Thoracic vertebral morphology in normal and scoliosis deformity in skeletally immature rabbits: A Longitudinal study

Predicting radiographic outcomes of vertebral body tethering in adolescent idiopathic scoliosis patients using machine learning

Anterior Vertebral Body Tethering (AVBT) is a growing alternative treatment for adolescent idiopathic scoliosis (AIS), offering an option besides spinal fusion. While AVBT aims to correct spinal deformity through growth correction, its outcomes have been mixed. To improve surgical outcomes, this study aimed to develop a machine learning-based tool to predict short- and midterm spinal curve correction in AIS patients who underwent AVBT surgery, using the most predictive clinical, radiographic, and surgical parameters. After institutional review board approval and based on inclusion criteria, 91 AIS patients who underwent AVBT surgery were selected from the Shriners Hospitals for Children, Philadelphia. For all patients, longitudinal standing (PA or AP, and lateral) and side bending spinal Radiographs were retrospectively obtained at six visits: preop and first standing, one year, two years, five years postop, and at the most recent follow-up. Demographic, radiographic, and surgical features associated with curve correction were collected. The sequential backward feature selection method was used to eliminate correlated features and to provide a rank-ordered list of the most predictive features of the AVBT correction. A Gradient Boosting Regressor (GBR) model was trained and tested using the selected features to predict the final correction of the curve in AIS patients. Eleven most predictive features were identified. The GBR model predicted the final Cobb angle with an average error of 6.3 ± 5.6 degrees. The model also provided a prediction interval, where 84% of the actual values were within the 90% prediction interval. A list of the most predictive features for AVBT curve correction was provided. The GBR model, trained on these features, predicted the final curve magnitude with a clinically acceptable margin of error. This model can be used as a clinical tool to plan AVBT surgical parameters and improve outcomes.

In vitro coupled motions of the whole human thoracic and lumbar spine with rib cage

Study design

In vitro biomechanical study investigating the coupled motions of the whole normative human thoracic spine (TS) and lumbar spine (LS) with rib cage.

Objective

To quantify the region-specific coupled motion patterns and magnitudes of the TS, thoracolumbar junction (TLJ), and LS simultaneously.

Background

Studying spinal coupled motions is important in understanding the development of complex spinal deformities and providing data for validating computational models. However, coupled motion patterns reported in vitro are controversial, and no quantitative data on region-specific coupled motions of the whole human TS and LS are available.

Methods

Pure, unconstrained bending moments of 8 Nm were applied to seven fresh-frozen human cadaveric TS and LS specimens (mean age: 70.3 ± 11.3 years) with rib cages to elicit flexion-extension (FE), lateral bending (LB), and axial rotation (AR). During each primary motion, region-specific rotational range of motion (ROM) data were captured.

Results

No statistically significant, consistent coupled motion patterns were observed during primary FE. During primary LB, there was significant (p < 0.05) ipsilateral AR in the TS and a general pattern of contralateral coupled AR in the TLJ and LS. There was also a tendency for the TS to extend and the LS to flex. During primary AR, significant coupled LB was ipsilateral in the TS and contralateral in both the TLJ and LS. Significant coupled flexion in the LS was also observed. Coupled LB and AR ROMs were not significantly different between the TS and LS or from one another.

Conclusions

The findings support evidence of consistent coupled motion patterns of the TS and LS during LB and AR. These novel data may serve as reference for computational model validations and future in vitro studies investigating spinal deformities and implants.

Patient-specific finite element modeling of scoliotic curve progression using region-specific stress-modulated vertebral growth

PURPOSE

This study describes the creation of patient-specific (PS) osteo-ligamentous finite element (FE) models of the spine, ribcage, and pelvis, simulation of up to three years of region-specific, stress-modulated growth, and validation of simulated curve progression with patient clinical angle measurements.

RESEARCH QUESTION

Does the inclusion of region-specific, stress-modulated vertebral growth, in addition to scaling based on age, weight, skeletal maturity, and spine flexibility allow for clinically accurate scoliotic curve progression prediction in patient-specific FE models of the spine, ribcage, and pelvis?

METHODS

Frontal, lateral, and lateral bending X-Rays of five AIS patients were obtained for approximately three-year timespans. PS-FE models were generated by morphing a normative template FE model with landmark points obtained from patient X-rays at the initial X-ray timepoint. Vertebral growth behavior and response to stress, as well as model material properties were made patient-specific based on several prognostic factors. Spine curvature angles from the PS-FE models were compared to the corresponding X-ray measurements.

RESULTS

Average FE model errors were 6.3 ± 4.6°, 12.2 ± 6.6°, 8.9 ± 7.7°, and 5.3 ± 3.4° for thoracic Cobb, lumbar Cobb, kyphosis, and lordosis angles, respectively. Average error in prediction of vertebral wedging at the apex and adjacent levels was 3.2 ± 2.2°. Vertebral column stress ranged from 0.11 MPa in tension to 0.79 MPa in compression.

CONCLUSION

Integration of region-specific stress-modulated growth, as well as adjustment of growth and material properties based on patient-specific data yielded clinically useful prediction accuracy while maintaining physiological stress magnitudes. This framework can be further developed for PS surgical simulation.

Predicting curve progression for adolescent idiopathic scoliosis using random forest model

Background

Adolescent Idiopathic Scoliosis (AIS) is a three-dimensional (3D) spinal deformity characterized by coronal curvature and rotational deformity. Predicting curve progression is important for the selection and timing of treatment. Although there is a consensus in the literature regarding prognostic factors associated with curve progression, the order of importance, as well as the combination of factors that are most predictive of curve progression is unknown.

Objectives

(1) create an ordered list of prognostic factors that most contribute to curve progression, and (2) develop and validate a Machine Learning (ML) model to predict the final major Cobb angle in AIS patients.

Methods

193 AIS patients were selected for the current study. Preoperative PA, lateral and lateral bending radiographs were retrospectively obtained from the Shriners Hospitals for Children. Demographic and radiographic features, previously reported to be associated with curve progression, were collected. Sequential Backward Floating Selection (SBFS) was used to select a subset of the most predictive features. Based on the performance of several machine learning methods, a Random Forest (RF) regressor model was used to provide the importance rank of prognostic features and to predict the final major Cobb angle.

Results

The seven most predictive prognostic features in the order of importance were initial major Cobb angle, flexibility, initial lumbar lordosis angle, initial thoracic kyphosis angle, age at last visit, number of levels involved, and Risser "+" stage at the first visit. The RF model predicted the final major Cobb angle with a Mean Absolute Error (MAE) of 4.64 degrees.

Conclusion

A RF model was developed and validated to identify the most important prognostic features for curve progression and predict the final major Cobb angle. It is possible to predict the final major Cobb angle value within 5 degrees error from 2D radiographic features. Such methods could be directly applied to guide intervention timing and optimization for AIS treatment.

Part 1. Review and meta-analysis of studies on modulation of longitudinal bone growth and growth plate activity: A macro-scale perspective

Growth modulation is an emerging method for treatment of angular skeletal deformities such as adolescent idiopathic scoliosis (AIS). The Hueter-Volkmann law, by which growth is stimulated in tension and inhibited in compression, is widely understood, and applied in current growth-modulating interventions such as anterior vertebral body tethering (AVBT) for AIS. However, without quantification of the growth rate effects of tension or compression, the possibility of under- or over- correction exists. A definitive mechanical growth modulation relationship relating to treatment of such skeletal deformities is yet to exist, and the mechanisms by which growth rate is regulated and altered are not fully defined. Review of current literature demonstrates that longitudinal (i.e., lengthwise) growth rate in multiple animal models depend on load magnitude, anatomical location, and species. Additionally, alterations in growth plate morphology and viability vary by loading parameters such as magnitude, frequency, and whether the load was applied persistently or intermittently. The aggregate findings of the reviewed studies will assist in work towards increasingly precise and clinically successful growth modulation methods. Part 1 of this review focuses on the effects of mechanical loading, species, age, and anatomical location on the macro-scale alterations in longitudinal bone growth, as well as factors that affect growth plate material properties. Part 2 considers the effects on micro-scale alterations in growth plate morphology such as zone heights and proportions, chondrocyte viability, and related gene and protein expression.