Can deep learning automatically predict fetal heart pregnancy with almost perfect accuracy?

Kan‐Tor, Yoav; Ben‐Meir, Assaf; Buxboim, Amnon

doi:10.1093/humrep/deaa083

Cited by 11 publications

(8 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The relevance of a high-quality dataset cannot be overestimated, since problems related to training on suboptimal datasets are numerous. One example is the result of training on an unbalanced dataset, which can lead to unreliable results (Chawla et al, 2004), which may have been the case in a study by Tran et al In this study, the high proportion of embryos with negative outcomes outweighed those with positive outcomes, resulting in a deeply unbalanced dataset, perhaps not representative of the problem, which in turn led to an almost unrealistic performance (area under the ROC curve of 0.93) (Tran et al, 2019;Kan-Tor et al, 2020a).…”

Section: Ai Algorithm Training and Validationmentioning

confidence: 87%

Artificial intelligence in the embryology laboratory: a review

Dimitriadis

Zaninović

Badiola

et al. 2022

Reproductive BioMedicine Online

View full text Add to dashboard Cite

Section: Ai Algorithm Training and Validationmentioning

confidence: 87%

Artificial intelligence in the embryology laboratory: a review

Dimitriadis

Zaninović

Badiola

et al. 2022

Reproductive BioMedicine Online

View full text Add to dashboard Cite

“…The reported prediction of MC outcome with AUC 0.68% is comparable with the prediction accuracy of recently reported CNN implantation classifiers, 20,22 suggesting that missing uterine parameters and/or other maternal factors prevent further improvement in the prediction of implantation outcome as well as MC outcome. 45 In combination with implantation potential assessment, our algorithm thus provides a real-time non-invasive decision-support tool for deselecting embryos with high risk of MC outcome, which is expected to improve live-birth rates and shorten time-to-pregnancy in IVF-embryo transfer treatments.…”

Section: Discussionmentioning

confidence: 99%

Embryo classification beyond pregnancy: Early prediction of first trimester miscarriage using machine learning

Amitai

Kan‐Tor

Srebnik

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

ObjectiveDevelop a machine learning classifier for predicting the risk of cleavage-stage embryos to undergo first trimester miscarriage based on time-lapse images of preimplantation development.DesignRetrospective study of a 4-year multi-center cohort of women undergoing intra-cytoplasmatic sperm injection (ICSI). The study included embryos with positive indication of clinical implantation based on gestational sac visualization either with first trimester miscarriage or live birth outcome. Miscarriage was determined based on negative fetal heartbeat indication during the first trimester.SettingData were recorded and obtained in hospital setting and research was performed in university setting.Patient(s)Data from 391 women who underwent fresh single or double embryo transfers were included.Intervention(s)None.Main Outcome Measure(s)A minimal subset of six non-redundant morphodynamic features were screened that maintain high prediction capacity. Using this feature subset, XGBoost and Random Forest models were trained following a 100-fold Monte-Carlo cross validation scheme. Feature importance was scored using the SHapley Additive exPlanations (SHAP) methodology. Miscarriage versus live-birth outcome prediction was evaluated using a non-contaminated balanced test set and quantified in terms of the area under the receiver operating characteristic (ROC) curve (AUC), precision-recall curve, positive predictive value (PPV), and confusion matrices.Result(s)Features that account for the distribution of the nucleolus precursor bodies within the small pronucleus and pronuclei dynamics were highly predictive of miscarriage outcome. AUC for miscarriage prediction of validation and test set embryos using both models was 0.68-to-0.69. Clinical utility was tested by setting two classification thresholds accounting for high sensitivity 0.73 with 0.6 specificity and high specificity 0.93 with 0.33 sensitivity.Conclusion(s)We report the development of a decision-support tool for identifying the embryos with high risk of miscarriage. Prioritizing embryos for transfer based on their predicted risk of miscarriage in combination with their predicted implantation potential will improve live-birth rates and shorten time-to-pregnancy.CapsuleThe risk of first trimester miscarriage of cleavage stage embryos is predicted with AUC 68% by screening a minimal subset of six non-redundant morpho-dynamic features and training a machine-learning classifier.

show abstract

“…Their ROC curve’s AUC was 0.93. However, as Kan-Tor et al (2020) point out, the majority of the embryos on which the algorithm had been trained and tested were of such poor quality that they would have been discarded in any event, thereby artificially inflating the AUC. As Kan-Tor et al explain, the clinical need is to identify the embryo with the highest chance of success among a set of embryos that appear to be potentially viable, and not from embryos which embryologists readily discard.…”

Section: Current Use Of Ai In Embryo Selectionmentioning

confidence: 99%

Interpretable, not black-box, artificial intelligence should be used for embryo selection

Afnan

Liu

Conitzer

et al. 2021

Human Reproduction Open

View full text Add to dashboard Cite

Artificial intelligence (AI) techniques are starting to be used in IVF, in particular for selecting which embryos to transfer to the woman. AI has the potential to process complex data sets, to be better at identifying subtle but important patterns, and to be more objective than humans when evaluating embryos. However, a current review of the literature shows much work is still needed before AI can be ethically implemented for this purpose. No randomized controlled trials (RCTs) have been published, and the efficacy studies which exist demonstrate that algorithms can broadly differentiate well between ‘good-’ and ‘poor-’ quality embryos but not necessarily between embryos of similar quality, which is the actual clinical need. Almost universally, the AI models were opaque (‘black-box’) in that at least some part of the process was uninterpretable. This gives rise to a number of epistemic and ethical concerns, including problems with trust, the possibility of using algorithms that generalize poorly to different populations, adverse economic implications for IVF clinics, potential misrepresentation of patient values, broader societal implications, a responsibility gap in the case of poor selection choices and introduction of a more paternalistic decision-making process. Use of interpretable models, which are constrained so that a human can easily understand and explain them, could overcome these concerns. The contribution of AI to IVF is potentially significant, but we recommend that AI models used in this field should be interpretable, and rigorously evaluated with RCTs before implementation. We also recommend long-term follow-up of children born after AI for embryo selection, regulatory oversight for implementation, and public availability of data and code to enable research teams to independently reproduce and validate existing models.

show abstract

Can deep learning automatically predict fetal heart pregnancy with almost perfect accuracy?

Cited by 11 publications

References 2 publications

Artificial intelligence in the embryology laboratory: a review

Artificial intelligence in the embryology laboratory: a review

Embryo classification beyond pregnancy: Early prediction of first trimester miscarriage using machine learning

Interpretable, not black-box, artificial intelligence should be used for embryo selection

Contact Info

Product

Resources

About