Importance Image-based deep learning models (DLMs) have been used in other disciplines, but this method has yet to be used to predict surgical outcomes.

Objective To apply image-based deep learning to predict complexity, defined as need for component separation, and pulmonary and wound complications after abdominal wall reconstruction (AWR).

Design, Setting, and Participants This quality improvement study was performed at an 874-bed hospital and tertiary hernia referral center from September 2019 to January 2020. A prospective database was queried for patients with ventral hernias who underwent open AWR by experienced surgeons and had preoperative computed tomography images containing the entire hernia defect. An 8-layer convolutional neural network was generated to analyze image characteristics. Images were batched into training (approximately 80%) or test sets (approximately 20%) to analyze model output. Test sets were blinded from the convolutional neural network until training was completed. For the surgical complexity model, a separate validation set of computed tomography images was evaluated by a blinded panel of 6 expert AWR surgeons and the surgical complexity DLM. Analysis started February 2020.

Exposures Image-based DLM.

Main Outcomes and Measures The primary outcome was model performance as measured by area under the curve in the receiver operating curve (ROC) calculated for each model; accuracy with accompanying sensitivity and specificity were also calculated. Measures were DLM prediction of surgical complexity using need for component separation techniques as a surrogate and prediction of postoperative surgical site infection and pulmonary failure. The DLM for predicting surgical complexity was compared against the prediction of 6 expert AWR surgeons.

Results A total of 369 patients and 9303 computed tomography images were used. The mean (SD) age of patients was 57.9 (12.6) years, 232 (62.9%) were female, and 323 (87.5%) were White. The surgical complexity DLM performed well (ROC = 0.744; P < .001) and, when compared with surgeon prediction on the validation set, performed better with an accuracy of 81.3% compared with 65.0% (P < .001). Surgical site infection was predicted successfully with an ROC of 0.898 (P < .001). However, the DLM for predicting pulmonary failure was less effective with an ROC of 0.545 (P = .03).

Conclusions and Relevance Image-based DLM using routine, preoperative computed tomography images was successful in predicting surgical complexity and more accurate than expert surgeon judgment. An additional DLM accurately predicted the development of surgical site infection.

重要性 基于图像的深度学习模型 (DLM) 已在其他学科中使用，但该方法尚未用于预测手术结果。

目的 应用基于图像的深度学习来预测复杂性，定义为组件分离的需要，以及腹壁重建 (AWR) 后的肺部和伤口并发症。

设计、设置和参与者 这项质量改进研究于 2019 年 9 月至 2020 年 1 月在一家拥有 874 个床位的医院和三级疝转诊中心进行。对由经验丰富的外科医生进行开放式 AWR 并具有包含整个手术的术前计算机断层扫描图像的腹疝患者的前瞻性数据库进行了查询疝气缺损。生成了一个 8 层卷积神经网络来分析图像特征。图像被分批成训练（约 80%）或测试集（约 20%）以分析模型输出。测试集对卷积神经网络不知情，直到训练完成。对于手术复杂性模型，由 6 名 AWR 外科医生组成的盲法小组评估了一组单独的计算机断层扫描图像验证和手术复杂性 DLM。分析于 2020 年 2 月开始。

曝光 基于图像的 DLM。

主要结果和测量 主要结果是模型性能，通过为每个模型计算的接收者操作曲线 (ROC) 中的曲线下面积来衡量；还计算了准确性以及伴随的敏感性和特异性。措施是使用组件分离技术作为替代指标来预测手术复杂性的 DLM 预测，以及预测术后手术部位感染和肺衰竭。将预测手术复杂性的 DLM 与 6 位 AWR 外科医生的预测进行了比较。

结果 共使用369例患者和9303张CT图像。患者的平均 (SD) 年龄为 57.9 (12.6) 岁，232 (62.9%) 为女性，323 (87.5%) 为白人。手术复杂性 DLM 表现良好（ROC = 0.744；P  < .001），并且与验证集上的外科医生预测相比，表现更好，准确度为 81.3%，而准确率为 65.0%（P  < .001）。手术部位感染预测成功，ROC 为 0.898 ( P  < .001)。然而，DLM 预测肺衰竭的效果较差，ROC 为 0.545 ( P  = .03)。

结论和相关性 基于图像的 DLM 使用常规的术前计算机断层扫描图像成功地预测了手术复杂性，并且比专家外科医生的判断更准确。另一个 DLM 准确地预测了手术部位感染的发展。