Machine Learning-based Evaluation of Pregnancy Success Indicators in Assisted Reproductive Technology (ART) Cycles
试验速览
- 阶段
- 不适用
- 状态
- 进行中（未招募）
- 入组人数
- 5,000
- 试验地点
- 1
- 主要终点
- Pregnancy rate
研究概览
简要总结
Infertility, as defined by the World Health Organization (WHO), is a disorder of the male or female reproductive system characterized by the inability to achieve a clinical pregnancy after 12 months or more of regular, unprotected sexual intercourse. In modern fertility treatment, assisted reproductive technologies (ART), including in vitro fertilization (IVF), have become a standard approach for addressing complex fertility issues and sterility. In Italy, infertility affects approximately 16.5% of couples.
Despite advancements in ART, comparing the failure rates of pregnancies achieved through ART with those of spontaneous pregnancies in Italy reveals significant differences, particularly in terms of success rates, miscarriage rates, and embryo implantation outcomes.
In this context, AI-based models have shown promising potential in predicting IVF success by analyzing complex datasets that include patient demographics, hormonal levels, and embryo morphology. Research indicates that AI can enhance embryo selection, predict the optimal timing for embryo transfer, and advance personalized medicine approaches in reproductive health.
This study aims to use of Machine Learning to identify patterns and factors associated with successful pregnancy outcomes by analyzing large-scale, anonymized ART data. The resulting predictive model could enable clinicians to better personalize treatment protocols for each patient, optimizing medication dosages, timing, and embryo selection. It could also improve pregnancy success rates while reducing the emotional and financial burden on patients, thus advancing the standard of care in ART.
详细描述
This is a multicentric, observational, retrospective, non-profit study, coordinated by the IRCCS San Raffaele Hospital, aims to analyze anonymized data collected between 2019 and 2024 from approximately 5,000 couples undergoing Assisted Reproductive Technology (ART) procedures across three participating centers. The study will examine key variables, including age, medical history, treatment protocols, ART techniques (such as In Vitro Fertilization [IVF] and Intracytoplasmic Sperm Injection [ICSI]), embryo quality, and pregnancy outcomes, to develop a machine learning-based predictive model for pregnancy outcomes. The selected timeframe ensures a sufficiently large dataset to facilitate robust development and validation of the predictive model.
By leveraging machine learning techniques, this study aims to enhance the accuracy of pregnancy outcome predictions, thereby improving patient counseling and treatment planning in ART procedures. The comprehensive dataset, encompassing a diverse range of variables and a substantial number of cases, will provide a robust foundation for developing a predictive model with high clinical applicability.
The primary objective of this study is to develop a Machine Learning-based predictive model for pregnancy outcomes in assisted reproductive technologies (ART), by analyzing large-scale, anonymized data, for scientific research purposes. The model aims to identify key patterns and factors that correlate with successful pregnancy outcomes to optimize individualized treatment protocols for patients undergoing ART.
SAMPLE SIZE:
The sample size will be approximately 5,000 pairs of subjects (women + men) based on the total number of ART cycles recorded at the participating centers during this period and the number of patients with complete data records that provide sufficient information for analysis. We expect approximately 1650 pairs for the class "success" and 3350 for the class "unsuccess" of the IVF treatment. Thus, the Machine Learning-based predictive model could be trained using a multi-parametric approach with a balanced set of 1350 pairs of subjects, using the remaining couples of subjects to test the performance of the model.
研究设计
- 研究类型
- Observational
- 观察模型
- Other
- 时间视角
- Retrospective
入排标准
- 年龄范围
- 18 Years 至 43 Years（Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Patients who underwent ART procedures, including IVF and ICSI, between 2019 and
- •Women aged between 18 and 43 years.
排除标准
- •Patiens with incomplete or missing data records that do not provide sufficient information for analysis.
- •women outside the 18 to 43 age range
结局指标
主要结局
Pregnancy rate
时间窗: Data will be extracted for all ART cycles conducted between 2019 and 2024 to allow for the comprehensive development of the Machine Learning-based model.
The primary endpoint of the study will be the clinical pregnancy defined as a pregnancy confirmed by an increasing level of hCG and the presence of a gestational sac or heartbeat detected by ultrasound.
次要结局
未报告次要终点