Time will tell: time-lapse technology and artificial intelligence to set time cut-offs indicating embryo incompetence.

Abstract

Study question: Can more reliable time cut-offs of embryo developmental incompetence be generated by combining time-lapse technology (TLT), artificial intelligence, and preimplantation genetics screening for aneuploidy (PGT-A)?

Summary answer: Embryo developmental incompetence can be better predicted by time cut-offs at multiple developmental stages and for different ranges of maternal age.

What is known already: TLT is instrumental for the continual and undisturbed observation of embryo development. It has produced morphokinetic algorithms aimed at selecting embryos able to generate a viable pregnancy, however, such efforts have had limited success. Regardless, the potential of this technology for improving multiple aspects of the IVF process remains considerable. Specifically, TLT could be harnessed to discriminate developmentally incompetent embryos: i.e. those unable to develop to the blastocyst stage or affected by full-chromosome meiotic aneuploidies. If proven valuable, this application would prevent the non-productive use of such embryos, thereby improving laboratory and clinical efficiency and reducing patient stress and costs due to unnecessary embryo transfer and cryopreservation.

Study design, size, duration: The training dataset involved embryos of PGT-A cycles cultured in Embryoscope with a single media (836 euploid and 1179 aneuploid blastocysts and 1874 arrested embryos; 2013-2020). Selection criteria were ejaculated sperm, own (not donated) fresh oocytes, trophectoderm biopsy and comprehensive-chromosome-testing to diagnose uniform aneuploidies. Out-of-sample (30% of training), internal (299 euploid and 490 aneuploid blastocysts and 680 arrested embryos; 2021-2022) and external (97 euploid, 110 aneuploid and 603 untested blastocysts and 514 arrested embryos, 2018 to early 2022) validations were conducted.

Participants/materials, setting, methods: A training dataset (70%) was used to define thresholds. Several models were generated by fitting outcomes to each timing (tPNa-t8) and maternal age. ROC curves pinpointed in-sample classification values associated with 95%, 99% and 99.99% true-positive rate for predicting incompetence. These values were integrated with upper limits of maternal age ranges (<35, 35-37, 38-40, 41-42, and >42 years) in logit functions to identify time cut-offs, whose accuracy was tested on the validation datasets through confusion matrices.

Main results and the role of chance: For developmental (in)competence, the best performing (i) tPNa cut-offs were 27.8 hpi (error-rate: 0/743), 32.6 hpi (error rate: 0/934), 26.8 hpi (error rate: 0/1178), 22.9 hpi (error-rate: 1/654, 0.1%) and 17.2 hpi (error rate: 4/423, 0.9%) in the <35, 35-37, 38-40, 41-42, and >42 years groups, respectively; (ii) tPNf cut-offs were 36.7 hpi (error rate: 0/738), 47.9 hpi (error rate: 0/921), 45.6 hpi (error rate: 1/1156, 0.1%), 44.1 hpi (error rate: 0/647) and 41.8 hpi (error rate: 0/417); (iii) t2 cut-offs were 50.9 hpi (error rate: 0/724), 49 hpi (error rate: 0/915), 47.1 hpi (error rate: 0/1146), 45.8 hpi (error rate: 0/636) and 43.9 hpi (error rate: 0/416); (iv) t4 cut-offs were 66.9 hpi (error rate: 0/683), 80.7 hpi (error rate: 0/836), 77.1 hpi (error rate: 0/1063), 74.7 hpi (error rate: 0/590) and 71.2 hpi (error rate: 0/389); and (v) t8 cut-offs were 118.1 hpi (error rate: 0/619), 110.6 hpi (error rate: 0/772), 140 hpi (error rate: 0/969), 135 hpi (error rate: 0/533) and 127.5 hpi (error rate: 0/355). tPNf and t2 showed a significant association with chromosomal (in)competence, also when adjusted for maternal age. Nevertheless, the relevant cut-offs were found to perform less well and were redundant compared with the blastocyst development cut-offs.

Limitations, reasons for caution: Study limits are its retrospective design and the datasets being unbalanced towards advanced maternal age cases. The potential effects of abnormal cleavage patterns were not assessed. Larger sample sizes and external validations in other clinical settings are warranted.

Wider implications of the findings: If confirmed by independent studies, this approach could significantly improve the efficiency of ART, by reducing the workload and patient impacts (extended culture and cleavage stage cryopreservation or transfer) associated with embryos that ultimately are developmentally incompetent and should not be considered for treatment. Pending validation, these data might be applied also in static embryo observation settings.

Study funding/competing interest(s): This study was supported by the participating institutions. The authors have no conflicts of interest to declare.

Trial registration number: N/A.