目的:开发一种多模式学习应用系统,该系统集成了电子病历(EMR)和宫腔镜图像,用于子宫内膜损伤导致的宫腔粘连(IUA)患者的生殖结局预测和风险分层。
方法:从我们建立的多中心IUA数据库中,对753例宫腔镜粘连松解术后患者的EMR和5014再次观察宫腔镜图像进行了随机分配,验证,和测试数据集。各自的数据集用于模型开发,调谐,和多模态学习应用程序的测试。MobilenetV3用于图像特征提取,和XGBoost用于EMR和图像特征集成学习。将应用程序的性能与单模态方法(EMR或宫腔镜图像)进行比较,DeepSurv和ElasticNet模型,以及临床评分系统。主要结果是1年受孕预测的准确性,次要结局是风险分层后的辅助生殖技术(ART)获益比.
结果:多模式学习系统在1年内预测受孕方面表现出优异的性能,曲线下面积为0.967(95%CI:0.950-0.985),0.936(95%CI:0.883-0.989),和0.965(95%CI:0.935-0.994)在训练中,验证,和测试数据集,分别,超越单模态方法,其他模型和临床评分系统(均P<0.05)。该模型的应用在宫腔镜平台上无缝运行,平均分析时间为每名患者3.7±0.8s。通过采用应用程序的概念基于概率的风险分层,中高危患者显示出显著的ART获益(比值比=6,95%CI:1.27-27.8,P=0.02),而低风险患者表现出良好的自然受孕潜力,ART治疗的受胎率没有显着增加(P=1)。
结论:使用宫腔镜图像和EMR的多模式学习系统在准确预测IUA患者的自然受孕并提供有效的术后分层方面显示出希望。可能有助于IUA手术后的ART分诊。
OBJECTIVE: To develop a multimodal learning application system that integrates electronic medical records (EMR) and hysteroscopic images for reproductive outcome prediction and risk stratification of patients with intrauterine adhesions (IUAs) resulting from endometrial injuries.
METHODS: EMR and 5014 revisited hysteroscopic images of 753 post hysteroscopic adhesiolysis patients from the multicenter IUA database we established were randomly allocated to training, validation, and test datasets. The respective datasets were used for model development, tuning, and testing of the multimodal learning application. MobilenetV3 was employed for image feature extraction, and XGBoost for EMR and image feature ensemble learning. The performance of the application was compared against the single-modal approaches (EMR or hysteroscopic images), DeepSurv and ElasticNet models, along with the clinical scoring systems. The primary outcome was the 1-year conception prediction accuracy, and the secondary outcome was the assisted reproductive technology (ART) benefit ratio after risk stratification.
RESULTS: The multimodal learning system exhibited superior performance in predicting conception within 1-year, achieving areas under the curves of 0.967 (95% CI: 0.950-0.985), 0.936 (95% CI: 0.883-0.989), and 0.965 (95% CI: 0.935-0.994) in the training, validation, and test datasets, respectively, surpassing single-modal approaches, other models and clinical scoring systems (all P<0.05). The application of the model operated seamlessly on the hysteroscopic platform, with an average analysis time of 3.7±0.8 s per patient. By employing the application\'s conception probability-based risk stratification, mid-high-risk patients demonstrated a significant ART benefit (odds ratio=6, 95% CI: 1.27-27.8, P=0.02), while low-risk patients exhibited good natural conception potential, with no significant increase in conception rates from ART treatment (P=1).
CONCLUSIONS: The multimodal learning system using hysteroscopic images and EMR demonstrates promise in accurately predicting the natural conception of patients with IUAs and providing effective postoperative stratification, potentially contributing to ART triage after IUA procedures.