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. 2024 May 22;22(1):58.
doi: 10.1186/s12958-024-01230-w.

Enhancing clinical utility: deep learning-based embryo scoring model for non-invasive aneuploidy prediction

Bing-Xin Ma #  1 , Guang-Nian Zhao #  2   3 , Zhi-Fei Yi  1 , Yong-Le Yang  1 , Lei Jin  4 , Bo Huang  5
Affiliations

Enhancing clinical utility: deep learning-based embryo scoring model for non-invasive aneuploidy prediction

Bing-Xin Ma et al. Reprod Biol Endocrinol. .

Abstract

Background: The best method for selecting embryos ploidy is preimplantation genetic testing for aneuploidies (PGT-A). However, it takes more labour, money, and experience. As such, more approachable, non- invasive techniques were still needed. Analyses driven by artificial intelligence have been presented recently to automate and objectify picture assessments.

Methods: In present retrospective study, a total of 3448 biopsied blastocysts from 979 Time-lapse (TL)-PGT cycles were retrospectively analyzed. The "intelligent data analysis (iDA) Score" as a deep learning algorithm was used in TL incubators and assigned each blastocyst with a score between 1.0 and 9.9.

Results: Significant differences were observed in iDAScore among blastocysts with different ploidy. Additionally, multivariate logistic regression analysis showed that higher scores were significantly correlated with euploidy (p < 0.001). The Area Under the Curve (AUC) of iDAScore alone for predicting euploidy embryo is 0.612, but rose to 0.688 by adding clinical and embryonic characteristics.

Conclusions: This study provided additional information to strengthen the clinical applicability of iDAScore. This may provide a non-invasive and inexpensive alternative for patients who have no available blastocyst for biopsy or who are economically disadvantaged. However, the accuracy of embryo ploidy is still dependent on the results of next-generation sequencing technology (NGS) analysis.

Keywords: Artificial intelligence; Embryo ploidy; Time-lapse system; iDAScore.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Schematic presentation of the study design. TL, time-lapse; PGT, preimplantation genetic testing
Fig. 2
Fig. 2
The ratio of blastocysts with different ploidy. (A) The ploidy of all blastocysts. (B) The ploidy of blastocysts with different length of incubation. (C) The ploidy of blastocysts with different iDAScores. *** presents p < 0.001
Fig. 3
Fig. 3
iDAScores of blastocysts with different ploidy. (A) iDAScores of all biopsied blastocysts. (B) iDAScores of all Day5 biopsied blastocysts. (C) iDAScores of all Day6 biopsied blastocysts. (D) iDAScores of different abnormal chromosome number. (E) iDAScores of different abnormal chromosome type. *** presents p < 0.001
Fig. 4
Fig. 4
The AUC of iDAScore on euploidy prediction.AUC, Area Under the Curve
See this image and copyright information in PMC

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