End-to-end deep learning for recognition of ploidy status using time-lapse videos

Chun I. Lee, Yan Ru Su, Chien Hong Chen, T. Arthur Chang, Esther En Shu Kuo, Wei Lin Zheng, Wen Ting Hsieh, Chun Chia Huang, Maw Sheng Lee, Mark Liu

Research output: Contribution to journalArticlepeer-review

Abstract

Purpose: Our retrospective study is to investigate an end-to-end deep learning model in identifying ploidy status through raw time-lapse video. Methods: By randomly dividing the dataset of time-lapse videos with known outcome of preimplantation genetic testing for aneuploidy (PGT-A), a deep learning model on raw videos was trained by the 80% dataset, and used to test the remaining 20%, by feeding time-lapse videos as input and the PGT-A prediction as output. The performance was measured by an average area under the curve (AUC) of the receiver operating characteristic curve. Result(s): With 690 sets of time-lapse video image, combined with PGT-A results, our deep learning model has achieved an AUC of 0.74 from the test dataset (138 videos), in discriminating between aneuploid embryos (group 1) and others (group 2, including euploid and mosaic embryos). Conclusion: Our model demonstrated a proof of concept and potential in recognizing the ploidy status of tested embryos. A larger scale and further optimization on the exclusion criteria would be included in our future investigation, as well as prospective approach.

Original languageEnglish (US)
Pages (from-to)1655-1663
Number of pages9
JournalJournal of Assisted Reproduction and Genetics
Volume38
Issue number7
DOIs
StatePublished - Jul 2021

Keywords

  • Deep learning
  • Ploidy status
  • Preimplantation genetic testing for aneuploidy (PGT-A)
  • Time-lapse

ASJC Scopus subject areas

  • Reproductive Medicine
  • Genetics
  • Obstetrics and Gynecology
  • Developmental Biology
  • Genetics(clinical)

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