Reproductive biomedicine online · 2024
An explainable ultrasound-based machine learning model for predicting reproductive outcomes after frozen embryo transfer.
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Xu Fangfang, Ma Qianqing, Lai Penghao, Hu Lili, Gao Chuanfen, Xu Qianhua, Fang Youyan, Guo Yixuan, Yao Wen, Zhang Chaoxue
Аннотация
RESEARCH QUESTION: Can an optimal machine learning model be developed to predict reproductive outcomes following frozen embryo transfer (FET)?
DESIGN: This prospective study included 787 infertile females who underwent FET. The participants were split into a training cohort (n = 550) and a test cohort (n = 237) at a ratio of seven to three. Radiomics features were extracted from ultrasound images of the endometrium and junctional zone. A radiomics model was developed to generate the radiomics score (rad score). Logistic regression was applied to process the clinical data and create a clinical model. A fusion machine learning model was developed by integrating the rad score with independent clinical data using the XGboost algorithm. The performance of the models was compared using the area under the receiver operating characteristic curve (AUC). The SHapley Additive exPlanations (SHAP) method was used to interpret and visualize the contributions of features to the outcomes of FET.
RESULTS: The fusion model demonstrated superior performance, as indicated by an AUC of 0.861 (95% CI 0.829-0.890), in the training cohort, surpassing both the clinical model (AUC 0.680, 95% CI 0.635-0.722; P < 0.001) and the radiomics model (AUC 0.814, 95% CI 0.777-0.848; P < 0.001). The SHAP summary plot reveals the impacts of each feature on the predictive model, and the rad score was found to be the main feature. SHAP force plots provided explanations at the individual level.
CONCLUSION: An explainable machine learning model was established utilizing clinical data and ultrasound images to forecast the outcomes of FET. By utilizing the SHAP method, clinicians may better comprehend the contributors to the outcomes of FET in individual patients, and make better decisions before FET.
DESIGN: This prospective study included 787 infertile females who underwent FET. The participants were split into a training cohort (n = 550) and a test cohort (n = 237) at a ratio of seven to three. Radiomics features were extracted from ultrasound images of the endometrium and junctional zone. A radiomics model was developed to generate the radiomics score (rad score). Logistic regression was applied to process the clinical data and create a clinical model. A fusion machine learning model was developed by integrating the rad score with independent clinical data using the XGboost algorithm. The performance of the models was compared using the area under the receiver operating characteristic curve (AUC). The SHapley Additive exPlanations (SHAP) method was used to interpret and visualize the contributions of features to the outcomes of FET.
RESULTS: The fusion model demonstrated superior performance, as indicated by an AUC of 0.861 (95% CI 0.829-0.890), in the training cohort, surpassing both the clinical model (AUC 0.680, 95% CI 0.635-0.722; P < 0.001) and the radiomics model (AUC 0.814, 95% CI 0.777-0.848; P < 0.001). The SHAP summary plot reveals the impacts of each feature on the predictive model, and the rad score was found to be the main feature. SHAP force plots provided explanations at the individual level.
CONCLUSION: An explainable machine learning model was established utilizing clinical data and ultrasound images to forecast the outcomes of FET. By utilizing the SHAP method, clinicians may better comprehend the contributors to the outcomes of FET in individual patients, and make better decisions before FET.