Next-generation prognosis framework for pediatric spinal deformities using bio-informed deep learning networks

Abstract

Predicting pediatric spinal deformity (PSD) from X-ray images collected on the patient's initial visit is a challenging task. This work builds on our previous method and provides a novel bio-informed framework based on a mechanistic machine learning technique with dynamic patient-specific parameters to predict PSD. We provide a geometry-based bone growth model that can be utilized in a range of applications to enhance the bio-informed mechanistic machine learning framework. The proposed technique is utilized to examine and predict spine curvature in PSD cases such as adolescent idiopathic scoliosis. The best fit of a segmented 3D volumetric geometry of the human spine acquired from 2D X-ray images is employed. Using an active contour model based on gradient vector flow snakes, the anteroposterior and lateral views of the X-ray images are segmented to derive the 2D contours surrounding each vertebra. Using minimal user input, the snake parameters are calibrated and automatically computed over the dataset, resulting in fast image segmentation and data collection. The 2D segmented outlines of each vertebra are transformed into a 3D image segmentation result. The Iterative Closest Point mesh registration technique is then used to establish a mesh morphing approach and creates a 3D atlas spine model. Using the comprehensive 3D volumetric model, one can automatically extract spinal geometry data as inputs to the mechanistic machine learning network. Moreover, the proposed bio-informed deep learning network with the modified bone growth model achieves competitive or even superior performance against other state-of-the-art learning-based methods.

Analysis of Yield Threshold in a Functionally Graded Variable Thickness Rotating Disc Using the Tresca Yield Criterion

“…(5) By substituting the stress components into equation ( 3), the equation of motion the disk is transformed into the form of equation ( 6). (6) The analytical solution of the second-order differential equation ( 6) has a general solution in the form of equation ( 7): (7) In which C 1 and C 2 are integration constants. Also, the constant parameters in equation ( 7) are defined as equations ( 8).…”

Exploring Temporal Discrimination of Visual Events though EEG-Based Functional Connectivity Analaysis of Brain Regions

“…Various estimators, such as correlation estimators, partial coherence, mutual information, phase locking value, phase lag index, phase slope index, weighted phase lag index, etc., have been employed in studies to explore functional connections [3][4][5]. The concept of time is defined as a quantitative measure of various sequential events to compare their duration or the interval between them [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21]. The traditional view, influenced by St. Augustine of Hippo philosophy, has been of interest to cognitive neuroscientists, suggesting that time is a composite of simultaneous and disjointed mental concepts.…”

An intelligent composite model incorporating global / regional X-rays and clinical parameters to predict progressive adolescent idiopathic scoliosis curvatures and facilitate population screening

“…A random forest regression method applied upon reconstructed 3D models predicted for changes in spinal morphology over time. 7 Others have incorporated biomechanical analysis 8 and dynamic patient-specific parameters 9 together with imaging features to predict curve progression. Computer vision has the advantage of automating extraction of imaging features for modeling via big data-driven training approaches.…”