Beyond black-box models: explainable AI for embryo ploidy prediction and patient-centric consultation.

Journal: Journal of assisted reproduction and genetics
PMID: 38963605

Abstract

PURPOSE: To determine if an explainable artificial intelligence (XAI) model enhances the accuracy and transparency of predicting embryo ploidy status based on embryonic characteristics and clinical data.

Authors

  • Thi-My-Trang Luong
    International Master Program in Medicine, College of Medicine, Taipei Medical University, Taipei, 110, Taiwan.
  • Nguyen-Tuong Ho
    Taipei Fertility Centre, Taipei, Taiwan.
  • Yuh-Ming Hwu
    Taipei Fertility Centre, Taipei, Taiwan.
  • Shyr-Yeu Lin
    Taipei Fertility Centre, Taipei, Taiwan.
  • Jason Yen-Ping Ho
    Taipei Fertility Centre, Taipei, Taiwan.
  • Ruey-Sheng Wang
    Taipei Fertility Centre, Taipei, Taiwan.
  • Yi-Xuan Lee
    Taipei Fertility Centre, Taipei, Taiwan.
  • Shun-Jen Tan
    Taipei Fertility Centre, Taipei, Taiwan.
  • Yi-Rong Lee
    Taipei Fertility Centre, Taipei, Taiwan.
  • Yung-Ling Huang
    Taipei Fertility Centre, Taipei, Taiwan.
  • Yi-Ching Hsu
    Taipei Fertility Centre, Taipei, Taiwan.
  • Nguyen-Quoc-Khanh Le
    Department of Computer Science and Engineering, Yuan Ze University, Chung-Li, 32003, Taiwan. Electronic address: khanhlee87@gmail.com.
  • Chii-Ruey Tzeng
    Taipei Fertility Centre, Taipei, Taiwan. tzengcr@tmu.edu.tw.

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