaging systems have been proposed as possible solutions, they are cost-prohibitive and require bulky and expensive hardware, and labor-intensive. Advances in convolutional neural networks (CNNs) have been utilized to provide accurate classifications across many medical and non-medical object categories. Here, we report an automated system for classification and selection of human embryos at the cleavage stage using a trained CNN combined with a genetic algorithm. The system selected the cleavage stage embryo at 70 hours post insemination (hpi) that ultimately developed into top-quality blastocyst at 70 hpi with 64% accuracy, outperforming the abilities of embryologists in identifying embryos with the highest developmental potential. Such systems can have a significant impact on IVF procedures by empowering embryologists for accurate and consistent embryo assessment in both resource-poor and resource-rich settings.

Published
2020

9. Evaluation of deep convolutional neural networks in classifying human embryo images based on their morphological quality

Author

Thirumalaraju, Prudhvi, Kanakasabapathy, Manoj Kumar, Bormann, Charles L, Gupta, Raghav, Pooniwala, Rohan, Kandula, Hemanth, Souter, Irene, Dimitriadis, Irene, and Shafiee, Hadi

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

A critical factor that influences the success of an in-vitro fertilization (IVF) procedure is the quality of the transferred embryo. Embryo morphology assessments, conventionally performed through manual microscopic analysis suffer from disparities in practice, selection criteria, and subjectivity due to the experience of the embryologist. Convolutional neural networks (CNNs) are powerful, promising algorithms with significant potential for accurate classifications across many object categories. Network architectures and hyper-parameters affect the efficiency of CNNs for any given task. Here, we evaluate multi-layered CNNs developed from scratch and popular deep-learning architectures such as Inception v3, ResNET, Inception-ResNET-v2, and Xception in differentiating between embryos based on their morphological quality at 113 hours post insemination (hpi). Xception performed the best in differentiating between the embryos based on their morphological quality.

Published
2020

26. Antioxidant Intake and Ovarian Reserve in Women Attending a Fertility Center.

Author

Maldonado-Cárceles, Ana B., Souter, Irene, Li, Ming-Chieh, Mitsunami, Makiko, Dimitriadis, Irene, Ford, Jennifer B., Mínguez-Alarcón, Lidia, and Chavarro, Jorge E.

Abstract

Background/Objectives: The aim of this study was to investigate the association between antioxidant intake and antral follicle count (AFC), a marker of ovarian reserve, in women attending a fertility clinic. Methods: We conducted an observational study with 567 women undergoing infertility evaluation at the Massachusetts General Hospital Fertility Center, who were enrolled in the Environment and Reproductive Health (EARTH) study. Participants filled out the lifestyle and health questionnaires and a validated food frequency questionnaire (FFQ) for assessing habitual dietary intake and underwent a transvaginal ultrasound to measure AFC. Intake of nutrients with direct antioxidant capacity (vitamin A, C, and E and carotenoids) and intake of antioxidant food sources were estimated from the FFQ. Adjusted Poisson regression models were fitted to assess the relationships between antioxidants and AFC while adjusting for potential confounders. Non-linearity was assessed with restricted cubic splines. Results: The median (interquartile range) age and AFC of participants were 35.0 (32.0–38.0) years and 13 (9–18), respectively. Our findings revealed a non-linear association between lycopene intake and AFC. There was a positive linear association with the highest AFC among women consuming approximately 6000 mcg/day of lycopene (p for non-linearity = 0.003). An inverse association was observed between retinol intake, predominantly from dairy foods, and AFC among women aged under 35 years (p-trend < 0.001 and 0.01, respectively). Conclusions: Our findings suggest that lycopene intake might influence the ovarian reserve in fertility patients. The observed inverse association with retinol, if confirmed, may reflect biological mechanisms different from oxidative stress. The underlying mechanisms of these associations remain to be elucidated and warrant further investigation. [ABSTRACT FROM AUTHOR]

Published
2025
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27. Association of Glycemic Index, Glycemic Load, and Carbohydrate Intake with Antral Follicle Counts Among Subfertile Females.

Author

Mitsunami, Makiko, Kazemi, Maryam, Nichols, Amy R., Mínguez-Alarcón, Lidia, Fitz, Victoria W., Souter, Irene, Hauser, Russ, and Chavarro, Jorge E.

Abstract

Background/Objectives: Few studies have investigated the association of dietary glycemic index (GI), glycemic load (GL), and carbohydrate intake with antral follicle count (AFC). This study aimed to investigate the association of total carbohydrate intake and carbohydrate quality, measured by dietary GI and GL, with ovarian reserve assessed by AFC. Methods: This study included 653 females from the Environment And Reproductive Health Study who completed AFC and food frequency questionnaire. Of these, 579 female individuals had a quantifiable AFC in both ovaries and were included in the primary analysis. We estimated average GI and GL for each participant from self-reported intakes of carbohydrate-containing foods and divided participants into tertiles. Poisson regression models were used to quantify the relations of GI, GL, carbohydrates, and AFC while adjusting for potential confounders. Results: Participants had a median age of 35 y. Compared to participants in the lowest tertile of dietary GI, those in the highest tertile had a 6.3% (0.6%, 12.3%) higher AFC (p, trend 0.03) after adjustment for potential confounders. Stratified analyses revealed that the association between GI and AFC was present only among participants who had not undergone infertility evaluations. Conclusions: A higher dietary GI was associated with a higher AFC. Subgroup analyses among individuals who had not had a diagnostic evaluation of infertility before joining the study suggest that high-glycemic carbohydrates may be related to PCOM. [ABSTRACT FROM AUTHOR]

Published
2025
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