# Computational Analysis and Classification of Hernia Repairs
###### Abstract  
Problems related to ventral hernia repairs (VHR) are very common, and evaluating them using computational methods can assist in selecting the most appropriate treatment. This study is based upon data from 3339 patients from different European countries observed during the last 12 years (2012-2023), which were collected by specialists in hernia surgery. Most patients underwent standard surgical procedures, with a growing trend towards laparoscopic 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. The paper presents surgical hernia treatment statistics related to patient age, BMI, and repair methods. The main conclusions point to mean groin hernia repair (GHR) complications of 19% for patients in the database. The accuracy of separating GHR mesh surgery with and without postoperative complications reached 74.4% using a two-layer neural network classification. Robotic surgeries represent 22.9% of all the evaluated hernia repairs. The proposed methodology suggests both an interdisciplinary approach and the utilization of computational intelligence in hernia surgery, potentially applicable in a clinical setting. hermia surgical repair; computational intelligence; feature extraction; classification; machine learning +
Footnote †: journal: Medical Informatics  
incisional and larger hernias [12; 13; 14]. This enables hernia surgeons to identify any morphological tissue abnormalities during the preoperative planning phase, enhancing their decision-making capabilities. Technological advancements, such as three-dimensional volumetric analysis, augmented reality, machine learning, and the application of computational intelligence [15; 16], contribute to the enhancement of patient care. Hernia surgery is currently witnessing a technological boom. Mathematical and engineering methods are extensively employed in studies focused on the mechanical properties of mesh [17; 18; 19] and fixation techniques. Computational modeling has been utilized to assess the safety and efficacy of hernia mesh and biomaterial-based implants [20]. Artificial intelligence (AI) enables the integration of medical imaging, robotic hernia surgery [21; 22; 23; 24; 25], computer-aided hernia repair, and surgeon training. Furthermore, deep learning-based methods have been employed for automated surgical phase recognition [26], which incorporates information about the complexity of intra-abdominal and abdominal wall anatomy. The use of mesh in groin hernia repairs has demonstrated a reduction in the risk of hernia recurrence, although it may be associated with a slightly higher risk of chronic pain. Globally, over 1 million meshes are implanted in patients annually. In ventral hernia repairs, there is a significant rate of failure and reoperation, yet the reasons for such poor outcomes are not fully understood. Designing surgical meshes with suitable properties for abdominal hernias constitutes a distinct research area [27]. The mechanical behavior of both the abdominal wall and mesh elasticity is investigated using appropriate numerical models to propose appropriate tensile strength for specific hernia repairs. The use of AI integrated with a reliable database [22] can aid in identifying risk factors and determining whether mesh usage is recommended. However, the final decision should be made on an individual basis, considering the risks and benefits for each patient. Classification of hernias (like all scoring systems used in medicine) based on their localization and size enables a comparison of research results of similar types of hernias and makes the planning of future studies easier. There are different nationwide datasets [28]. The European Hernia Society (EHS) classification of ventral hernias is a commonly adopted scoring system and is also used in the EHS registry [29; 30].