基于术前腹部CT图像构建腹壁切口疝术后复发预测模型，以辅助疝外科医生制定个体化治疗方案。

收集2016—2019年在首都医科大学附属北京朝阳医院就诊的528例腹壁切口疝患者资料，将患者术前腹部CT图像进行标准化处理后共获得44380张图像。按4∶1的比例随机分为训练集及验证集，用以构建和验证预测切口疝复发的卷积神经网络（CNN）模型。通过灵敏度、特异度、受试者工作特征曲线及曲线下面积（AUC）等指标验证模型性能。

528例接受切口疝修补手术的患者中有73例出现复发，复发率为13.8%。本研究成功建立了切口疝术后复发预测的CNN模型，验证结果显示AUC值为0.840，灵敏度85.2%，特异度68.1%。

基于术前腹部CT图像构建的CNN模型对切口疝患者术后复发具有较好的预测能力，对疝外科医生制定个体化治疗方案具有一定辅助作用。

To construct a predictive model for the postoperative recurrence of incisional hernia based on preoperative abdominal CT images, with the aim of assisting hernia surgeons in formulating individualized treatment plans.

A cohort of 528 patients with incisional hernia who were treated at Beijing Chaoyang Hospital between 2016 and 2019, was assembled. Preoperative abdominal CT images underwent standardization, yielding 44,380 images. These images were randomly divided into training and validation sets in a 4∶1 ratio to train and validate a convolutional neural network (CNN) model for predicting incisional hernia recurrence. Model performance was evaluated utilizing indicators including sensitivity, specificity, receiver operating characteristic (ROC) curve, and area under the curve (AUC).

Among the 528 patients who underwent incisional hernia repair, 73 experienced recurrence, resulting in a recurrence rate of 13.8%. The study successfully established a CNN model for predicting postoperative recurrence of incisional hernia, with a validated AUC value of 0.840, sensitivity of 85.2%, and specificity of 68.1%.

The CNN model constructed based on preoperative abdominal CT images has a good ability to predict postoperative recurrence in patients with incisional hernia and has a certain role for hernia surgeons in developing individualized treatment plan.