Artificial intelligence in the in vitro fertilization laboratory: a review of advancements over the last decade

Jiang, Victoria S.

doi:10.1016/j.fertnstert.2023.05.149

Cited by 17 publications

(10 citation statements)

References 39 publications

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“…In summary, the progress made in reproductive science with the creation of micro/nano robotic systems for sperm manipulation implies a significant turning point. By employing novel methodologies such as magnetic microbots and targeted drug delivery systems, scientists have attained an unparalleled level of accuracy and regulation in the manipulation and selection of sperm [18,19,21,22,44]. These developments possess tremendous potential for improving the efficacy of IVF procedures and for creating individualized fertility treatments that cater to specific requirements.…”

Section: Advancements In Sperm Manipulationmentioning

confidence: 99%

“…Additionally, the integration of micro/nano robotics with artificial intelligence (AI) and machine learning algorithms represents a significant advancement in ART, particularly in the embryo assessment and selection process. combining micro/nano robots with AI and machine learning algorithms is a notable progression in ART, especially in the evaluation and selection of embryos [35,44,55]. Embryologists can utilize a combination of robotics and AI systems to analyze extensive data obtained from microfluidic platforms and imaging technologies in order to enhance their understanding of embryo quality and developmental capacity.…”

Section: Advancements In Embryo Culturementioning

confidence: 99%

“…The integration of AI and machine learning algorithms represents a notable shift in the process of selecting embryos and forecasting pregnancy outcomes based on embryo features. AI systems can identify complex patterns and biomarkers associated with embryo quality and developmental potential by analyzing data from microfluidic devices, imaging technologies, and genetic studies [44,[60][61][62][63]. Prediction models and AI-driven decision-support systems let embryologists make more educated and personalized decisions when selecting embryos.…”

Section: Future Directionsmentioning

confidence: 99%

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Micro/Nanorobotics in In Vitro Fertilization: A Paradigm Shift in Assisted Reproductive Technologies

BENHAL

2024

Preprint

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In vitro fertilization (IVF) has revolutionized the field of assisted reproductive technologies (ART) by offering hope to individuals and couples struggling with infertility. Nevertheless, traditional IVF methods are not exempt from drawbacks, such as suboptimal success rates, intrusive procedures, and ethical considerations. The introduction of micro/nanorobotics in IVF offers a revolutionary chance to tackle these difficulties. This paper presents a short review of the utilization of micro/nanorobotics in IVF, investigating progress in the manipulation of sperm, retrieval of oocytes, culture of embryos, and potential future developments in this fast progressing area. In this discussion, we explore the fundamental concepts, advantages, difficulties, and possible influence of micro/nanorobotics in transforming IVF processes and enhancing patient outcomes.

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Section: Advancements In Sperm Manipulationmentioning

confidence: 99%

Section: Advancements In Embryo Culturementioning

confidence: 99%

Section: Future Directionsmentioning

confidence: 99%

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Micro/Nanorobotics in In Vitro Fertilization: A Paradigm Shift in Assisted Reproductive Technologies

BENHAL

2024

Preprint

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“…For instance, it has been shown that counting pronuclei would not necessarily differentiate an abnormal embryo from a normal embryo both in terms of morphology and genome integrity ( Destouni et al , 2018 ). Therefore, artificial intelligence (AI)-based automatic annotations, characterized by high accuracy and reliability, which capture and evaluate the continuous development of IVF embryos can enhance embryo selection strategies and prediction of IVF success ( Jiang and Bormann, 2023 ).…”

Section: Introductionmentioning

confidence: 99%

Generative artificial intelligence to produce high-fidelity blastocyst-stage embryo images

Cao,

Derhaag,

Coonen

et al. 2024

Human Reproduction

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STUDY QUESTION Can generative artificial intelligence (AI) models produce high-fidelity images of human blastocysts? SUMMARY ANSWER Generative AI models exhibit the capability to generate high-fidelity human blastocyst images, thereby providing substantial training datasets crucial for the development of robust AI models. WHAT IS KNOWN ALREADY The integration of AI into IVF procedures holds the potential to enhance objectivity and automate embryo selection for transfer. However, the effectiveness of AI is limited by data scarcity and ethical concerns related to patient data privacy. Generative adversarial networks (GAN) have emerged as a promising approach to alleviate data limitations by generating synthetic data that closely approximate real images. STUDY DESIGN, SIZE, DURATION Blastocyst images were included as training data from a public dataset of time-lapse microscopy (TLM) videos (n = 136). A style-based GAN was fine-tuned as the generative model. PARTICIPANTS/MATERIALS, SETTING, METHODS We curated a total of 972 blastocyst images as training data, where frames were captured within the time window of 110–120 h post-insemination at 1-h intervals from TLM videos. We configured the style-based GAN model with data augmentation (AUG) and pretrained weights (Pretrained-T: with translation equivariance; Pretrained-R: with translation and rotation equivariance) to compare their optimization on image synthesis. We then applied quantitative metrics including Fréchet Inception Distance (FID) and Kernel Inception Distance (KID) to assess the quality and fidelity of the generated images. Subsequently, we evaluated qualitative performance by measuring the intelligence behavior of the model through the visual Turing test. To this end, 60 individuals with diverse backgrounds and expertise in clinical embryology and IVF evaluated the quality of synthetic embryo images. MAIN RESULTS AND THE ROLE OF CHANCE During the training process, we observed consistent improvement of image quality that was measured by FID and KID scores. Pretrained and AUG + Pretrained initiated with remarkably lower FID and KID values compared to both Baseline and AUG + Baseline models. Following 5000 training iterations, the AUG + Pretrained-R model showed the highest performance of the evaluated five configurations with FID and KID scores of 15.2 and 0.004, respectively. Subsequently, we carried out the visual Turing test, such that IVF embryologists, IVF laboratory technicians, and non-experts evaluated the synthetic blastocyst-stage embryo images and obtained similar performance in specificity with marginal differences in accuracy and sensitivity. LIMITATIONS, REASONS FOR CAUTION In this study, we primarily focused the training data on blastocyst images as IVF embryos are primarily assessed in blastocyst stage. However, generation of an array of images in different preimplantation stages offers further insights into the development of preimplantation embryos and IVF success. In addition, we resized training images to a resolution of 256 × 256 pixels to moderate the computational costs of training the style-based GAN models. Further research is needed to involve a more extensive and diverse dataset from the formation of the zygote to the blastocyst stage, e.g. video generation, and the use of improved image resolution to facilitate the development of comprehensive AI algorithms and to produce higher-quality images. WIDER IMPLICATIONS OF THE FINDINGS Generative AI models hold promising potential in generating high-fidelity human blastocyst images, which allows the development of robust AI models as it can provide sufficient training datasets while safeguarding patient data privacy. Additionally, this may help to produce sufficient embryo imaging training data with different (rare) abnormal features, such as embryonic arrest, tripolar cell division to avoid class imbalances and reach to even datasets. Thus, generative models may offer a compelling opportunity to transform embryo selection procedures and substantially enhance IVF outcomes. STUDY FUNDING/COMPETING INTEREST(S) This study was supported by a Horizon 2020 innovation grant (ERIN, grant no. EU952516) and a Horizon Europe grant (NESTOR, grant no. 101120075) of the European Commission to A.S. and M.Z.E., the Estonian Research Council (grant no. PRG1076) to A.S., and the EVA (Erfelijkheid Voortplanting & Aanleg) specialty program (grant no. KP111513) of Maastricht University Medical Centre (MUMC+) to M.Z.E. TRIAL REGISTRATION NUMBER Not applicable.

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“…A discussion of the similarities and differences of the above-mentioned IVF pre-treatment models necessarily relate to model design, training techniques, validation, and performance metrics, which are beyond the scope of this article but are provided in a review by Yao et al, submitted [ 33 ]. Aside from AI/ML applications in patient counseling, there has been significant development in AI applications using unstructured data such as images or videos to support embryologists in selecting embryos most likely to result in successful pregnancies, and early prototypes have been reported to support optimization of various aspects of the IVF treatment protocol [ 34 , 35 ].…”

Section: Introductionmentioning

confidence: 99%

Improving IVF Utilization with Patient-Centric Artificial Intelligence-Machine Learning (AI/ML): A Retrospective Multicenter Experience

Yao,

Nguyen,

Retzloff

et al. 2024

JCM

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Objectives: In vitro fertilization (IVF) has the potential to give babies to millions more people globally, yet it continues to be underutilized. We established a globally applicable and locally adaptable IVF prognostics report and framework to support patient–provider counseling and enable validated, data-driven treatment decisions. This study investigates the IVF utilization rates associated with the usage of machine learning, center-specific (MLCS) prognostic reports (the Univfy® report) in provider-patient pre-treatment and IVF counseling. Methods: We used a retrospective cohort comprising 24,238 patients with new patient visits (NPV) from 2016 to 2022 across seven fertility centers in 17 locations in seven US states and Ontario, Canada. We tested the association of Univfy report usage and first intra-uterine insemination (IUI) and/or first IVF usage (a.k.a. conversion) within 180 days, 360 days, and “Ever” of NPV as primary outcomes. Results: Univfy report usage was associated with higher direct IVF conversion (without prior IUI), with odds ratios (OR) 3.13 (95% CI 2.83, 3.46), 2.89 (95% CI 2.63, 3.17), and 2.04 (95% CI 1.90, 2.20) and total IVF conversion (with or without prior IUI), OR 3.41 (95% CI 3.09, 3.75), 3.81 (95% CI 3.49, 4.16), and 2.78 (95% CI 2.59, 2.98) in 180-day, 360-day, and Ever analyses, respectively; p < 0.05. Among patients with Univfy report usage, after accounting for center as a factor, older age was a small yet independent predictor of IVF conversion. Conclusions: Usage of a patient-centric, MLCS-based prognostics report was associated with increased IVF conversion among new fertility patients. Further research to study factors influencing treatment decision making and real-world optimization of patient-centric workflows utilizing the MLCS reports is warranted.

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Artificial intelligence in the in vitro fertilization laboratory: a review of advancements over the last decade

Cited by 17 publications

References 39 publications

Micro/Nanorobotics in In Vitro Fertilization: A Paradigm Shift in Assisted Reproductive Technologies

Micro/Nanorobotics in In Vitro Fertilization: A Paradigm Shift in Assisted Reproductive Technologies

Generative artificial intelligence to produce high-fidelity blastocyst-stage embryo images

Improving IVF Utilization with Patient-Centric Artificial Intelligence-Machine Learning (AI/ML): A Retrospective Multicenter Experience

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