Prediction of Temperature and Loading History Dependent Lumbar Spine Biomechanics Under Cyclic Loading Using Recurrent Neural Networks.

Extended-duration cyclic loading of the spine is known to be correlated to lower back pain (LBP). Therefore, it is important to understand how the loading history affects the entire structural behavior of the spine, including the viscoelastic effects. Six human spinal segments (L4L5) were loaded with pure moments up to 7.

Development of convolutional neural networks for recognition of tenogenic differentiation based on cellular morphology.

Background And Objective: The use of automated systems for image recognition is highly preferred for regenerative medicine applications to evaluate stem cell differentiation early in the culturing state with non-invasive methodologies instead of invasive counterparts. Bone marrow-derived mesenchymal stem cells (BMSCs) are able to differentiate into desired cell phenotypes, and thereby promise a proper cell source for tendon regeneration. The therapeutic success of stem cell therapy requires cellular characterization prior to the implantation of cells.