Embryo Ranking Intelligent Classification Algorithm (ERICA): artificial intelligence clinical assistant predicting embryo ploidy and implantation

Chávez-Badiola, Alejandro; Farías, Adolfo Flores-Saiffe; Mendizabal-Ruiz, Gerardo; Drakeley, Andrew J.; Cohen, Jacques

doi:10.1016/j.rbmo.2020.07.003

Cited by 89 publications

(63 citation statements)

References 23 publications

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“…Furthermore, images of the oocytes, sperm and embryos will be collected and evaluated using AI deep machine learning, as recent studies have suggested their assessment could better inform embryo selection [ 37 ].…”

Section: Methodsmentioning

confidence: 99%

Omics and Artificial Intelligence to Improve In Vitro Fertilization (IVF) Success: A Proposed Protocol

et al. 2021

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The prediction of in vitro fertilization (IVF) outcome is an imperative achievement in assisted reproduction, substantially aiding infertile couples, health systems and communities. To date, the assessment of infertile couples depends on medical/reproductive history, biochemical indications and investigations of the reproductive tract, along with data obtained from previous IVF cycles, if any. Our project aims to develop a novel tool, integrating omics and artificial intelligence, to propose optimal treatment options and enhance treatment success rates. For this purpose, we will proceed with the following: (1) recording subfertile couples’ lifestyle and demographic parameters and previous IVF cycle characteristics; (2) measurement and evaluation of metabolomics, transcriptomics and biomarkers, and deep machine learning assessment of the oocyte, sperm and embryo; (3) creation of artificial neural network models to increase objectivity and accuracy in comparison to traditional techniques for the improvement of the success rates of IVF cycles following an IVF failure. Therefore, “omics” data are a valuable parameter for embryo selection optimization and promoting personalized IVF treatment. “Omics” combined with predictive models will substantially promote health management individualization; contribute to the successful treatment of infertile couples, particularly those with unexplained infertility or repeated implantation failures; and reduce multiple gestation rates.

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Section: Methodsmentioning

confidence: 99%

Omics and Artificial Intelligence to Improve In Vitro Fertilization (IVF) Success: A Proposed Protocol

et al. 2021

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“…PR AUC has the same benefits and disadvantages as the F 1 -score mentioned above, however with the important difference of providing a performance measure across all possible thresholds. Another ranking metric reported by [13] is the normalized discounted cumulative gain (nDCG) [36]. It measures the ranking quality within a cohort by weighting embryos by their relevance and their position in the sorted list of model scores.…”

Section: Model-wide Metricsmentioning

confidence: 99%

“…Automated embryo evaluation using machine learning or computer vision based on embryo images has been an active field of research for more than a decade [9,10]. Yet, within the past few years, many of the publications have focused more on commercialization and competition rather than methodological novelties and technical details of the AI [11][12][13][14][15]. Instead, they seem to focus on reporting large datasets, high performance values based upon a variety of metrics, and ability to surpass human/embryologist performance.…”

Section: Introductionmentioning

confidence: 99%

Embryo selection with artificial intelligence: how to evaluate and compare methods?

Kragh

Karstoft

2021

J Assist Reprod Genet

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Embryo selection within in vitro fertilization (IVF) is the process of evaluating qualities of fertilized oocytes (embryos) and selecting the best embryo(s) available within a patient cohort for subsequent transfer or cryopreservation. In recent years, artificial intelligence (AI) has been used extensively to improve and automate the embryo ranking and selection procedure by extracting relevant information from embryo microscopy images. The AI models are evaluated based on their ability to identify the embryo(s) with the highest chance(s) of achieving a successful pregnancy. Whether such evaluations should be based on ranking performance or pregnancy prediction, however, seems to divide studies. As such, a variety of performance metrics are reported, and comparisons between studies are often made on different outcomes and data foundations. Moreover, superiority of AI methods over manual human evaluation is often claimed based on retrospective data, without any mentions of potential bias. In this paper, we provide a technical view on some of the major topics that divide how current AI models are trained, evaluated and compared. We explain and discuss the most common evaluation metrics and relate them to the two separate evaluation objectives, ranking and prediction. We also discuss when and how to compare AI models across studies and explain in detail how a selection bias is inevitable when comparing AI models against current embryo selection practice in retrospective cohort studies.

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“…To be sure, AI has clear and rate-limiting challenges. As a measure of its assessment of embryo quality, the overall accuracy in predicting euploidy was only 70% [ 15 ]. AI requires calibration and there is currently no agreement on how to compare performances of various AI models for optimal methods.…”

Section: Conclusion (Trolice)mentioning

confidence: 99%

“…AI and machine leaning (ML) are used to analyze data much more efficiently than humans could ever collate, analyze, and/or understand to solve intricate problems, such as fraud detection and weather forecasting. The advanced computing capabilities of AI are being used pre-conception to develop complex predictive models to select the best embryo for transfer [ 12 – 14 ], and diagnose the ploidy status of embryos [ 15 , 16 ], as well as allow post-conception disease diagnoses while predicting adverse outcomes during pregnancy [ 17 ] (e.g., preterm birth, preeclampsia [ 18 ], and miscarriage [ 19 ]) that have a complicated and undetermined etiology. In places like the USA, maternal mortality is increasing, despite the contrary trend worldwide.…”

Section: Introduction (Trolice)mentioning

confidence: 99%

Artificial intelligence—the future is now

Trolice

Curchoe²,

Quaas

2021

J Assist Reprod Genet

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Embryo Ranking Intelligent Classification Algorithm (ERICA): artificial intelligence clinical assistant predicting embryo ploidy and implantation

Cited by 89 publications

References 23 publications

Omics and Artificial Intelligence to Improve In Vitro Fertilization (IVF) Success: A Proposed Protocol

Omics and Artificial Intelligence to Improve In Vitro Fertilization (IVF) Success: A Proposed Protocol

Embryo selection with artificial intelligence: how to evaluate and compare methods?

Artificial intelligence—the future is now

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