Elhage Sharbel Adib, Deerenberg Eva Barbara, Ayuso Sullivan Armando, Murphy Keith Joseph, Shao Jenny Meng, Kercher Kent Williams, Smart Neil James, Fischer John Patrick, Augenstein Vedra Abdomerovic, Colavita Paul Dominick, Heniford B Todd
Department of Surgery, Franciscus Gasthuis en Vlietland, Rotterdam, the Netherlands.
Division of Gastrointestinal and Minimally Invasive Surgery, Department of Surgery, Carolinas Medical Center, Charlotte, North Carolina.
JAMA Surg. 2021 Oct 1;156(10):933-940. doi: 10.1001/jamasurg.2021.3012.
Image-based deep learning models (DLMs) have been used in other disciplines, but this method has yet to be used to predict surgical outcomes.
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.
Image-based DLM.
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.
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).
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.
基于图像的深度学习模型（DLMs）已在其他学科中使用，但该方法尚未用于预测手术结果。
应用基于图像的深度学习来预测腹壁重建（AWR）后的复杂性，定义为是否需要进行成分分离以及肺部和伤口并发症。
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张床位的医院和三级疝气转诊中心进行。前瞻性数据库查询了患有腹侧疝且由经验丰富的外科医生进行开放性AWR并具有包含整个疝缺损的术前计算机断层扫描图像的患者。生成了一个8层卷积神经网络来分析图像特征。图像被分批分为训练集（约80%）或测试集（约20%）以分析模型输出。在训练完成之前，测试集对卷积神经网络是保密的。对于手术复杂性模型，由6名AWR专家外科医生组成的盲评小组和手术复杂性DLM对一组单独的计算机断层扫描图像验证集进行评估。分析于2020年2月开始。
基于图像的DLM。
主要结局是每个模型在受试者工作特征曲线（ROC）中通过曲线下面积测量的模型性能；还计算了伴随敏感性和特异性的准确性。测量指标包括使用成分分离技术需求作为替代指标的DLM对手术复杂性的预测以及对术后手术部位感染和肺功能衰竭的预测。将预测手术复杂性的DLM与6名AWR专家外科医生的预测进行比较。
共使用了369例患者和9303张计算机断层扫描图像。患者的平均（标准差）年龄为57.9（12.6）岁，232例（62.9%）为女性，323例（87.5%）为白人。手术复杂性DLM表现良好（ROC = 0.744；P <.001），与验证集上外科医生的预测相比，其准确性为81.3%，而外科医生的预测准确性为65.0%（P <.001），表现更好。成功预测手术部位感染的ROC为0.898（P <.001）。然而，预测肺功能衰竭的DLM效果较差，ROC为0.545（P = 0.03）。
使用常规术前计算机断层扫描图像的基于图像的DLM成功预测了手术复杂性，并且比专家外科医生的判断更准确。另一个DLM准确预测了手术部位感染的发生。
