An approach to evaluating and benchmarking the mechanical behavior of a surgical mesh prototype designed for the repair of abdominal wall defects

García-García, C.; Carrascal-Morillo, M.T.; Gomez, Julie; Guerrero, C. Bernal; García-Prada, Juan Carlos

doi:10.1016/j.jmbbm.2021.104909

Cited by 5 publications

(2 citation statements)

References 31 publications

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“…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].…”

Section: Introductionmentioning

confidence: 99%

Computational Analysis and Classification of Hernia Repairs

Charvátová,

East,

Procházka

et al. 2024

Applied Sciences

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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.

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Section: Introductionmentioning

confidence: 99%

Computational Analysis and Classification of Hernia Repairs

Charvátová,

East,

Procházka

et al. 2024

Applied Sciences

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“…Hernia surgery is not only experiencing a technological boom but mathematics and engineering methods are also widely utilised in studies dedicated to mechanical properties of mesh [14][15][16][17] and fixation methods. Computational modelling has been used to evaluate the safety and effectiveness of hernia mesh and biomaterial-based implants 18 .…”

Section: Introductionmentioning

confidence: 99%

Computational Intelligence in Hernia Surgery with Classification of Repair Types

Charvátová

East

Procházka

2022

Preprint

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Problems of the ventral hernia are very common and their evaluation by computational methods can help with the selection of the most appropriate treatment. This study collected data from the records of the set of more than 2800 patients from different European countries observed during last 11 years (2012-2022) and were collected by specialists in hernia surgery. The majority of patients were treated by the standard surgery approach, with the increasing trend towards robotic surgery. This paper is devoted to a statistical evaluation of the treatment method related to the age of the patients, their body mass index (BMI), and the type of repair. Appropriate mathematical methods are used to extract and classify the selected features.The proposed methodology focuses on computational and machine learning methods. Mesh hernia surgery is classified into two classes: groin hernia repairs (GHR) and primary ventral, incisional ventral and parastomal hernia repairs (PVHR, IVHR and PHR).This paper presents statistics of surgery hernia treatments related to the age of patients and repair technologies with different kinds of meshes. The main conclusions point to classification of repair technologies related to the BMI and the age of the patients. Separation accuracy of GHR surgery from other types of repairs reached 69.2 % and 70 % for the Bayesian and SVM methods, respectively. The proposed methodology suggests a very close interdisciplinary approach and the use of computational intelligence in hernia surgery, with its possible use in the clinical environment.

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