Time-lapse imaging of embryos: current evidence supporting its use

Leung, Annie; Son, Weon‐Young; Dahan, Michael H.

doi:10.1080/17434440.2016.1230015

Cited by 7 publications

(5 citation statements)

References 19 publications

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“…To reduce the computational load associated with image processing, raw images were first converted to a grayscale (27, 28) and adjusted to a standard size using the Matlab ® Image Processing Toolbox™ grayscale conversion and resizing functions (29).…”

Section: Methodsmentioning

confidence: 99%

An image processing protocol to extract variables predictive of human embryo fitness for assisted reproduction

Chéles

Ferreira

Jesus

et al. 2022

Preprint

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OBJECTIVE: To study the application of image processing for segmentation of blastocysts images and extraction of potential variables for prediction of embryo fitness. DESIGN: Retrospective study. SETTING: Single reproductive medical center. IVI-RMA (Valencia, Spain) between 2017 and 2019. PATIENTS: An initial dataset including 353 images from EmbryoScope and 474 images from Geri incubators was acquired, of which 320 images from EmbryoScope and 309 images from Geri incubators were used in this study. INTERVENTION(S): None. MAIN OUTCOME MEASURE(S): Successful segmentation of images into trophectoderm (TE), blastocoel, and inner cell mass (ICM) using the proposed processing steps. RESULTS: A total of 33 variables were automatically generated by digital image processing, each representing a different aspect of the embryo and describing a different characteristic of the expanding blastocyst (EX), ICM, or TE. These variables can be categorized into texture, gray level average, gray level standard deviation, modal value, relations, and light level. The automated and directed steps of the proposed processing protocol exclude spurious results, except when image quality (e.g., focus) prevents correct segmentation. CONCLUSIONS: The proposed image processing protocol that can successfully segment human blastocyst images from two distinct sources and extract 33 variables with potential utility in embryo selection for ART.

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Section: Methodsmentioning

confidence: 99%

An image processing protocol to extract variables predictive of human embryo fitness for assisted reproduction

Chéles

Ferreira

Jesus

et al. 2022

Preprint

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show abstract

“…To reduce the computational load associated with image processing, raw images were first converted to grayscale [27,28] and adjusted to a standard size using the Matlab ® Image Processing Toolbox™ grayscale conversion and resizing functions [29].…”

Section: Grayscale Conversion and Image Resizingmentioning

confidence: 99%

An Image Processing Protocol to Extract Variables Predictive of Human Embryo Fitness for Assisted Reproduction

et al. 2022

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Despite the use of new techniques on embryo selection and the presence of equipment on the market, such as EmbryoScope® and Geri®, which help in the evaluation of embryo quality, there is still a subjectivity between the embryologist’s classifications, which are subjected to inter- and intra-observer variability, therefore compromising the successful implantation of the embryo. Nonetheless, with the acquisition of images through the time-lapse system, it is possible to perform digital processing of these images, providing a better analysis of the embryo, in addition to enabling the automatic analysis of a large volume of information. An image processing protocol was developed using well-established techniques to segment the image of blastocysts and extract variables of interest. A total of 33 variables were automatically generated by digital image processing, each one representing a different aspect of the embryo and describing a different characteristic of the blastocyst. These variables can be categorized into texture, gray-level average, gray-level standard deviation, modal value, relations, and light level. The automated and directed steps of the proposed processing protocol exclude spurious results, except when image quality (e.g., focus) prevents correct segmentation. The image processing protocol can segment human blastocyst images and automatically extract 33 variables that describe quantitative aspects of the blastocyst’s regions, with potential utility in embryo selection for assisted reproductive technology (ART).

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“…Several studies have demonstrated high accuracy in predicting blastocyst formation using morphokinetic parameters [8][9][10][11]. In comparison, predicting implantation potential or pregnancy and live birth rates is more complicated since it involves other factors such as uterine receptivity [12]. The KIDScore algorithm, based on a retrospective evaluation of 3,275 embryos with known implantation data (KID) and measurements of 6 morphokinetic parameters, achieved AUC scores of 0.650, 0.745, and 0.679 for predicting implantation potential, blastocyst formation, and blastocyst quality respectively [13].…”

Section: Blastocyst Quality and Implantation Potentialmentioning

confidence: 99%

Current trends in artificial intelligence in reproductive endocrinology

Bhaskar

Chang

Wang

2022

Current Opinion in Obstetrics &Amp; Gynecology

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Purpose of reviewArtificial Intelligence, a tool that integrates computer science and machine learning to mimic human decision-making processes, is transforming the world and changing the way we live. Recently, the healthcare industry has gradually adopted artificial intelligence in many applications and obtained some degree of success. In this review, we summarize the current applications of artificial intelligence in Reproductive Endocrinology, in both laboratory and clinical settings. Recent findingsArtificial Intelligence has been used to select the embryos with high implantation potential, proper ploidy status, to predict later embryo development, and to increase pregnancy and live birth rates. Some studies also suggested that artificial intelligence can help improve infertility diagnosis and patient management. Recently, it has been demonstrated that artificial intelligence also plays a role in effective laboratory quality control and performance.

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Time-lapse imaging of embryos: current evidence supporting its use

Cited by 7 publications

References 19 publications

An image processing protocol to extract variables predictive of human embryo fitness for assisted reproduction

An image processing protocol to extract variables predictive of human embryo fitness for assisted reproduction

An Image Processing Protocol to Extract Variables Predictive of Human Embryo Fitness for Assisted Reproduction

Current trends in artificial intelligence in reproductive endocrinology

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