Machine intelligence for nerve conduit design and production

Abstract

Nerve guidance conduits (NGCs) have emerged from recent advances within tissue engineering as a promising alternative to autografts for peripheral nerve repair. NGCs are tubular structures with engineered biomaterials, which guide axonal regeneration from the injured proximal nerve to the distal stump. NGC design can synergistically combine multiple properties to enhance proliferation of stem and neuronal cells, improve nerve migration, attenuate inflammation and reduce scar tissue formation. The aim of most laboratories fabricating NGCs is the development of an automated process that incorporates patient-specific features and complex tissue blueprints (e.g. neurovascular conduit) that serve as the basis for more complicated muscular and skin grafts. One of the major limitations for tissue engineering is lack of guidance for generating tissue blueprints and the absence of streamlined manufacturing processes. With the rapid expansion of machine intelligence, high dimensional image analysis, and computational scaffold design, optimized tissue templates for 3D bioprinting (3DBP) are feasible. In this review, we examine the translational challenges to peripheral nerve regeneration and where machine intelligence can innovate bottlenecks in neural tissue engineering.

Keywords: Artificial intelligence; Bioprinting; Computer vision; Data science; Machine learning; Nerve regeneration; Tissue engineering.

Fig. 1

Sunderland Classification of Nerve Injuries [18]

Fig. 2

Hypothetical steps to create NGCs with the proposed integrated tissue engineering and machine intelligence approaches. The optimal performance of NGC requires a tight integration and synergy from basic science, advanced tissue engineering approaches and clinical practices with elaborated model of machine intelligence. Ample data will facilitate training and standardize the production and feedback look to fulfil the requirement of regulatory compliance