Employing seamless mesh implants during Lichtenstein hernia repair with the use of insights enabled by artificial intelligence

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Introduction

When it comes to surgical mesh fabrication, polypropylene (PP) is the most widely used material worldwide [1]. It is nonabsorbable, electrostatically neutral, extremely hydrophobic, nonpolar, and has a high tensile strength. It can be found in mono or multifilaments, coated or uncoated, heavy or lightweight forms [2]. Fuse Deposition Modelling (FDM) in 3D printing surgical meshes [3] reduces the tensile strength of the mesh and creates multiple contact points between the mesh and surrounding tissues, as opposed to the mobile joint points that are achieved through knitting in non-pre-shaped meshes. This results in a significant reduction in both operative time and postoperative pain [4].

In terms of surgical time, postoperative seroma/hematoma production, urinary retention, and even more, recurrence rates, the use of pre-shaped surgical meshes is better to that of non-pre-shaped suture-fixed ones [5, 6]. Many theories explain why the application of self-adhering pre-shaped meshes causes less postoperative pain than sutured ones by linking the pathophysiology of early postoperative inguinal pain to not only surgery-related causes but also tissue trauma from surgical field preparation, mesh inflammatory reaction, and mesh fixation. Furthermore, cost-effective research demonstrated that self-adhering pre-shaped meshes were more cost-effective to use than ordinary sutured meshes, despite their higher price [7, 8].

Tissue adhesives have been proposed as an alternative to permanent fixation devices in hernia repair with the aim of reducing perforation-associated complications and chronic pain. The adhesives can be divided into three main categories: biologic products (e. g., fibrin), synthetic glues (e. g., cyanoacrylate based), and genetically engineered polymer protein glues. Fibrin sealant has been used extensively in all surgical disciplines for tissue adhesion, suture support, hemostasis, wound care, and endoscopic treatment of bleeding. Cyanoacrylate based glues have been investigated in numerous scientific studies as hemostatic agents, topical dressing, and adhesives in soft tissues, in ophthalmology, odontostomatology, osteosynthesis of bone fracture and, recently, as drug carriers. They were also used in gastroenterology for esophagus varix treatment, in maxillo-facial surgery, and in vascular surgery for arterious anastomoses [9].

The accuracy of diagnoses, clinical judgment, and patient outcomes have all been transformed by the application of artificial intelligence and statistical science in medicine, especially surgery. The crucial significance statistical frameworks play in organizing and approving medical research is shown by current research. When assessing clinical outcomes, especially in surgical operations, fundamental statistical parameters like sensitivity, specificity, and predictive values are still crucial. Likewise, statistically verified artificial intelligence (AI) models improve surgical accuracy and outcome prediction [10, 11].

Another popular statistical technique is regression analysis, for which Lee suggests standardized uses for continuous variables in clinical research [12]. Machine learning algorithms are becoming more widely used in a variety of fields, including radiology and surgical risk assessment. These algorithms provide doctors with dynamic models that get better with time and additional data [13]. The strength of statistical science in controlling chronic illnesses is further demonstrated by the use of machine learning in complicated areas, such as blood glucose prediction in Type 1 diabetes. In this situation, the precision, scalability, and reproducibility of research are greatly impacted by the data analysis methods selected. Although many people use Microsoft Excel for simple data management and visualization, Python offers more flexibility and computational resilience. With the help of libraries like Pandas, NumPy, SciPy, and scikit-learn, Python allows for sophisticated statistical modeling, real-time data analysis, and automation on a scale that Excel is unable to rival [14, 15].

The growing relevance of machine learning in medical research was reviewed by Garg and Mago [16], who emphasized how Python makes it easier to construct algorithms for diagnostic and therapy planning. Additionally, Fang explains how statistical techniques guarantee increased reproducibility and transparency in biological research when applied using programming environments such as Python. Additionally, Python is a better option for medical researchers working with high-dimensional clinical datasets due to its connection with hospital information systems and compatibility with big data platforms. With these developments, Python is now more suited to construct intelligent healthcare systems and serves as a supplement to statistical science [17].

The aim of this work is to enhance the results of Lichtenstein, the gold standard technique for anterior hernia repair [18], given that the global market for hernia repair is expected to reach $6.3 billion in the next few years [19].

Materials and methods

Patients

Between January 2022 and March 2025, our team conducted 120 patients with inguinal hernias who underwent anterior inguinal hernioplasty with seamless implants during Liechtenstein. The study took place at RUDN University’s Department of Operative Surgery and Clinical Anatomy, located at the Clinical Federal Hospital № 85 in Moscow, Russia. We used patient card control, which allowed us to select all of the relevant help criteria. As summarized in Table 1.

Inclusion criteria

1. Patients with a unilateral, main inguinal hernia and no additional hernias.
2. Patients in the age range of 21 to 71.
3. Both men and non-pregnant women.
4. Patients were treated using the Lichtenstein method.
5. Patients beforehand.

Disqualification standards

1. People who have bilateral, recurrent inguinal hernias or other hernias that occur concurrently are excluded.
2. Individuals who are over 71 or under 21.
3. Expectant mothers.
4. Patients who expressed their desire for laparoscopic surgery.
5. Individuals in need of urgent care.

Table 1
Control card for the patients

esearch methodology

A combination of a prospective and retrospective analysis.

For the purposes of our investigation, we divided the 120 participants in our clinical trial into three equal groups (n = 40).

Adhesix^TM self-gripping mesh implants (Figure 1) were used in Group A; 40 male patients make up this group. The average age was 51.45. Thirty-two patients had a right inguinal hernia, while eight patients had a left. There is a 24:16 ratio of oblique to straight inguinal hernias. The averages of age, duration of herniation, hernial and external henial ring, are summarized in Table 2.

Table 2
Averages of age, duration of herniation, dimensions of hernia and external henial ring for group A

Hertra^TM pre-fitted mesh implants (Figure 2) were used in Group B; 36 men and 4 women. 55.95 years old was the average. This group consists of twenty patients with right inguinal hernias and twenty patients with left inguinal hernias. Oblique to direct inguinal hernias is 20:20 in ratio. The averages of age, duration of herniation, hernial and external henial ring, are summarized in table 3.

Glue-repaired Lintex^TM mesh implants (Figure 3) were used in Group C; there were also 14 female patients and 26 male patients. The average age was 58.89 years. Of the patients, 26 had a right inguinal hernia and 14 had a left. 21:19 is the oblique to direct inguinal hernia ratio. The averages of age, duration of herniation, hernial and external henial ring, are summarized in Table 4.

Table 3
Averages of age, duration of herniation, dimensions of hernia and external henial ring for group B

Table 4
Averages of age, duration of herniation, dimensions of hernia and external henial ring for group C

Fig. 1. Adhesix^TM self-gripping mesh implant

Fig. 2. Hertra^TM pre-fitted mesh implant

Fig. 3. Glue-repaired Lintex^TM mesh implant

The main complaints of the patients are summarized in Table 5.

After collecting the medical histories of the patients in the three groups, we discovered the following comorbidities as summarized in Table 6.

Table 5
Main complaints of the patients

Table 6
Patients’ associated comorbidities

Statistical analysis

The statistics module of the Python programming language, which is machine learning-based statistical software with artificial intelligence support, was used to perform the calculations. Descriptive statistics, such as arithmetic mean (M), standard error, median, mode, standard deviation (s), range, minimum (Min), and maximum (Max), were used to statistically examine the collected data. Windows 11 was the operating system. 8 GB of RAM is needed for the system.

Results and discussion

The comparison-based criteria

I. The time for operation.

The average operating time for patients in group (A) who had surgery with Adhesix^TM self-gripping mesh implants was 27.8 minutes; for patients in group (B) who had surgery with Hertra^TM mesh implants fixed by a single polypropylene suture to the pubic bone, it was 31.4 minutes; and for patients in group (C) with Lintex^TM glue-fixed mesh implant was 38.9 minutes as summarized in Table 7.

The average operating duration of 3.6 minutes does not appear to differ statistically significantly between groups A and B, according to the p-value of 0.90.

The p-value (< 0.0001) shows a statistically significant difference in operative time between groups A and C, with an average of roughly 11.1 minutes.

The p-value (< 0.0001) shows a statistically significant difference in the average operating duration (~7.5 minutes) between groups (B) and (C).

In terms of operating time, these figures show a statistically significant difference between the use of Adhesix^TM self-gripping mesh, Hertra^TM mesh fastened by single stitch, and Lintex^TM mesh fixed by glue.

Table 7
The average operative-time for the 3 group

II. Hospitalization (day/bed)

Hospital stays for group (A) patients who had surgery with Adhesix^TM self-gripping mesh implants lasted an average of 4.9 days; group (B) patients who had surgery with Hertra^TM mesh implants that were secured to the pubic bone with a single polypropylene suture lasted 4.95 days; and group (C) patients who had surgery with Lintex^TM implants that were secured with glue lasted 4.95 days, as summarized in Table 8.

The average length of stay in the hospital was about five days, similar for all groups.

The p-value of 0.90 indicates that there is no statistically significant difference in the length of hospitalization for either group.

These numbers showed that the three study groups’ hospital stays did not differ statistically significantly.

III.Recovery issues after surgery

During their hospital stay following surgery, we found that everyone in group (A) was trouble-free.

No problems exist between any of the group members for the patients in group (B).

Conversely, we found that three patients in group (C) had postoperative pain that was reduced following NSAID use. One patient had seroma. The other group members did not experience any pain. During the hospital stay following surgery, none of the patients in group (C) experienced any further issues.

 Table 8
Hospitalization (day/bed)

IV. Issues with the short-term follow up.

During the six-month short-term follow-up, we noticed that all of the patients in group (A) had no issues. There were no repetitions.There are no problems among the patients in Group (B). There were no repetitions. Among the patients in group (C), we found two who had mesh migration and recurrence.

So, the three types of seamless implants that Lichtenstein uses for open inguinal hernioplasty are self-gripping mesh (Adhesix), mesh implants (Hertra) that are fastened to the pubic bone with a single wire, and mesh implants (Lintex) that are fixed with glue.

Discussion 1. Patients who had surgery in the first group were hospitalized for shorter periods of time than those in the second and third groups (group A‑4.9 b/d, group B‑4.9.5 b/d, and group C‑4.95 b/d), but there were no statistically significant differences.

Discussion 2. Patients in the first group take a lot less time to complete the process than patients in the second and third groups (groups A, B, and C, respectively; 27.8 minutes, 31.4 minutes, and 38.9 minutes).

Discussion 3. Unlike the third group, which included three patients with postoperative discomfort and one with postoperative seroma formation, the first and second groups’ postoperative hospitalization stays were free of complications.

Discussion 4. Unlike the third group, which included two patients with mesh migration and hernia recurrence, the first and second group patients experienced no complications during the short-term follow-up.

Conclusion

Compared to Lintex mesh implants, the operative times for inguinal hernioplasty with an anterior approach using self-gripping mesh (Adhesix), mesh implants (Hertra) fixed by a single polypropylene to the pubic bone, and mesh implants (Lintex) fixed by glue are significantly shorter (11. minutes and 7.5 minutes, respectively). In contrast to Lintex^TM, which experienced three post-operative pain cases and one seroma case, Adhesix^TM and Hertra^TM experienced no issues during the post-operative hospital stay. In the short-term follow-up, the first group experienced no issues, the second group experienced none, and the third group experienced two mesh migrations and a hernia recurrence.

About the authors

Andrey V. Protasov

Email: mekhaeel60@yahoo.com
ORCID iD: 0000-0001-8452-5776
SPIN-code: 3126-7423
Moscow, Russian Federation

Mekhaeel Sh. F. Mekhaeel

Author for correspondence.
Email: mekhaeel60@yahoo.com
ORCID iD: 0000-0002-0381-3379
Moscow, Russian Federation

Sameh M.A. Salem

Email: mekhaeel60@yahoo.com
ORCID iD: 0009-0008-0690-6811
Moscow, Russian Federation

References

Supplementary files

Supplementary Files

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1. JATS XML

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2. Fig. 1. AdhesixTM self-gripping mesh implant

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3. Fig. 2. HertraTM pre-fitted mesh implant

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4. Fig. 3. Glue-repaired LintexTM mesh implant

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