Transforming medicine: artificial intelligence integration in the peripheral nervous system

Abstract

In recent years, artificial intelligence (AI) has undergone remarkable advancements, exerting a significant influence across a multitude of fields. One area that has particularly garnered attention and witnessed substantial progress is its integration into the realm of the nervous system. This article provides a comprehensive examination of AI's applications within the peripheral nervous system, with a specific focus on AI-enhanced diagnostics for peripheral nervous system disorders, AI-driven pain management, advancements in neuroprosthetics, and the development of neural network models. By illuminating these facets, we unveil the burgeoning opportunities for revolutionary medical interventions and the enhancement of human capabilities, thus paving the way for a future in which AI becomes an integral component of our nervous system's interface.

Keywords: artificial intelligence; neural network; neuro-prosthetic; pain management; peripheral nervous system.

Figure 1

The integration of AI and precision medicine enhances individual healthcare by optimizing therapy planning and diagnostic methods.

Figure 2

The initial steps for preparing images for analysis, and the layout of the SOLOv2-MN deep learning model. The figure initially shows how images are pre-processed, including normalization and augmentation techniques and provides visual representation of the SOLOv2-MN model’s architecture, critical for their method of automated median nerve segmentation in dynamic ultrasound imaging (24).

Figure 3

A deep neural network for MRI identification of carpal tunnel syndrome. (A) classification model to determine which MRI category. (B) U-Net for carpal tunnel area segmentation. (C) The result of the new proposed deep neural network, where the marker position (left) and predicted position (right) of a carpal tunnel.

Figure 4

Different methods that help the patient to manage the neuropathic pain using artificial algorithms and neural networks.

Figure 5

Illustrates the signal-processing procedure in an artificial tactile sensing system. It shows how signals from two types of sensors (SA and FA) are processed through an artificial mechanoreceptor neural board. This processing mimics the responses of real nerve cells to pressure and vibration stimuli, replicating the sensory functions of biological skin (81).

Figure 6

(A) Data from nerves in the individual’s amputated arm are collected using Neuronix neural interface chips. This is followed by the process of extracting key features. Subsequently, a deep learning AI utilizes these features to ascertain the individual’s intention to move multiple degrees of freedom at once. These predictions are then translated into real-time movements of either a virtual hand or a prosthetic hand. (B) The deep learning AI based on the recurrent neural network (RNN) architecture (88).