目的 构建基于YOLOv11m深度学习框架的腹腔镜胃袖状切除联合胃底折叠术（LSGFD）同期实施食管裂孔疝修补术（HHR）一体化手术场景的计算机视觉人工智能模型，并进行独立验证，旨在为未来手术导航及机器人半自动化手术系统的研发提供视觉模块支持。 方法 回顾性分析新疆维吾尔自治区人民医院微创、疝和腹壁外科及和田地区人民医院肝胆外科行LSGFD联合HHR的手术视频资料。经筛选及处理后获得3180张样本图片，通过实例分割进行标注。采用分层随机划分法进行数据集分配，首先从总样本中随机抽取10%的样本作为独立测试集，随后从剩余 90% 的样本中进一步随机抽取20%的样本作为验证集，剩余部分则全部作为训练集。调用YOLOv11m深度学习的权重文件进行迁移训练与验证。 结果 分层随机划分后，训练集、验证集和独立测试集分别包含16 239、4022、2196个目标。模型在边界框损失、分割损失、分类损失及分布焦点损失上均呈持续且稳定的下降趋势；验证集损失曲线与训练集一致，提示模型未见明显过拟合，泛化能力良好。在独立测试集上，模型整体检测与分割性能优异：边界框（Box）与掩码（Mask）的IoU=0.5的平均精度均值（mAP₅₀）均为0.908；Box精确率（P）、召回率（R）分别为0.848、0.884，Mask（P）、Mask（R）分别为0.846、0.881。Box mAP₅₀>0.90的类别涵盖胃抓钳、肝脏拉钩、肝脏、胃、纱布、超声刀、肠钳、持针器、分离钳、切割闭合器、脾脏及施夹器；Box mAP₅₀在0.80~0.90的类别包括生物夹、针、折叠瓣、食管（Mask mAP₅₀>0.9）；Box mAP₅₀<0.80的类别为膈肌脚和膈肌（Mask mAP₅₀>0.8）。 结论 计算机视觉人工智能模型可高效、精准地检测与分割LSGFD联合HHR的关键解剖结构与手术器械，可为该术式后续多场景拓展应用提供技术支持。

Objective To construct a computer vision artificial intelligence model for the integrated surgical scenario of laparoscopic gastric sleeve resection combined with fundoplication (LSGFD) plus concomitant hiatal hernia repair (HHR) based on the YOLOv11m deep learning framework, and to conduct independent verification, for providing visual module support for the future development of surgical navigation and robotic semi-automated surgical systems. Methods The surgical video data of patients who underwent LSGFD combined with HHR at the Department of Minimally Invasive, Hernia and Abdominal Wall Surgery, People’s Hospital of Xinjiang Uygur Autonomous Region and the Department of Hepatobiliary Surgery, Hotan Regional People’s Hospital were retrospectively analyzed. After screening and processing, a total of 3180 sample images were obtained and annotated via instance segmentation. Stratified random sampling method was adopted for dataset allocation: first, 10% of the total samples were randomly selected as the independent test set; subsequently, 20% of the remaining 90% samples were further randomly extracted as the validation set; the rest were all assigned as the training set. The weight files of the YOLOv11m deep learning model were loaded for transfer training and validation. Results After stratified random partitioning, the training set, validation set, and independent test set contained 16 239, 4022, and 2196 targets, respectively. The model showed a continuous and stable downward trend in bounding box loss, segmentation loss, classification loss, and distribution focal loss; the loss curve of the validation set was consistent with that of the training set, suggesting no overfitting and good generalization ability of the model. On the independent test set, the model demonstrated excellent overall detection and segmentation performance: the mean average precision at IoU=0.5 (mAP₅₀) for both the bounding box (Box) and mask (Mask) was 0.908; the precision (P) and recall (R) for Box were 0.848 and 0.884, respectively, and for Mask were 0.846 and 0.881, respectively. Categories with a Box mAP₅₀ > 0.90 included gastric graspers, liver retractors, liver, stomach, gauze, ultrasonic scalpels, intestinal clamps, needle holders, dissecting forceps, staplers, spleen, and appliers.Categories with a Box mAP₅₀ between 0.80 and 0.90 included biological clips, needles, folded flaps, and esophagus (with a Mask mAP₅₀ > 0.9).Categories with a Box mAP₅₀ < 0.80 were diaphragmatic crus and diaphragm (with a Mask mAP₅₀ > 0.8). Conclusion Computer vision artificial intelligence model technology can efficiently and accurately detect and segment the key anatomical structures and surgical instruments involved in the combined LSGFD and HHR procedure, thereby providing technical support for the subsequent expanded application of this surgical approach across multiple scenarios.