Deep learning based on images of human embryos obtained from high-resolusion time-lapse cinematography for predicting good-quality embryos

Iwata, Kazunori; Sato, Maiko; Matsumoto, Izumi; Shimura, Tomoya; Yumoto, Keitaro; Negami, A. I.; Mio, Y.

doi:10.1016/j.fertnstert.2018.07.615

Cited by 7 publications

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“…It was discovered that when age, k1 and eta1 algorithms and architecture were employed, the model could accurately forecast blastocyst development with a positive predictive value of 80% (95% CI: 60.45–91.28) and a negative predictive value of 63.8% (95% CI: 53.42–73.18). Meanwhile, Iwata et al 35 proposed a DL-based prediction model concerning the identification of good quality embryos. Investigators used images of human embryos acquired from high-resolution time lapse cinematography (31 hourly images recorded for 30 hours).…”

Section: Discussionmentioning

confidence: 99%

Intelligent systems in obstetrics and midwifery: Applications of machine learning

Barbounaki¹,

Vivilaki²

2021

Eur J Midwifery

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INTRODUCTION Machine learning is increasingly utilized over recent years in order to develop models that represent and solve problems in a variety of domains, including those of obstetrics and midwifery. The aim of this systematic review was to analyze research studies on machine learning and intelligent systems applications in midwifery and obstetrics. METHODS A thorough literature review was performed in four electronic databases (PubMed, APA PsycINFO, SCOPUS, ScienceDirect). Only articles that discussed machine learning and intelligent systems applications in midwifery and obstetrics, were considered in this review. Selected articles were critically evaluated as for their relevance and a contextual synthesis was conducted. RESULTS Thirty-two articles were included in this systematic review as they met the inclusion and methodological criteria specified in this study. The results suggest that machine learning and intelligent systems have produced successful models and systems in a broad list of midwifery and obstetrics topics, such as diagnosis, pregnancy risk assessment, fetal monitoring, bladder tumor, etc. CONCLUSIONS This systematic review suggests that machine learning represents a very promising area of artificial intelligence for the development of practical and highly effective applications that can support human experts, as well the investigation of a wide range of exciting opportunities for further research.

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Section: Discussionmentioning

confidence: 99%

Intelligent systems in obstetrics and midwifery: Applications of machine learning

Barbounaki¹,

Vivilaki²

2021

Eur J Midwifery

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“…More recently, Segal et al 41 have developed a random forest classifier-based tool using 2744 embryos to predict which patients should have extended culture with an accuracy of 76.4%. Also, Matsumoto et al 42 used time-lapse monitoring of 118 human embryos to determine good-quality embryos using deep learning-based method, which is based on the Keras neural network library. They achieved 70 and 80% accuracy for the validation dataset through two different cell stages that are significantly lower than the performance obtained from our framework.…”

Section: Discussionmentioning

confidence: 99%

Deep learning enables robust assessment and selection of human blastocysts after in vitro fertilization

et al. 2019

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Visual morphology assessment is routinely used for evaluating of embryo quality and selecting human blastocysts for transfer after in vitro fertilization (IVF). However, the assessment produces different results between embryologists and as a result, the success rate of IVF remains low. To overcome uncertainties in embryo quality, multiple embryos are often implanted resulting in undesired multiple pregnancies and complications. Unlike in other imaging fields, human embryology and IVF have not yet leveraged artificial intelligence (AI) for unbiased, automated embryo assessment. We postulated that an AI approach trained on thousands of embryos can reliably predict embryo quality without human intervention. We implemented an AI approach based on deep neural networks (DNNs) to select highest quality embryos using a large collection of human embryo time-lapse images (about 50,000 images) from a high-volume fertility center in the United States. We developed a framework (STORK) based on Google’s Inception model. STORK predicts blastocyst quality with an AUC of >0.98 and generalizes well to images from other clinics outside the US and outperforms individual embryologists. Using clinical data for 2182 embryos, we created a decision tree to integrate embryo quality and patient age to identify scenarios associated with pregnancy likelihood. Our analysis shows that the chance of pregnancy based on individual embryos varies from 13.8% (age ≥41 and poor-quality) to 66.3% (age <37 and good-quality) depending on automated blastocyst quality assessment and patient age. In conclusion, our AI-driven approach provides a reproducible way to assess embryo quality and uncovers new, potentially personalized strategies to select embryos.

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“…Moreover, the gold standard samples are different in different studies. Kuznyetsov et al [30], Jiao et al [16], Xu et al [6], and Li et al [11] found that the consistency between culture medium and the whole embryo was greater than 90 %. Using whole embryos as a gold standard, Huang et al [17] even observed a higher consistency in culture medium DNA than in TE-biopsied samples, indicating that culture medium-based PGT-A could potentially be more accurate than TE-based PGT-A.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning-Guided Noninvasive Embryo Selection for Clinical in Vitro Fertilization Treatment to Avoid Wasting Potentially Qualified Embryos

Chen

Liu

et al. 2021

Preprint

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BackgroundThe success rates of in vitro fertilization (IVF) treatment are limited by the aneuploidy of human embryos. Pre-implantation genetic testing for aneuploidy(PGT-A) is often used to select embryos with normal ploidy but requires invasive embryo biopsy. MethodsWe performed chromosome sequencing of 345 paired blastocyst culture medium and whole blastocyst samples and developed a noninvasive embryo grading system based on the random forest machine-learning algorithm to predict blastocyst ploidy. The system was validated in 266 patients, and a blinded prospective observational study was performed to investigate clinical outcomes between machine learning-guided and traditional niPGT-A analyses. We graded embryos as A, B, or C using machine learning-guided niPGT-A analysis according to their euploidy probability levels predicted by noninvasive chromosomal screening. ResultsWe observed higher live birth rate in A- versus C-grade embryos (50.4% versus 27.1%, p=0.006) and B- versus C-grade embryos (45.3% versus 27.1%, p=0.022) and lower miscarriage rate in A- versus C-grade embryos (15.9% versus 33.3%, p=0.026) and B- versus C-grade embryos (14.3% versus 33.3%, p=0.021). The embryo utilization rate was significantly higher through machine learning strategy compared to the conventional dichotomic judgment of euploidy or aneuploidy in the niPGT-A analysis (78.8% versus 57.9%, p<0.001). We observed better outcomes in A- and B-grade embryos versus C-grade embryos and higher embryo utilization rates through machine learning strategies than traditional niPGT-A analysis. ConclusionThese results demonstrate that the machine learning-guided embryo grading system can optimize embryo selection and avoid wasting potential embryos.Trial registrationChinese Clinical Trial Registry,ChiCTR-RRC-17010396.Registered 11 January 2017, http://www.chictr.org.cn/ChiCTR-RRC-17010396

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Deep learning based on images of human embryos obtained from high-resolusion time-lapse cinematography for predicting good-quality embryos

Abstract: The simplified SART embryo scoring system is highly correlated to implantation and live birth in single blastocyst transfers. Journal of assisted reproduction and genetics, 30(4), 563-567.

Cited by 7 publications

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Intelligent systems in obstetrics and midwifery: Applications of machine learning

Intelligent systems in obstetrics and midwifery: Applications of machine learning

Deep learning enables robust assessment and selection of human blastocysts after in vitro fertilization

Machine Learning-Guided Noninvasive Embryo Selection for Clinical in Vitro Fertilization Treatment to Avoid Wasting Potentially Qualified Embryos

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