Scholarlink

<p>Despite advancements in assisted reproductive technology (ART), recurrent implantation failure (RIF) continues to pose a significant challenge to achieving pregnancy. We included 2,463 retrospective RIF patients with no gynecological and anatomical anomalies who were referred to a clinical immunologist and received targeted immunotherapies. Twenty-three variables were used to develop a deep learning (TabNet) model to predict live births. Statistical analyses were used to compare characteristics between live birth and implantation failure groups. Model performance was evaluated using a confusion matrix, the receiver operating characteristic (ROC) curve, and calibration plots. Our model showed an accuracy of 87.4% and an AUROC of 0.952. According to the model, when there were no missing input variables, the most important features were age, Th1/Th2 ratio, BMI, anti-thyroid peroxidase (anti-TPO), antinuclear antibodies (ANA), anti-dsDNA, and anti-tissue transglutaminase (anti-TTG), respectively. In conclusion, the TabNet model yielded strong performance in predicting live births in RIF patients using a combination of 23 variables. This model can help improve understanding of the underlying mechanism of implantation failure and stratify patients who may benefit from immune modulation interventions.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Immunological risk factors for recurrent implantation failure using a deep learning model: a multicenter retrospective cohort study

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月1日

查看全文
  • Mohsen Dashti,
  • Afsaneh Ghasemzadeh,
  • Sare Doustfateme,
  • Maryam Daraei,
  • Shahla Danaii,
  • Nazila Najdi,
  • Katayoun Berjis,
  • Javad Ahmadian Heris,
  • Forough Chakari-Khiavi,
  • Shima Karimi,
  • Shahrzad Rahimifar,
  • Sara Davoodi,
  • Sina Baharaghdam,
  • Nasim Bolouri,
  • Zahra Jafarisavari,
  • Reza Mousavi Ardehaie,
  • Arvin Amir,
  • Mehdi Yousefi

摘要

Despite advancements in assisted reproductive technology (ART), recurrent implantation failure (RIF) continues to pose a significant challenge to achieving pregnancy. We included 2,463 retrospective RIF patients with no gynecological and anatomical anomalies who were referred to a clinical immunologist and received targeted immunotherapies. Twenty-three variables were used to develop a deep learning (TabNet) model to predict live births. Statistical analyses were used to compare characteristics between live birth and implantation failure groups. Model performance was evaluated using a confusion matrix, the receiver operating characteristic (ROC) curve, and calibration plots. Our model showed an accuracy of 87.4% and an AUROC of 0.952. According to the model, when there were no missing input variables, the most important features were age, Th1/Th2 ratio, BMI, anti-thyroid peroxidase (anti-TPO), antinuclear antibodies (ANA), anti-dsDNA, and anti-tissue transglutaminase (anti-TTG), respectively. In conclusion, the TabNet model yielded strong performance in predicting live births in RIF patients using a combination of 23 variables. This model can help improve understanding of the underlying mechanism of implantation failure and stratify patients who may benefit from immune modulation interventions.