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Scoliosis, an abnormal curvature of the human spinal column, is characterized by a lateral deviation of the spine, accompanied by axial rotation of the vertebrae. Adolescent Idiopathic Scoliosis (AIS) is the most common type, affecting children between ages 8 to 18 when bone growth is at its maximum rate. We propose a mechanistic machine learning algorithm in order to study patient-specific AIS curve progression, which is associated with the bone growth and other genetic and environmental factors. Two different frameworks are used to analyse and predict curve progression, one with implementing clinical data extracted from 2D X-ray images and the other one with incorporating both clinical data and physical equations governing the non-uniform bone growth. The physical equations governing bone growth are affiliated with calculating all stress components at each region. The stress values are evaluated through a surrogate finite element simulation and a bone growth model on a detailed patient-specific geometry of the human spine. We also propose a patient-specific framework to generate the volumetric model of human spine which is partitioned into different tissues for both vertebra and intervertebral disc. It is shown that implementing physical equations governing bone growth into the prediction framework will notably improve the prediction results as compared to only using clinical data for prediction. In addition, we can predict curve progression at ages outside the range of training samples., Mechatronic Design

Limb contractures are debilitating complications associated with various muscle and nervous system disorders. This report summarizes presentations at a conference at the Shirley Ryan AbilityLab in Chicago, Illinois, on April 19–20, 2018, involving researchers and physicians from diverse disciplines who convened to discuss current clinical and preclinical understanding of contractures in Duchenne muscular dystrophy, stroke, cerebral palsy, and other conditions. Presenters described changes in muscle architecture, activation, extracellular matrix, satellite cells, and muscle fiber sarcomeric structure that accompany or predispose muscles to contracture. Participants identified ongoing and future research directions that may lead to understanding of the intersecting factors that trigger contractures. These include additional studies of changes in muscle, tendon, joint, and neuronal tissues during contracture development with imaging, molecular, and physiologic approaches. Participants identified the requirement for improved biomarkers and outcome measures to identify patients likely to develop contractures and to accurately measure efficacy of treatments currently available and under development.