A non-invasive artificial intelligence approach for the prediction of human blastocyst ploidy: a retrospective model development and validation study

Barnes, J. Wesley; Brendel, Matthew; Gao, Vianne R.; Rajendran, Suraj; Kim, Junbum; Li, Qianzi; Malmsten, Jonas; Sierra, José; Zisimopoulos, Pantelis; Sigaras, Alexandros; Khosravi, Pegah; Meseguer, M.; Rosenwaks, Zev; Elemento, Olivier; Zaninović, Nikica; Hajirasouliha, Iman

doi:10.1016/s2589-7500(22)00213-8

Cited by 53 publications

(15 citation statements)

References 34 publications

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“…CNN algorithms are a type of deep-learning model that attempts to simply replicate the human visual cortex with a simulated network of connected neuron layers (neural network) that, through iterative training, transforms input data into the desired output labels. There have been considerable studies on the utility of machine learning and AI-based image analysis on the selection of embryos for prediction of euploidy status, implantation potential and incidence of miscarriage (Barnes et al, 2023, Diakiw et al, 2022, Duval et al, 2023, Hariharan et al, 2019, Tran et al, 2018, VerMilyea et al, 2020). Studies have also proven the application of ML in the selection and assessment of sperm for use in ICSI by tacking sperm correlated with better quality blastocysts (Joshi et al, 2023, Mendizabal-Ruiz et al, 2022).…”

Section: Discussionmentioning

confidence: 99%

AI facilitated sperm detection in azoospermic samples for use in ICSI

Goss,

Vasilescu,

Vasilescu

et al. 2023

Preprint

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Research question: Can artificial intelligence (AI) improve efficiency and efficacy of sperm searches in azoospermic samples? Design: This two-phase proof-of-concept study beginning with a training phase using 8 azoospermic patients (>10000 sperm images) to provide a variety of surgically collected samples for sperm morphology and debris variation to train a convolutional neural network to identify sperm. Secondly, side-by-side testing on 2 cohorts, an embryologist versus the AI identifying all sperm in still images (cohort 1, N=4, 2660 sperm) and then a side-by-side test with deployment of the AI model on an ICSI microscope and the embryologist performing a search with and without the aid of the AI (cohort 2, N=4, >1300 sperm). Time taken, accuracy and precision of sperm identification was measured. Results: In cohort 1, the AI model showed improvement in time-taken to identify all sperm per field of view (0.019+-0.30 x 10-5s versus 36.10+-1.18s, P<0.0001) and improved accuracy (91.95+-0.81% vs 86.52+-1.34%, P<0.001) compared to an embryologist. From a total of 688 sperm in all samples combined, 560 were found by an embryologist and 611 were found by the AI in <1000th of the time. In cohort 2, the AI-aided embryologist took significantly less time per droplet (98.90+-3.19s vs 168.7+-7.84s, P<0.0001) and found 1396 sperm, while 1274 were found without AI, although no significant difference was observed. Conclusions: AI-powered image analysis has the potential for seamless integration into laboratory workflows, and to reduce time to identify and isolate sperm from surgical sperm samples from hours to minutes, thus increasing success rates from these treatments.

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Section: Discussionmentioning

confidence: 99%

AI facilitated sperm detection in azoospermic samples for use in ICSI

Goss,

Vasilescu,

Vasilescu

et al. 2023

Preprint

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“…Large language models represent some of the largest and most complex ML models ever developed, with hundreds of billions of trainable Compared to the use of traditional methods, the application of AI technologies represents a critical opportunity for dramatically improving the scalability, accuracy, and utility of clinical genomics. Diverse AI approaches are already being applied in this realm (Ledgister Hanchard et al, 2022)-to support the identification of rare diseases through analysis of facial gestalt in the clinic (Gurovich et al, 2019;Hsieh et al, 2022;Myers et al, 2020;Porras et al, 2021), to noninvasively select embryos for preimplantation genetic testing or direct uterine transfer (Barnes et al, 2023;Dimitriadis et al, 2022), and to provide information to individuals with genetic health concerns (Schmidlen et al, 2022;Smith et al, 2023).…”

Section: Artificial Intelligence Methods In Laboratory Genomicsmentioning

confidence: 99%

Applications of artificial intelligence in clinical laboratory genomics

Aradhya

Facio

Metz

et al. 2023

American J of Med Genetics Pt C

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The transition from analog to digital technologies in clinical laboratory genomics is ushering in an era of “big data” in ways that will exceed human capacity to rapidly and reproducibly analyze those data using conventional approaches. Accurately evaluating complex molecular data to facilitate timely diagnosis and management of genomic disorders will require supportive artificial intelligence methods. These are already being introduced into clinical laboratory genomics to identify variants in DNA sequencing data, predict the effects of DNA variants on protein structure and function to inform clinical interpretation of pathogenicity, link phenotype ontologies to genetic variants identified through exome or genome sequencing to help clinicians reach diagnostic answers faster, correlate genomic data with tumor staging and treatment approaches, utilize natural language processing to identify critical published medical literature during analysis of genomic data, and use interactive chatbots to identify individuals who qualify for genetic testing or to provide pre‐test and post‐test education. With careful and ethical development and validation of artificial intelligence for clinical laboratory genomics, these advances are expected to significantly enhance the abilities of geneticists to translate complex data into clearly synthesized information for clinicians to use in managing the care of their patients at scale.

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“…Hence, various technological solutions have been sought to ascertain embryos' genetic complements in a non-invasive fashion, i.e., without a biopsy. These commonly rely on an embryo reaching certain developmental milestones or some other characteristics ( 53 – 56 ), commonly interpreted using Artificial Intelligence ( 57 – 59 ). Crucially, in the past, such technologies were only ever used for embryo selection/prioritisation, and even embryos considered to be suboptimal were eventually transferred once embryos assessed as being “better” were used up without success.…”

Section: Non-invasive Genetic Testing and Selectionmentioning

confidence: 99%

Novel embryo selection strategies—finding the right balance

Polyakov,

Rozen,

Gyngell

et al. 2023

Front. Reprod. Health

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The use of novel technologies in the selection of embryos during in vitro fertilisation (IVF) has the potential to improve the chances of pregnancy and birth of a healthy child. However, it is important to be aware of the potential risks and unintended consequences that may arise from the premature implementation of these technologies. This article discusses the ethical considerations surrounding the use of novel embryo selection technologies in IVF, including the growing uptake of genetic testing and others, and argues that prioritising embryos for transfer using these technologies is acceptable, but discarding embryos based on unproven advances is not. Several historical examples are provided, which demonstrate possible harms, where the overall chance of pregnancy may have been reduced, and some patients may have missed out on biological parenthood altogether. We emphasise the need for caution and a balanced approach to ensure that the benefits of these technologies outweigh any potential harm. We also highlight the primacy of patients' autonomy in reproductive decision-making, especially when information gained by utilising novel technologies is imprecise.

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A non-invasive artificial intelligence approach for the prediction of human blastocyst ploidy: a retrospective model development and validation study

Cited by 53 publications

References 34 publications

AI facilitated sperm detection in azoospermic samples for use in ICSI

AI facilitated sperm detection in azoospermic samples for use in ICSI

Applications of artificial intelligence in clinical laboratory genomics

Novel embryo selection strategies—finding the right balance

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