Background/Objectives: The integration of artificial intelligence (AI) in medical imaging is rapidly advancing, yet its application in gynecologic use remains limited. This proof-of-concept study presents the development and validation of a convolutional neural network (CNN) designed to automatically detect and classify endometrial polyps.Methods: A multicenter dataset (n = 3) comprising 65 hysteroscopies was used, yielding 33,239 frames and 37,512 annotated objects. Still frames were extracted from full-length videos and annotated for the presence of histologically confirmed polyps. A YOLOv1-based object detection model was used with a 70–20–10 split for training, validation, and testing. Primary performance metrics included recall, precision, and mean average precision at an intersection over union (IoU) ≥ 0.50 (mAP50). Frame-level classification metrics were also computed to evaluate clinical applicability.Results: The model achieved a recall of 0.96 and precision of 0.95 for polyp detection, with a mAP50 of 0.98. At the frame level, mean recall was 0.75, precision 0.98, and F1 score 0.82, confirming high detection and classification performance.Conclusions: This study presents a CNN trained on multicenter, real-world data that detects and classifies polyps simultaneously with high diagnostic and localization performance, supported by explainable AI features that enhance its clinical integration and technological readiness. Although currently limited to binary classification, this study demonstrates the feasibility and potential of AI to reduce diagnostic subjectivity and inter-observer variability in hysteroscopy. Future work will focus on expanding the model’s capabilities to classify a broader range of endometrial pathologies, enhance generalizability, and validate performance in real-time clinical settings.
**背景/目的：** 人工智能在医学影像领域的整合正快速发展，但其在妇科领域的应用仍有限。本研究为一项概念验证，旨在开发和验证一种用于自动检测和分类子宫内膜息肉的卷积神经网络。 **方法：** 研究使用了一个多中心数据集（n = 3），包含65例宫腔镜检查，共提取33,239帧图像，标注了37,512个目标对象。从完整视频中提取静态帧，并对经组织学证实的息肉存在情况进行标注。采用基于YOLOv1的目标检测模型，按70-20-10的比例划分训练集、验证集和测试集。主要性能指标包括召回率、精确率以及交并比≥0.50时的平均精度均值。同时计算了帧级分类指标以评估临床适用性。 **结果：** 模型在息肉检测方面实现了0.96的召回率和0.95的精确率，mAP50为0.98。在帧级别，平均召回率为0.75，精确率为0.98，F1分数为0.82，证实了其高检测和分类性能。 **结论：** 本研究提出了一种基于多中心真实世界数据训练的CNN模型，能够同时以高诊断和定位性能检测并分类息肉，并辅以可解释的人工智能特征，增强了其临床整合和技术成熟度。尽管目前仅限于二元分类，但本研究证明了人工智能在减少宫腔镜检查中诊断主观性和观察者间差异方面的可行性和潜力。未来的工作将集中于扩展模型能力以分类更广泛的子宫内膜病变、增强泛化性，并在实时临床环境中验证其性能。
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