Published: 04 July 2024
DOI: 10.1007/s10815-024-03178-7
Thi-My-Trang Luong1,2,3, Nguyen-Tuong Ho3,4, Yuh-Ming Hwu3, Shyr-Yeu Lin3, Jason Yen-Ping Ho3, Ruey-Sheng Wang3, Yi-Xuan Lee3, Shun-Jen Tan3 ,Yi-Rong Lee3, Yung-Ling Huang3, Yi-Ching Hsu3, Nguyen-Quoc-Khanh Le5,6,7,8, Chii-Ruey Tzeng9
Authors information
1 International Master Program in Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.
2 AIBioMed Research Group, Taipei Medical University, Taipei, Taiwan.
3 Taipei Fertility Centre, Taipei, Taiwan.
4 IVFMD, My Duc Hospital, Ho Chi Minh, Vietnam.
5 AIBioMed Research Group, Taipei Medical University, Taipei, Taiwan. khanhlee@tmu.edu.tw.
6 Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan. khanhlee@tmu.edu.tw.
7 Research Center for Artificial Intelligence in Medicine, Taipei Medical University, Taipei, Taiwan. khanhlee@tmu.edu.tw.
8 Translational Imaging Research Center, Taipei Medical University Hospital, Taipei, Taiwan. khanhlee@tmu.edu.tw. 9 Taipei Fertility Centre, Taipei, Taiwan. tzengcr@tmu.edu.tw.
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.
Keywords: Embryo selection; Explainable artificial intelligence; Machine learning; Ploidy prediction; Preimplantation genetic testing.