Enhancing assisted reproductive technologies with deep learning and data visualization

Active Project Grant Awarded Last action

Award ID
R01HD104969
Recipient
Awarding agency
Obligated
$7,420,908

Award facts

Completion date 93% complete
Originally
Funding
  • $6,690,498.00 Federal
  • $730,410.00 Non-federal
Place of performance
Boston, MA 02134, USA

Award details come from USAspending.gov.

Description

As the agency reported it.

Abstract
Assisted reproduction technology (art) is a clinical treatment for infertile couples who want to achieve a pregnancy. In art, embryologists fertilize eggs retrieved from the patient or a donor, culture the resulting embryos in vitro, and then transfer the selected embryo(s) to the mother's uterus. While art is responsible for 1.9% of babies born in the united states as of 2018, selecting which embryo to transfer is a signicant challenge. The difculty comes from the complexity of confounding factors and the lack of understanding of human pre-implantation embryo development. Because of this difculty, multiple embryos are often transferred to increases the potential of success, resulting in multiple pregnancy rates of nearly 20%, which can lead to signicant morbidity and medical expenses to patients. The ideal is to transfer only a single embryo, but this necessitates the ability to select the best embryo from a cohort. Here, we propose to create a clinical decision support system to improve embryo selection in art. To this end, we will develop novel deep learning models for robust embryo feature extraction and interactive data visualization methods for human-in-the-loop analysis. We will RST extract and analyze visual features from routinely collected images of embryos. We will then combine these visual features with patients' electronic health record (EHR) data to develop interpretable computation models that score embryos on their viability. We plan to integrate our machine learning solutions into an easily accessible cloud service platform that will be adaptable across clinics to improve art embryo selection and clinical data analysis. Our research goals will be achieved by novel machine learning-based models for morphological feature extrac- tion and importance estimation of each confounding factor and a clinical decision support system for art. For morphological feature extraction, we plan to conduct semi-supervised learning of convolutional neural networks to minimize manual labeling that requires extensive human effort. Our feature extraction model will be the RST comprehensive classication and segmentation method for art. To aid in embryo selection, we will develop novel deep learning-based models to predict probabilities of achieving pregnancy by accepting visual features and EHR data as the input. We will also develop visual analytic tools that allow analysts to better understand and steer these deep learning models. We will estimate the importance of each input interpretable factor in embryo selection to explain the prediction to embryologists. Finally, we will develop embryoproler, a clinical decision support system for art, that combines our machine learning-based models with a user-facing suite of visual analytic tools to support user guidance and clinical decision making. Embryoproler will help facilitate daily operation in clinics, foster human-guided decision making, enrich data-driven embryo analysis, and enhance the ability to select the developmentally most competent embryo for transfer to improve art success rates. Our project will create state-of-the-art analysis approaches for art clinicians.

Transactions

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Original award $730,410 $730,410
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Subawards

Subrecipient Amount Awarded
Tel Aviv Sourasky Medical Center Dr. Ben Yosef will play a supervisory role on aspects of this work related to embryology and clinical IVF. She will provide advice and expertise in those areas,… $52,229
Association Hopital Foch Under the supervision of Professor Catherine Racowsky, University Consultani at Hospital FOCH, embryo images will be extracted from the FOCH Hospital internal… $0
Association Hopital Foch "Under the supervision of Professor Catherine Racowsky, University Consultani at Hospital FOCH, embryo images will be extracted from the FOCH Hospital… $64,620

Quick answers

Who received award R01HD104969?

R01HD104969 was awarded to President And Fellows Of Harvard College by National Institute of Child Health and Human Development (National Institutes of Health) on April 1, 2021.

How much is award R01HD104969 worth?

$7,420,908 has been obligated on R01HD104969: $6,690,498 in federal funding and $730,410 in non-federal funding.

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