Data Related to Computational Analysis and Classification of Ventral Hernia Treatments Based on Repair Technologies

Problems related to ventral hernia are very common, and evaluating them using computational methods can assist in selecting the most appropriate treatment. This study collected data from over 3500 patients from different European countries observed during last 11 years (2012-2022), which were collected by specialists in hernia surgery. The majority of patients underwent standard surgical procedures, with a growing trend towards robotic surgery. This paper focuses on statistically evaluating the treatment methods in relation to patient age, body mass index (BMI), and the type of repair. Appropriate mathematical methods are employed to extract and classify the selected features, with emphasis on computational and machine learning techniques. Mesh hernia surgery is categorized into two classes: groin hernia repairs (GHR) and primary ventral, incisional ventral, and parastomal hernia repairs (PVHR, IVHR, and PHR). The paper presents surgical hernia treatment statistics related to patient age and repair technologies involving different types of meshes. The main conclusions highlight the classification of repair technologies based on patient BMI and age. The accuracy of separating GHR mesh surgery from other types of repairs reached 79.5 % using a two-layer neural network classification, with true negative and true positive rates of 89.6 % and 63.3 %, respectively. The proposed methodology suggests a close interdisciplinary approach and the utilization of computational intelligence in hernia surgery, potentially applicable in a clinical setting.

腹壁疝问题极为普遍，利用计算方法对其评估有助于选择最适宜的治疗方案。本研究收集了来自欧洲不同国家的超过3500名患者的数据，这些数据是在过去11年（2012-2022年）由疝修补外科专家观察收集的。大多数患者接受了标准手术程序，而机器人辅助手术的趋势日益增长。本文着重于对与患者年龄、体重指数（BMI）和修复类型相关的治疗方法的统计学评估。采用适当的数学方法提取并分类所选特征，重点关注计算和机器学习技术。网状疝手术被分为两大类：股疝修复（GHR）和原发性腹壁、切口性腹壁以及旁造口疝修复（PVHR、IVHR和PHR）。论文展示了与患者年龄和不同类型网状修复技术相关的手术疝治疗统计数据。主要结论强调了基于患者BMI和年龄对修复技术的分类。通过双层神经网络分类，将GHR网状手术与其他类型修复区分的准确率达到79.5%，其中真阴性率和真阳性率分别为89.6%和63.3%。提出的方法建议采用跨学科紧密合作的方法，并在疝修补手术中运用计算智能，有望在临床环境中得到应用。