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AI-Based Optimal Treatment Strategy Selection for Female Infertility for First and Subsequent IVF-ET Cycles

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Abstract

Over the last 20 years, China’s infertility rate has risen from 3% to 12.5%–15%. Infertility has become the third largest disease following cancer and cardiovascular disease. Then, the in vitro fertilization and embryo transfer (IVF-ET) becomes more and more important in infertility treatment field. However, the reported success rate for IVT-ET is 30%–40% and costs are gradually rising. Meanwhile, to increase success rates and decrease costs, the optimal selection of the IVF-ET treatment strategy is crucial. In a clinical work, the IVF-ET treatment strategy selection is always based on the experience of the doctor without a uniform standard. To solve this important and complex problem, we proposed an artificial intelligence (AI)-based optimal treatment strategy selection system to extract implicit knowledge from clinical data for new and returning patients, by mimicking the IVF-ET process and analysing a myriad of treatment decisions. We demonstrated that the performance of the model was different in 10 AI classification algorithms. Hence, we need to select the optimal method for predicting patient pregnancy result in different IVF-ET treatment strategies. Moreover, feature ranking is determined in the proposed model to measure the importance of each patient characteristics. Therefore, better advice can be provided for individual patient characteristics, doctors can provide more valid suggestions regarding certain patient characteristics to improve the accuracy of diagnosis and efficiency.

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Data availability

There are restrictions to the availability of our data, as our data is the property of Tongji Hospital and we are not authorized to disclose it. Data that does not involve confidential patient information is available from the corresponding author upon reasonable request.

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Acknowledgements

We thank all staff of the Reproductive Medicine Center of Tongji Hospital, Wuhan, Hubei, China, for their support and cooperation.

Funding

We gratefully acknowledge all staff of the Reproductive Medicine Center of Tongji Hospital for their support and cooperation. This work was supported by the National Natural Science Foundation of China (71871169;U1933120), the Chinese Medical Association of Clinical Medicine special funds for scientific research projects (17020400709), the Hubei Provincial Natural Science Foundation of China (2019CFA062), and Open Fund of State Key Laboratory of Reproductive Medicine (SKLRM-K201802).

Author information

Authors and Affiliations

  1. Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, PR China

    Renjie Wang, Lei Jin & Shujie Liao

  2. School of Applied Economics, Renmin University of China, Beijing, 100872, China

    Wei Pan & Li Wen

  3. Business School, Sichuan University, Chengdu, 610064, China

    Lean Yu

  4. School of Information Management, Jiangxi University of Finance and Economics, Nanchang, 330032, China

    Xiaoming Zhang

  5. School of Economic and Management, Wuhan University, Wuhan, 430072, China

    Wulin Pan & Cheng Hu

Authors
  1. Renjie Wang
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  3. Lean Yu
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  5. Wulin Pan
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  8. Lei Jin
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  9. Shujie Liao
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Contributions

Wei Pan, Lean Yu, Lei Jin, and Shujie Liao contributed to study design. Renjie Wang, Wulin Pan, Cheng Hu, and Xiaoming Zhang collected and analysed the data. Cheng Hu, Xiaoming Zhang, Li Wen, and Renjie Wang interpreted results. Wei Pan, Lean Yu, Lei Jin, and Shujie Liao wrote the manuscript. Revision of the manuscript and the final approval of the version to be published: all authors.

Corresponding authors

Correspondence to Wei Pan, Lean Yu, Xiaoming Zhang, Cheng Hu, Lei Jin or Shujie Liao.

Ethics declarations

Ethics approval

 The study was approved by the Ethics Committee of Tongji Hospital, Tongji Medical College of Huazhong University of Science and Technology. The procedures used in this study adhere to the tenets of the Declaration of Helsinki. According to the Institutional Review Board (IRB) of Tongji Hospital, our study was not subjected to ethics review, because all women participating in the study received routine IVF–ET treatment in the hospital and no additional intervention or sampling was performed, as described by Pan et al. [32].

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The authors have no conflicts of interest to report.

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Cite this article

Wang, R., Pan, W., Yu, L. et al. AI-Based Optimal Treatment Strategy Selection for Female Infertility for First and Subsequent IVF-ET Cycles. J Med Syst 47, 87 (2023). https://doi.org/10.1007/s10916-023-01967-8

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