STEM CELL-DERIVED EXOSOMES FOR SPINAL CORD INJURY REPAIR: AI-DRIVEN ANALYSIS FOR OPTIMIZING THERAPEUTIC EFFICACY

Abstract

This research investigates the application of an advanced AI-driven image analysis solution to assess the therapeutic efficacy of stem cell-derived exosomes in spinal cord injury therapy. This study focuses on analyzing exosomes derived from stem cells, especially from MSCs, due to their regenerative capabilities and immunomodulatory and differentiating properties capable of more amply healing the injured spinal cords within the context of SCI protocols. Traditional techniques of determining the viability of stem cells and readiness for therapy employ varied degrees of invasion and lengthy processes with more or less precision; thus, we here describe a novel image analysis technique for predicting the viability and therapeutic potential of MSC-derived exosomes in SCI models. Phase-contrast microscopy allows our algorithm to analyze exosome preparations in the early proliferation phases associated with SCI-specific therapeutic protocols. With the type of image-analyzing algorithms we describe (the basis of our development is edge detection in which the overlapping of structures among clusters), exosomes are recognized based on their structural features and functional characteristics using an algorithm involving thresholding with morphological operations. We use H-minima transforms and Hidden Markov Models (HMM) for resolving overlapping structures in clusters, relying on marker-controlled watershed approaches to further boost the segmentation result for single exosomes. Machine learning algorithms classify exosome phenotypes based on morphometric and textural features extracted from segmentation. This model driven on a set of 17890 exosomes for month 3, 110,670 for month 6, and 241,010 for month 12 acquired from SCI cultures (Months 3,6, and 12=123,190) for proposed datasets-1 images, 70,293 , and  49,350 exosome acquired from SCI cultures  for proposed datasets-3 images developed and with high detection of sensitivity (99%) and specificity (98%) and almost perfect precision (99%) in both detection and segmentation of exosome structures. It yields an AUC of 0.99 (CI9s=0.976-0.988) during the classification of phenotypes of exosomes developing in the early and mid-logarithmic growth stages. Such is a testament to the accuracy and reliability intrinsic to this approach of noninvasively assessing this cell therapy. Thus offering a robust method for the mechanical and informational validation and optimization