Possibilities of Reflecting the Mechanical Properties of Non-Absordable Surgical Meshes in an AI-Based Model in the Context of Industry 4.0/5.0

Non-absorbable surgical meshes are key biomedical materials used for tissue reinforcement, designed for durability, biocompatibility, and mechanical stability in clinical applications. The mechanical properties of these meshes, such as tensile strength, elasticity, and porosity, are crucial for their long-term performance and integration with host tissue. In the context of Industry 4.0/5.0, emphasis is placed on integrating intelligent technologies, such as real-time data acquisition and advanced computational modeling, to improve the design and production of surgical meshes. Computational models simulate the mechanical behavior of meshes under physiological conditions, enabling precise optimization of their material properties and design. In this article, we propose potential artificial intelligence (AI)-based approaches for future research, such as machine learning (ML), for analyzing large datasets from computational and experimental studies to identify optimal mesh configurations. The direction of tensile loading significantly influences the mechanical response of the mesh. Transversely stretched specimens demonstrated higher maximum failure forces and greater fatigue resistance than longitudinally stretched specimens, both in sutured and unsutured conditions. Suturing the mesh to biological tissue significantly reduced its mechanical strength and stiffness, demonstrating a weakening effect at the mesh-tissue interface. Cyclic loading revealed a gradual decrease in strength in all specimens, suggesting fatigue, but transversely stretched meshes maintained higher forces for >1000 cycles than longitudinally stretched meshes. The observed differences in mechanical behavior can be attributed to the anisotropic mesh structure and mechanical suturing effects, which introduce stress concentrations and structural discontinuities. These results emphasize the importance of considering both directionality and surgical technique when selecting and implementing mesh implants. Both AI-based models achieved scores above 80%, demonstrating their clinical utility and the potential for development toward prediction accuracy above 85–90% in clinical settings. Future research should incorporate AI-based computational models to improve predictive capabilities, ultimately leading to the development of more effective, patient-specific surgical meshes.