How much information about embryo implantation potential is included in morphokinetic data? A prediction model based on artificial neural networks and principal component analysis

Milewski, Robert; Kuczyńska, Agnieszka; Stankiewicz, Bożena; Kuczyński, Waldemar

doi:10.1016/j.advms.2017.02.001

Cited by 40 publications

(30 citation statements)

References 27 publications

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“…Durairaj and Nandhakumar [30] suggested an integrated methodology of ANN with data mining techniques and the experimental model exhibited an overall accuracy of 90%, thus proposed the applied techniques for finding the minimum set of influential parameters in order to predict a success rate of IVF. In another approach on AI utilization, Milewski et al [32] revisited ANNs by combining embryo morphokinetic data to determine embryo implantation potential and the model presented was able to correctly predict approximately 70% of pregnancies, although no other variables were utilized. In a previous theoretical evaluation of the usefulness of AI systems for personalized management in ART, we proposed an ANN with the respective parameters to be included in the analysis and later construction of the system [34].…”

Section: Discussionmentioning

confidence: 99%

“…These appear advantageous due to their remarkable information-processing characteristics pertinent mainly to nonlinearity, high level of parallelism, noise and fault tolerance, and learning, generalization, and selfadapting capabilities [19]. The first ANN constructed with application on assisted reproduction was proposed by Kauffman et al in 1997 [20] with onwards reporting of similar systems developed with AI targeting on various aspects of ART, in an attempt to predict clinical outcomes and especially live birth, but with varying input variables and predictive power [21][22][23][24][25][26][27][28][29][30][31][32].…”

Section: Introductionmentioning

confidence: 99%

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An artificial neural network for the prediction of assisted reproduction outcome

Vogiatzi

Pouliakis

Siristatidis

2019

J Assist Reprod Genet

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Purpose To construct and validate an efficient artificial neural network (ANN) based on parameters with statistical correlation to live birth, to be used as a comprehensive tool for the prediction of the clinical outcome for patients undergoing ART. Methods Data from 257 infertile couples that underwent a total of 426 IVF/ICSI cycles from 2010 to 2017 was collected on an ensemble of 118 parameters for each cycle. Statistical correlation of the parameters with the outcome of live birth was performed, using either t test or χ 2 test, and the parameters that demonstrated statistical significance were used to construct the ANN. Crossvalidation was performed by random separation of data and repeating the training-testing procedure by 10 times. Results 12 statistically significant parameters out of the initial ensemble were used for the ANN construction, which exhibited a cumulative sensitivity and specificity of 76.7% and 73.4%, respectively. During cross-validation, the system exhibited the following: sensitivity 69.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An artificial neural network for the prediction of assisted reproduction outcome

Vogiatzi

Pouliakis

Siristatidis

2019

J Assist Reprod Genet

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“…In contrast to other methods, unsupervised learning and deep learning in health are still developing; their applications in the field of reproduction have been scarce so far (Miotto et al 2018). Milewski et al (2017) used PCA and ANN to create a model for implantation prediction based on the morphokinetic information recordings of 610 human embryos transferred in 514 cycles. The combination algorithm was efficient with the AUC as 75%.…”

Section: Overview Of the Ai In Reproductive Medicinementioning

confidence: 99%

Artificial intelligence in reproductive medicine

et al. 2019

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Artificial intelligence (AI) has experienced rapid growth over the past few years, moving from the experimental to the implementation phase in various fields, including medicine. Advances in learning algorithms and theories, the availability of large datasets and improvements in computing power have contributed to breakthroughs in current AI applications. Machine learning (ML), a subset of AI, allows computers to detect patterns from large complex datasets automatically and uses these patterns to make predictions. AI is proving to be increasingly applicable to healthcare, and multiple machine learning techniques have been used to improve the performance of assisted reproductive technology (ART). Despite various challenges, the integration of AI and reproductive medicine is bound to give an essential direction to medical development in the future. In this review, we discuss the basic aspects of AI and machine learning, and we address the applications, potential limitations and challenges of AI. We also highlight the prospects and future directions in the context of reproductive medicine.

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“…Importantly, the algorithm was validated on an independent data set and the obtained results were very similar (AUC = 0.70, 95% CI: 0.59-0.80), proving that it is reliable and applicable in clinical practice (Milewski et al 2016a). t2-t5 division timings, s2 and cc2 were also used, this time together with morphological parameters such as fragmentation, multinucleation, blastomere size at t2 and t4, and female age, in a model applying the artificial neural network method (Milewski et al 2017)…”

Section: R43mentioning

confidence: 99%

“…artificial neural networks), allowing more extensive utilization of the time-lapse data. Indeed, current biomedical research indicates that application of the artificial neural network method can maximize the predictive power of reproductive algorithms (Milewski et al 2009(Milewski et al , 2017.…”

Section: Challenges and Perspectives Of The Morphokinetic Modelsmentioning

confidence: 99%

Time-lapse imaging of cleavage divisions in embryo quality assessment

Milewski¹,

Ajduk²

2017

Reproduction

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fertilization (IVF) is one of the most important procedures for treating infertility. As several embryos are usually produced in a single IVF cycle, it is crucial to select only the most viable ones for transfer to the patient. Morphokinetics, i.e. analysis of the dynamics of cleavage divisions and processes such as compaction and cavitation, has provided both biologists and clinicians with a new set of data regarding embryonic behaviour during preimplantation development and its association with embryo quality. In the current review, we focus on biological significance of morphokinetic parameters and show how they can be used to predict a reproductive outcome. We also explain the statistics behind the predictive algorithms and discuss the future perspectives of morphokinetics.

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How much information about embryo implantation potential is included in morphokinetic data? A prediction model based on artificial neural networks and principal component analysis

Cited by 40 publications

References 27 publications

An artificial neural network for the prediction of assisted reproduction outcome

An artificial neural network for the prediction of assisted reproduction outcome

Artificial intelligence in reproductive medicine

Time-lapse imaging of cleavage divisions in embryo quality assessment

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