{"id":115,"title":"Machine learning prediction of IVF outcomes in unexplained infertility","title_zh":"机器学习预测不明原因不孕患者IVF结局的探索性研究","url":"https://pubmed.ncbi.nlm.nih.gov/42969655","doi":"10.4274/tjod.galenos.2026.10663","pmid":"42969655","journal":"Turk J Obstet Gynecol","kind":"paper","lang":"en","published_at":"2026-11-02T00:00:00+00:00","fetched_at":"2026-11-08T03:43:53+00:00","authors":["Kaya Y","Aydın Y","Kaya C","Tahta T","Çelik Ö","Özkaya Karabıçak Ş"],"primary_topic":"art-clinical","topics":[{"slug":"art-clinical","score":12.0,"hits":["clinical pregnancy","gonadotropin","in vitro fertilization","infertility","ivf","live birth"]},{"slug":"ai-compute","score":5.0,"hits":["artificial intelligence","machine learning"]},{"slug":"male","score":1.0,"hits":["sperm"]}],"topic_score":12.0,"preference_score":0.0,"preference_labels":[],"llm_score":60.0,"final_score":60.0,"score_dims":{"novelty":65,"rigor":45,"impact":50,"relevance":90,"translation":55},"recommend_reason":"可作为AI辅助生殖结局预测的早期探索阅读，但样本量小、单中心、无外部验证且以临床妊娠为终点，临床转化价值有限。","keywords":["机器学习","不明原因不孕","体外受精","临床妊娠","预测模型","LGBM"],"selected":1,"story_id":206,"llm_provider":"deepseek","llm_model":"deepseek-flash","llm_effort":"low","source_key":"pubmed-art","source_name":"PubMed · 辅助生殖临床","source_tier":1,"mention_count":1,"has_interpretation":false,"journal_key":null,"journal_tier":"unknown","journal_if":null,"is_mock":false,"summary_zh":"该回顾性单中心研究纳入145对不明原因不孕夫妇，采用五种机器学习算法预测IVF临床妊娠结局。LGBM模型表现最佳，测试集ROC-AUC为0.712、准确率65.2%。置换特征重要性提示促性腺激素总剂量、初始剂量、精子总数、前向运动精子数及不孕年限较重要。作者强调样本小、无外部验证且缺少活产数据，结果仅为探索性，尚不能用于临床决策。","summary":"OBJECTIVE: To develop and evaluate machine learning (ML) models to predict clinical pregnancy (CP) outcomes in couples with unexplained infertility (UI) undergoing in vitro fertilization (IVF), thereby enhancing early treatment decision-making and patient counseling. MATERIALS AND METHODS: A retrospective analysis was conducted on 145 couples diagnosed with UI and treated at a single-center IVF clinic. Live birth data were not available; therefore, CP was used as the primary outcome. Demographic, clinical, and laboratory parameters were collected, and five ML algorithms-light gradient boosting machine (LGBM), random forest, logistic regression, extra trees classifier, and eXtreme gradient boosting-were applied to predict CP outcomes. Model performance was assessed using the area under the receiver operating characteristic curve (ROC-AUC), accuracy, sensitivity, and specificity. Permutation-based feature importance analysis was performed to identify key predictive variables. RESULTS: Among the evaluated models, the LGBM model achieved the highest test-set ROC-AUC (0.712) and an accuracy of 65.2%. Given the small test sample and the lack of external validation, these findings should be interpreted as exploratory. Variables receiving the highest permutation feature-importance scores included total gonadotropin dose, initial gonadotropin dose, total sperm count, progressive motile sperm count, and infertility duration. These findings should be interpreted as exploratory and do not establish that these variables are independent predictors of CP. CONCLUSION: This study presents the first ML-based approach tailored to predicting CP outcomes in couples with UI undergoing IVF. The results highlight the potential of artificial intelligence to inform personalized reproductive care. These exploratory findings demonstrate the feasibility of applying ML methods in UI. Larger prospective multicenter studies are required to determine whether clinically useful prediction models can be developed.","interpretation_basis":null,"fulltext_status":null,"interpretation":null,"fulltext":{"available":false,"status":null,"reason":"尚未抓取全文（未入选、分数未达阈值，或还没跑 fulltext 步骤）","pmcid":null,"license":null,"chars":0,"source":null,"fetched_at":null}}