https://doi.org/10.1007/s10815-024-03178-7
Published: 04 July 2024
Thi-My-Trang Luong, Nguyen-Tuong Ho, Yuh-Ming Hwu, Shyr-Yeu Lin, Jason Yen-Ping Ho, Ruey-Sheng Wang, Yi-Xuan Lee, Shun-Jen Tan, Yi-Rong Lee, Yung-Ling Huang, Yi-Ching Hsu, Nguyen-Quoc-Khanh Le & Chii-Ruey Tzeng
Authors information
International Master Program in Medicine, College of Medicine, Taipei Medical University, Taipei, TaiwanThi-My-Trang Luong
AIBioMed Research Group, Taipei Medical University, Taipei, TaiwanThi-My-Trang Luong & Nguyen-Quoc-Khanh Le
Taipei Fertility Centre, Taipei, TaiwanThi-My-Trang Luong, Nguyen-Tuong Ho, Yuh-Ming Hwu, Shyr-Yeu Lin, Jason Yen-Ping Ho, Ruey-Sheng Wang, Yi-Xuan Lee, Shun-Jen Tan, Yi-Rong Lee, Yung-Ling Huang, Yi-Ching Hsu & Chii-Ruey Tzeng
IVFMD, My Duc Hospital, Ho Chi Minh, VietnamNguyen-Tuong Ho
Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei, TaiwanNguyen-Quoc-Khanh Le
Research Center for Artificial Intelligence in Medicine, Taipei Medical University, Taipei, TaiwanNguyen-Quoc-Khanh Le
Translational Imaging Research Center, Taipei Medical University Hospital, Taipei, TaiwanNguyen-Quoc-Khanh Le
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.
Methods
This retrospective study utilized a dataset of 1908 blastocyst embryos. The dataset includes ploidy status, morphokinetic features, morphology grades, and 11 clinical variables. Six machine learning (ML) models including Random Forest (RF), Linear Discriminant Analysis (LDA), Logistic Regression (LR), Support Vector Machine (SVM), AdaBoost (ADA), and Light Gradient-Boosting Machine (LGBM) were trained to predict ploidy status probabilities across three distinct datasets: high-grade embryos (HGE, n = 1107), low-grade embryos (LGE, n = 364), and all-grade embryos (AGE, n = 1471). The model’s performance was interpreted using XAI, including SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) techniques.
Results
The mean maternal age was 38.5 ± 3.85 years. The Random Forest (RF) model exhibited superior performance compared to the other five ML models, achieving an accuracy of 0.749 and an AUC of 0.808 for AGE. In the external test set, the RF model achieved an accuracy of 0.714 and an AUC of 0.750 (95% CI, 0.702–0.796). SHAP’s feature impact analysis highlighted that maternal age, paternal age, time to blastocyst (tB), and day 5 morphology grade significantly impacted the predictive model. In addition, LIME offered specific case-ploidy prediction probabilities, revealing the model’s assigned values for each variable within a finite range.
Conclusion
The model highlights the potential of using XAI algorithms to enhance ploidy prediction, optimize embryo selection as patient-centric consultation, and provides reliability and transparent insights into the decision-making process.