A Predictive Model of Progression for Adolescent Idiopathic Scoliosis Based on 3D Spine Parameters at First Visit

Correlation of transverse rotation of the spine using surface topography and 3D reconstructive radiography in children with idiopathic scoliosis

PURPOSE

The relationship between axial surface rotation (ASR) measured by surface topography (ST) and axial vertebral rotation (AVR) measured by radiography in the transverse plane is not well defined. This study aimed to: (1) quantify ASR and AVR patterns and their magnitudes from T1 to L5; (2) determine the correlation or agreement between the ASR and AVR; and (3) investigate the relationship between axial rotation differences (ASR-AVR) and major Cobb angle.

METHODS

This is a retrospective study evaluating patients (age 8-18) with IS or spinal asymmetry with both radiographic and ST measurements. Demographics, descriptive analysis, and correlations and agreements between ASR and AVR were evaluated. A piecewise linear regression model was further created to relate rotational differences to Cobb angle.

RESULTS

Fifty-two subjects met inclusion criteria. Mean age was 14.1 ± 1.7 and 39 (75%) were female. Looking at patterns, AVR had maximal rotation at T8, while ASR had maximal rotation at T11 (r = 0.35, P = .006). Cobb angle was 24.1° ± 13.3° with AVR of - 1° ± 4.6° and scoliotic angle was 20.9° ± 11.5° with ASR of - 2.3° ± 6.6°. (ASR-AVR) vs Cobb angle was found to be very weakly correlated with a curve of less than 38.8° (r = 0.15, P = .001).

CONCLUSION

Our preliminary findings support that ASR measured by ST has a weak correlation with estimation of AVR by 3D radiographic reconstruction. This correlation may further help us to understand the application of transverse rotation in some clinical scenarios such as specific casting manipulation, padding mechanism in brace, and surgical correction of rib deformity.

Rib index is a strong surrogate of scoliometric reading in idiopathic scoliosis

INTRODUCTION AND AIM

Scoliometry is not always included in the examination protocol of IS patients. The aim of this report is to examine the degree of correlation of Segmental Rib Index (SRI) to scoliometry, in order for SRI to be used as a surrogate of scoliometric angle of trunk rotation (ATR).

MATERIAL AND METHOD

66 Idiopathic Scoliosis (IS) subjects were studied, with a mean age 12.2 ± 2.9 years, 18 boys and 48 girls: 20 thoracic, 22 thoracolumbar and 24 lumbar curves. The standing lateral spine radiographs (LSR) were obtained and the Segmental Rib Index (SRI) from T1 to T12 were assessed. The ATR was documented.

RESULTS

In all 66 cases with IS the scoliometer readings (ATR) were significantly correlated to the SRI at the T6, T7 and T8 levels. In the thoracic curves SRI and ATR correlations were significant for the levels T6-T12.

DISCUSSION

It was suggested that as long as the patients doesn't have scoliometer measurements, the SRI, could be used as a surrogate for scoliometry. It was also found that in thoracic, thoracolumbar and lumbar level, in both genders, changing from the flexed position to the standing position, the mean trunk asymmetry (TA) decreases. Therefore, if these patients had their TA measured using a scoliometer during the Adams test, their body asymmetry would have been greater than that measured using the SRI method on standing LSR. Consequently, it is evident that the significantly correlated SRI used as a surrogate for the scoliometric assessment of TA is reasonably a strong surrogate.

Associated factors and effects of coronal vertebral wedging angle in thoracic adolescent idiopathic scoliosis

BACKGROUND

Adolescent idiopathic scoliosis (AIS) causes vertebral wedging, but associated factors and the impact of vertebral wedging are still unknown. We investigated associated factors and effects of vertebral wedging in AIS using computed tomography (CT).

METHODS

Preoperative patients (n = 245) with Lenke types-1 and 2 were included. Vertebral wedging, lordosis, and rotation of the apical vertebra were measured by preoperative CT. Skeletal maturity and radiographic global alignment parameters were evaluated. Multiple regression analysis was performed on associated factors for vertebral wedging. Side-bending radiographs were evaluated using multiple regression analysis to calculate the percentage of reduction of Cobb angles to determine curve flexibility.

RESULTS

The mean vertebral wedging angle was 6.8 ± 3.1°. Vertebral wedging angle was positively correlated with proximal thoracic (r = 0.40), main thoracic (r = 0.54), and thoracolumbar/lumbar curves (r = 0.38). By multiple regression, the central sacral vertical line (p = 0.039), sagittal vertical axis (p = 0.049), main thoracic curve (p = 0.008), and thoracolumbar/lumbar curve (p = 0.001) were significant factors for vertebral wedging. In traction and side-bending radiographs there were positive correlations between curve rigidity and the vertebral wedging angle (r = 0.60, r = 0.59, respectively). By multiple regression, thoracic kyphosis (p < 0.001), lumbar lordosis (p = 0.013), sacral slope (p = 0.006), vertebral wedging angle (p = 0.003), and vertebral rotation (p = 0.002) were significant factors for curve flexibility.

CONCLUSIONS

Vertebral wedging angle was found to be highly correlated to coronal Cobb angle, with larger vertebral wedging indicating less flexibility.

Automated design of nighttime braces for adolescent idiopathic scoliosis with global shape optimization using a patient-specific finite element model

Adolescent idiopathic scoliosis is a complex three-dimensional deformity of the spine, the moderate forms of which require treatment with an orthopedic brace. Existing brace design approaches rely mainly on empirical manual processes, vary considerably depending on the training and expertise of the orthotist, and do not always guarantee biomechanical effectiveness. To address these issues, we propose a new automated design method for creating bespoke nighttime braces requiring virtually no user input in the process. From standard biplanar radiographs and a surface topography torso scan, a personalized finite element model of the patient is created to simulate bracing and the resulting spine growth over the treatment period. Then, the topography of an automatically generated brace is modified and simulated over hundreds of iterations by a clinically driven optimization algorithm aiming to improve brace immediate and long-term effectiveness while respecting safety thresholds. This method was clinically tested on 17 patients prospectively recruited. The optimized braces showed a highly effective immediate correction of the thoracic and lumbar curves (70% and 90% respectively), with no modifications needed to fit the braces onto the patients. In addition, the simulated lumbar lordosis and thoracic apical rotation were improved by 5° ± 3° and 2° ± 3° respectively. Our approach distinguishes from traditional brace design as it relies solely on biomechanically validated models of the patient's digital twin and a design strategy that is entirely abstracted from empirical knowledge. It provides clinicians with an efficient way to create effective braces without relying on lengthy manual processes and variable orthotist expertise to ensure a proper correction of scoliosis.

3D Back Contour Metrics in Predicting Idiopathic Scoliosis Progression: Retrospective Cohort Analysis, Case Series Report and Proof of Concept

Adolescent Idiopathic Scoliosis is a 3D spinal deformity commonly characterized by serial radiographs. Patients with AIS may have increased average radiation exposure compared to unaffected patients and thus may be implicated with a modest increase in cancer risk. To minimize lifetime radiation exposure, alternative imaging modalities such as surface topography are being explored. Surface topography (ST) uses a camera to map anatomic landmarks of the spine and contours of the back to create software-generated spine models. ST has previously shown good correlation to radiographic measures. In this study, we sought to use ST in the creation of a risk stratification model. A total of 38 patients met the inclusion criteria for curve progression prediction. Scoliotic curves were classified as progressing, stabilized, or improving, and a predictive model was created using the proportional odds logistic modeling. The results showed that surface topography was able to moderately appraise scoliosis curvatures when compared to radiographs. The predictive model, using demographic and surface topography measurements, was able to account for 86.9% of the variability in the future Cobb angle. Additionally, attempts at classification of curve progression, stabilization, or improvement were accurately predicted 27/38 times, 71%. These results provide a basis for the creation of a clinical tool in the tracking and prediction of scoliosis progression in order to reduce the number of X-rays required.

The Clinical and Imaging Outcome of the Tandem Growing Rod Technique in Early-Onset Scoliosis With the Proximal Upper Thoracic Curve Progression: A Modified Technique of Growing Rod

STUDY DESIGN

Retrospective cohort study.

OBJECTIVE

The orthopaedic ability of traditional GR for severe EOS is limited. The proximal upper thoracic curve may progress during the lengthening procedure, which may lead to coronal imbalance and inhibit the longitudinal growth of the spine. In this retrospective cohort study, we investigated the clinical outcome of tandem GR.

METHODS

We modified the traditional technique by using two groups of GR devices to control the major and the proximal upper thoracic curve, connected the two groups of GR in series, and named it tandem GR. The clinical and imaging outcomes of the new technique were evaluated and compared with traditional technique.

RESULTS

Twenty one patients were enrolled in the tandem GR group, and 30 patients were treated with traditional GR as the control. The baseline parameters were consistent between the two groups. In the tandem GR group, the secondary curve progressed from 24.9 ± 3.9° to 31.4 ± 3.2° (P = .006) in the procedure with the traditional GR and improved to 18.4 ± 4.5° (P = .001) after the switch. Meanwhile, the clavicular angle aggravated from 1.6 ± 1.0° to 2.6 ± .7° (P = .041), and improved to 1.7 ± .8° after changed to the tandem GR (P = .033). At the final of the follow-up, the secondary curve was higher in the control group (27.1 ± 8.3° vs 18.4 ± 4.5°, P = .034), the clavicle angle was 2.4 ± 1.1° in control and 1.7 ± .8° in the tandem GR group (P = .028), the T1-S1 height was 28.2 ± 4.8 cm in the control and 33.3 ± 3.0 cm in the tandem GR group (P = .027). The average growth rate was 1.0 ± .3 cm/year in the control and 1.2 ± .4 cm/year in the tandem GR group (P = .046).

CONCLUSION

Tandem GR can effectively improve the proximal upper thoracic curve progression during the treatment of EOS. Compared with the traditional GR, tandem GR can achieve better curve correction, better shoulder balance, and retains more capacity for longitudinal spine growth.

Higher Baseline Serum Myokine of FSTL1 May Serve as a Potential Predictive Biomarker for Successful Brace Treatment in Girls With Adolescent Idiopathic Scoliosis

STUDY DESIGN

A retrospective case-control study.

OBJECTIVE

This study aimed to investigate whether myokine, which is related to exercise and muscle mass, could serve as a biomarker for predicting bracing outcomes.

SUMMARY OF BACKGROUND DATA

Several risk factors have been documented to be associated with bracing failure in patients with adolescent idiopathic scoliosis (AIS). However, serum biomarkers have not been extensively explored.

PATIENTS AND METHODS

Skeletally immature females with AIS, without previous histories of bracing or surgery, were included. Peripheral blood was collected at the time of the bracing prescription. Baseline serum concentrations of 8 myokines [apelin, fractalkine, brain-derived neurotrophic factor, erythropoietin, osteonectin, fatty-acid-binding protein 3, follistatin-like 1 (FSTL1), and musclin] were measured by multiplex assays. Patients were followed up until weaned from bracing and then designated as a "failure" (defined as Cobb angle progression >5°) or "success." A logistic regression analysis was performed that accounted for serum myokines and skeletal maturity.

RESULTS

We included 117 patients, with 27 in the failure group. Patients in the failure group had lower initial Risser sign and lower baseline serum levels of myokines, including FSTL1 (2217.3 ± 617.0 vs . 1369.3 ± 704.9, P = 0.002), apelin [116.5 (12.0, 335.9) vs . 83.5 (10.5, 221.1), P = 0.016], fractalkine (979.6 ± 457.8 vs . 743.8 ± 456.1, P = 0.020), and musclin [211.3 (16.3, 370.3) vs . 67.8 (15.5, 325.6), P = 0.049]. Following adjusted analysis, serum FSTL1 [odds ratio = 10.460; (2.213-49.453)] was determined to be predictive of bracing effectiveness.

CONCLUSION

Patients who failed AIS bracing had significantly lower mean baseline levels of FSTL1 than those who achieved success. FSTL1 may serve as a biomarker that can inform outcomes after bracing.

Assessment of reliability and validity of a handheld surface spine scanner for measuring trunk rotation in adolescent idiopathic scoliosis

PURPOSE

To assess the reliability and validity of a handheld scanner (SpineScan3D) for trunk rotation measurement in adolescent idiopathic scoliosis (AIS) subjects, as compared with Scoliometer.

METHODS

This was a cross-sectional study with AIS subjects recruited. Biplanar spine radiographs were performed using an EOS imaging system with coronal Cobb angle (CCA) determined. The angle of trunk rotation (ATR) was measured using Scoliometer. SpineScan3D was employed to assess the axial rotation of subjects' back at forward bending, recorded as surface tilt angle (STA). Intra- and inter-examiner repeats were conducted to evaluate the reliability of SpineScan3D.

RESULTS

97 AIS patients were recruited. Intra- and inter-examiner reliability of STA measures were good to excellent in major thoracic and lumbar curves (p < 0.001). A strong correlation was found between STA and ATR measures in both curve types (p < 0.001) with a standard error of the ATR estimate of between 1 and 2 degrees from linear regression models (R squared: 0.8-0.9, p < 0.001). A similar correlation with CCA was found for STA and ATR measures (r: 0.5-0.6, p < 0.002), which also demonstrated a similar sensitivity (72%-74%) and specificity (62%-77%) for diagnosing moderate to severe curves.

CONCLUSION

SpineScan3D is a handheld surface scanner with a potential of wide applications in subjects with AIS. The current study indicated that SpineScan3D is reliable and valid for measuring trunk rotation in AIS subjects, comparable to Scoliometer. Further studies are planned to investigate its measurements in coronal and sagittal planes and the potential of this device as a screening and monitoring tool.

TRIAL REGISTRATION NUMBER (DATE OF REGISTRATION)

HKUCTR-2288 (06 Dec 2017).

LEVEL OF EVIDENCE

Level III.

Predicting Progression in Adolescent Idiopathic Scoliosis at the First Visit by Integrating 2D Imaging and 1D Clinical Information

STUDY DESIGN

Retrospective observational study.

OBJECTIVES

The prediction of curve progression in patients with adolescent idiopathic scoliosis (AIS) remains an unresolved area in orthopedic surgery. To make a rapid meaningful prediction, easily accessible multi-dimensional data at the patient's first consultation should be used. Current studies use clinical growth parameters and numerical values extracted from radiographs to compile a predictive model, leaving out the radiographs themselves. Such practice inevitably wastes a lot of information. Thus, this study aims to create a neural network that can predict AIS progression among patients with curves indicated for bracing by integrating both one-dimensional (1D) clinical and two-dimensional (2D) radiological data collected at the patient's first visit in a fully automated manner.

METHODS

513 idiopathic scoliosis patients indicated for and managed with bracing orthosis were recruited. After exclusion, 463 patients were included in deep learning analysis. Processed first-visit growth parameters and posteroanterior radiographs are used as training inputs and the curve progression outcomes obtained in follow ups are used as binary training outputs. The CapsuleNet architecture was modified and trained accordingly to make a prediction.

RESULTS

The final model achieved 90% sensitivity with an overall accuracy of 73.9% in the prediction of AIS in-brace curve progression by using first-visit multi-dimensional data, outperforming conventional convolutional neural networks.

CONCLUSIONS

This first-ever multidimensional-input model shows promise in serving as a screening tool for AIS in-brace curve progression. The incorporation of such a model into routine AIS diagnostic pipeline can assist orthopedics clinicians in personalizing the most appropriate management for each patient.

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

BACKGROUND

Adolescent idiopathic scoliosis (AIS) affects up to 5% of the population. The efficacy of school-aged screening remains controversial since it is uncertain which curvatures will progress following diagnosis and require treatment. Patient demographics, vertebral morphology, skeletal maturity, and bone quality represent individual risk factors for progression but have yet to be integrated towards accurate prognostication. The objective of this work was to develop composite machine learning-based prediction model to accurately predict AIS curves at-risk of progression.

METHODS

1870 AIS patients with remaining growth potential were identified. Curve progression was defined by a Cobb angle increase in the major curve of ≥6° between first visit and skeletal maturity in curves that exceeded 25°. Separate prediction modules were developed for i) clinical data, ii) global/regional spine X-rays, and iii) hand X-rays. The hand X-ray module performed automated image classification and segmentation tasks towards estimation of skeletal maturity and bone mineral density. A late fusion strategy integrated these domains towards the prediction of progressive curves at first clinic visit.

FINDINGS

Composite model performance was assessed on a validation cohort and achieved an accuracy of 83.2% (79.3-83.6%, 95% confidence interval), sensitivity of 80.9% (78.2-81.9%), specificity of 83.6% (78.8-84.1%) and an AUC of 0.84 (0.81-0.85), outperforming single modality prediction models (AUC 0.65-0.78).

INTERPRETATION

The composite prediction model achieved a high degree of accuracy. Upon incorporation into school-aged screening programs, patients at-risk of progression may be prioritized to receive urgent specialist attention, more frequent follow-up, and pre-emptive treatment.

FUNDING

Funding from The Society for the Relief of Disabled Children was awarded to GKHS.

Prediction of future curve angle using prior radiographs in previously untreated idiopathic scoliosis: natural history from age 6 to after the end of growth (SOSORT 2022 award winner)

PURPOSE

Treatment selection for idiopathic scoliosis is informed by the risk of curve progression. Previous models predicting curve progression lacked validation, did not include the full growth/severity spectrum or included treated patients. The objective was to develop and validate models to predict future curve angles using clinical data collected only at, or both at and prior to, an initial specialist consultation in idiopathic scoliosis.

METHODS

This is an analysis of 2317 patients with idiopathic scoliosis between 6 and 25 years old. Patients were previously untreated and provided at least one prior radiograph prospectively collected at first consult. Radiographs were re-measured blinded to the predicted outcome: the maximum Cobb angle on the last radiograph while untreated. Linear mixed-effect models were used to examine the effect of data from the first available visit (age, sex, maximum Cobb angle, Risser, and curve type) and from other visits while untreated (maximum Cobb angle) and time (from the first available radiograph to prediction) on the Cobb angle outcome. Interactions of the first available angle with time, of time with sex, and time with Risser were also tested.

RESULTS

We included 2317 patients (83% of females) with 3255 prior X-rays where 71% had 1, 21.1% had 2, and 7.5% had 3 or more. Mean age was 13.9 ± 2.2yrs and 81% had AIS. Curve types were: 50% double, 26% lumbar/thoracolumbar-lumbar, 16% thoracic, and 8% other. Cobb angle at the first available X-ray was 20 ± 10° (0-80) vs 29 ± 13° (6-122) at the outcome visit separated by 28 ± 22mths. In the model using data at and prior to the specialist consult, larger values of the following variables predicted larger future curves: first available Cobb angle, Cobb angle on other previous X-ray, and time (with Time2 and Time3) to the target prediction. Larger values on the following variables predicted a smaller future Cobb angle: Risser and age at the first available X-ray, time*Risser and time*female sex interactions. Cross-validation found a median error of 4.5o with 84% predicted within 10°. Similarly, the model using only data from the first specialist consult had a median error of 5.5o with 80% of cases within 10° and included: maximum Cobb angle at first specialist consult, Time, Time2, age, curve type, and both interactions.

CONCLUSIONS

The models can help clinicians predict how much curves would progress without treatment at future timepoints of their choice using simple variables. Predictions can inform treatment prescription or show families why no treatment is recommended. The nonlinear effects of time account for the rapid increase in curve angle at the beginning of growth and the slowed progression after maturity. These validated models predicted future Cobb angle with good accuracy in untreated idiopathic scoliosis over the full growth spectrum.

Post-Maturity Progression in Adolescent Idiopathic Scoliosis Curves of 40° to 50°

BACKGROUND

Adolescent idiopathic scoliosis (AIS) curves of 50° to 75° are inclined to progress and are thus indicated for surgery. Nevertheless, the natural history of curves of 40° to 50° following skeletal maturity remains uncertain and presents a clinical dilemma. The aim of this study was to determine the prevalence, rate, and prognostic indicators of curve progression within this patient group.

METHODS

This was a retrospective study of 73 skeletally mature patients with AIS. Following yearly or more frequent follow-up, patients were stratified as having no progression (<5° increase) or progression (≥5° increase). Those with progression were further differentiated as having standard progression (<2° increase/year) or fast progression (≥2° increase/year). Radiographic parameters (coronal balance, sagittal balance, truncal shift, apical translation, T1 tilt, apical vertebral wedging) and height were determined on skeletal maturity. Parameters that were significantly associated with progression were subject to receiver operating characteristic (ROC) curve analysis.

RESULTS

The average period of post-maturity follow-up was 11.8 years. The prevalence of progression was 61.6%. Among those with progression, the curve increased by a mean of 1.47° ± 1.22° per year, and among those with fast progression, by 3.0° ± 1.2° per year. Thoracic apical vertebral wedging (concave/convex vertebral height × 100) was more apparent in those with progression than in those without progression (84.1 ± 7.5 versus 88.6 ± 3.1; p = 0.003). Increased coronal imbalance (C7 plumb line to central sacral vertebral line) differentiated those with fast progression from others (16.0 ± 11.0 versus 8.7 ± 7.7 mm; p = 0.007). An ROC curve of height-corrected coronal balance demonstrated an area under the curve (AUC) of 0.722, sensitivity of 75.0%, and specificity of 72.5% in identifying fast progression. An ROC curve of height-corrected coronal balance together with apical vertebral wedging to identify those with progression demonstrated an AUC of 0.746, with specificity of 93.7% and sensitivity of 64.5%.

CONCLUSIONS

While the majority of curves progressed, the average rate of progression was slow, and thus, yearly observation was a reasonable management approach. Upon validation in larger cohorts, apical wedging and coronal imbalance may identity patients suited for closer monitoring and early spinal fusion.

LEVEL OF EVIDENCE

Prognostic Level III . See Instructions for Authors for a complete description of levels of evidence.

Deriving a Novel Score for the Stratification of Risk Progression in Early-onset Scoliosis: A Multicenter Initiative

STUDY DESIGN

This was a retrospective multicenter study.

OBJECTIVE

To develop a novel progression risk stratification scoring system for early-onset scoliosis.

SUMMARY OF BACKGROUND DATA

There is a lack of investigations into variables affecting the risk of curve progression in early-onset scoliosis, which prevents stratification. A novel risk score system is needed to help in progression risk estimation.

METHODS

A retrospective analysis was done at three centers, from 1995 to 2020. Scoliosis cases before the age of 10 years, were included. Medical identifier, date of birth, sex, primary diagnosis, curve type, date/modality of treatment, date of follow-up appointments, and Cobb angles, were collected. Five ranks were selected for stratification. Categories with the same ranks were discarded. Point scores started at 0, for the lowest risk, and ended at 4, for highest risk. Iterations of variable combinations were conducted and clinical relevance was determined by evaluating sensitivity, specificity, positive predictive value, and negative predictive value based on score ranges for low and high risk of progression.

RESULTS

A total of 476 (230 males, 246 females) early-onset scoliosis patients were collected. The average age at diagnosis was 4.8 years (SD±2.8 yr). The average follow-up duration was 9.3 years (SD±6.9 yr, range: 5 mo-38 yr). Appointments totaled 2911, giving 2182 observations for the analysis. Patient observations numbered: 800 (36.7%) ending in progression, 1265 (58.0%) for nonprogression, 117 (5.4%) for inadequate follow-up, and 368 (16.9%) for rapid progression. The risk scoring system contained four categories: etiology, age, curve magnitude, and curve type. Categorized point combinations totaled 755, giving 1975 iterations. Sensitivity, specificity, positive predictive value, and negative predictive value were calculated to be 85.8%, 96.5%, 89.7%, and 95.1%, respectively.

CONCLUSION

A novel progression risk score for early-onset scoliosis was derived. The system can reliably differentiate between low and high-risk cases in clinical settings. Further validation in other regions may be important for verifying clinical relevance.

LEVEL OF EVIDENCE

Level 3.

"Will I Need a Brace?": likelihood of curve progression to bracing range in adolescent idiopathic scoliosis

PURPOSE

Bracing treatment for adolescent idiopathic scoliosis (AIS) is typically initiated in skeletally immature patients with primary curves greater than 25°. The goal of this study was to develop a model predicting a patient's likelihood of progressing to bracing treatment.

METHODS

All patients with AIS presenting to a large pediatric spine center with a primary curve below 25° and skeletally immature (Sanders stage 1-6) were included. A patient was considered to have progressed into the bracing range if their primary curve reached a 25° threshold prior to skeletal maturity. Binary logistic regression analysis was performed to predict the likelihood of curve progression into bracing range.

RESULTS

A total of 180 patients (71% female) were included in this study with an average presenting age of 13.2 ± 1.4 years. At presentation, 31 (17%) were pre-peak height velocity, 62 (34%) were at their peak height velocity, and 87 (48%) were in the late adolescent growth stage. The high-risk patient group was defined as Sanders 1-2 and curve size > 10 and < 25° or Sanders 3-6 and curve size > 20 but < 25°. Those in the high-risk group demonstrated an over 5 times higher risk of progression to bracing range when accounting for age, sex, and curve location (OR: 5.168, 95% CI: 2.212-12.071, p < 0.001).

CONCLUSION

Patient's curve magnitude and skeletal maturity can be used to predict their likelihood of curve progression to greater than 25° and thus require bracing treatment. Orthopaedic providers can consider earlier treatment interventions or stricter follow-up adherence for patients at high risk for progression.

LEVEL OF EVIDENCE

3-retrospective cohort study.

XR (Extended Reality: Virtual Reality, Augmented Reality, Mixed Reality) Technology in Spine Medicine: Status Quo and Quo Vadis

In recent years, with the rapid advancement and consumerization of virtual reality, augmented reality, mixed reality, and extended reality (XR) technology, the use of XR technology in spine medicine has also become increasingly popular. The rising use of XR technology in spine medicine has also been accelerated by the recent wave of digital transformation (i.e., case-specific three-dimensional medical images and holograms, wearable sensors, video cameras, fifth generation, artificial intelligence, and head-mounted displays), and further accelerated by the COVID-19 pandemic and the increase in minimally invasive spine surgery. The COVID-19 pandemic has a negative impact on society, but positive impacts can also be expected, including the continued spread and adoption of telemedicine services (i.e., tele-education, tele-surgery, tele-rehabilitation) that promote digital transformation. The purpose of this narrative review is to describe the accelerators of XR (VR, AR, MR) technology in spine medicine and then to provide a comprehensive review of the use of XR technology in spine medicine, including surgery, consultation, education, and rehabilitation, as well as to identify its limitations and future perspectives (status quo and quo vadis).

Application of deep learning upon spinal radiographs to predict progression in adolescent idiopathic scoliosis at first clinic visit

BACKGROUND

Prediction of curve progression risk in adolescent idiopathic scoliosis (AIS) remains elusive. Prior studies have revealed the potential for three-dimensional (3D) morphological parameters to prognosticate progression, but these require specialized biplanar imaging equipment and labor-intensive software reconstruction. This study aimed to formulate a deep learning model with standing posteroanterior (PA) X-rays at first clinic visit to differentiate between progressive (P) and non-progressive (NP) curves.

METHODS

For this retrospective cohort study, we identified patients presenting with AIS between October 2015 to April 2020 at our tertiary referral centre. Patients with mild curvatures (11 - 30o) who were skeletally immature (Risser sign of ≤2) were recruited. Patients receiving biplanar X-ray radiographs (EOS™) were divided between a training-cross-validation cohort (328 patients) and independent testing cohort (110 patients). Another 52 patients receiving standard PA spinal X-rays were recruited for cross-platform validation. Following 3D reconstruction, we designated the major curve apex upon PA X-rays as the region of interest (ROI) for machine learning. A self-attentive capsule network was constructed to differentiate between curves manifesting P and NP trajectories. A two-stage transfer learning strategy was introduced to pre-train and fine-tune the model. Model performance (accuracy, sensitivity, specificity) was compared to that of traditional convolutional neural networks (CNNs) and a clinical parameter-based logistic regression model.

FINDINGS

3D reconstruction identified that apical rotation of the major curve and torsion were significantly different between P and NP curve trajectories. Our predictive model utilizing an ROI centered on the major curve apex achieved an accuracy of 76.6%, a sensitivity of 75.2% and a specificity of 80.2% upon independent testing. Cross-platform performance upon standard standing PA X-rays yielded an accuracy of 77.1%, a sensitivity of 73.5% and a specificity of 81.0%. Errors in prediction occurred when the degree of apical rotation / torsion was discrepant from that of the subsequent curve trajectory but could be rectified by considering serial X-rays. Performance was superior to that of traditional CNNs as well as clinical parameter-based regression models.

INTERPRETATION

This is the first report of automated prediction of AIS curve progression based on radiomics and deep learning, towards directing treatment strategy at first visit. Patients predicted to be at-risk of progression may be counselled to receive early bracing with enforcement of treatment compliance. Over-treatment may be avoided in curves deemed to be non-progressive. Results need to be consolidated in larger sample populations of different ethnicities.

FUNDING

The Society for the Relief of Disabled Children (SRDC).

Accounting for Biomechanical Measures from Musculoskeletal Simulation of Upright Posture Does Not Enhance the Prediction of Curve Progression in Adolescent Idiopathic Scoliosis

A major clinical challenge in adolescent idiopathic scoliosis (AIS) is the difficulty of predicting curve progression at initial presentation. The early detection of progressive curves can offer the opportunity to better target effective non-operative treatments, reducing the need for surgery and the risks of related complications. Predictive models for the detection of scoliosis progression in subjects before growth spurt have been developed. These models accounted for geometrical parameters of the global spine and local descriptors of the scoliotic curve, but neglected contributions from biomechanical measurements such as trunk muscle activation and intervertebral loading, which could provide advantageous information. The present study exploits a musculoskeletal model of the thoracolumbar spine, developed in AnyBody software and adapted and validated for the subject-specific characterization of mild scoliosis. A dataset of 100 AIS subjects with mild scoliosis and in pre-pubertal age at first examination, and recognized as stable (60) or progressive (40) after at least 6-months follow-up period was exploited. Anthropometrical data and geometrical parameters of the spine at first examination, as well as biomechanical parameters from musculoskeletal simulation replicating relaxed upright posture were accounted for as predictors of the scoliosis progression. Predicted height and weight were used for model scaling because not available in the original dataset. Robust procedure for obtaining such parameters from radiographic images was developed by exploiting a comparable dataset with real values. Six predictive modelling approaches based on different algorithms for the binary classification of stable and progressive cases were compared. The best fitting approaches were exploited to evaluate the effect of accounting for the biomechanical parameters on the prediction of scoliosis progression. The performance of two sets of predictors was compared: accounting for anthropometrical and geometrical parameters only; considering in addition the biomechanical ones. Median accuracy of the best fitting algorithms ranged from 0.76 to 0.78. No differences were found in the classification performance by including or neglecting the biomechanical parameters. Median sensitivity was 0.75, and that of specificity ranged from 0.75 to 0.83. In conclusion, accounting for biomechanical measures did not enhance the prediction of curve progression, thus not supporting a potential clinical application at this stage.

The use of statistical modelling to identify important parameters for the shape of the torso following surgery for adolescent idiopathic scoliosis

The surgical strategy in adolescent idiopathic scoliosis (AIS) aims to recreate the symmetry of the torso. This requires the minimisation of both the size of the scoliosis and the angulation between the sides of the torso, along with the recreation of a normal thoracic kyphosis. This study uses predictive modelling to identify the significance of the value of the pre-operative parameters, and the change in the magnitude of the parameters as a result of an operation on the shape of the torso using the 'most prominent points'; two areas of maximum prominence on either side of the spine with x, y and z coordinates. The pre-operative values, and the change in magnitude between the pre and post-operative values, for scoliosis, kyphosis and skin angulation from a group of Lenke 1 convex to the right AIS were analysed with measures collected using Integrated Spine Imaging System 2 surface topography and compared with those without visible spinal deformity. The models best explained the z coordinate and least well explained the x coordinate, although there was a contribution to all of the models that remained unexplained. The parameters that affected the position of the coordinates in the model differed between the models. This confirms that surgically altering the shape of the spine and torso whilst correcting an AIS does not lead to a symmetrical torso. There are as yet, undefined factors which contribute to the shape of the torso and which if identified and corrected surgically would lead to greater symmetry post-operatively.

Brace Effectiveness Is Related to 3-Dimensional Plane Parameters in Patients with Adolescent Idiopathic Scoliosis

BACKGROUND

Although scoliosis is a 3-dimensional (3D) deformity, little research has been performed on the use of 3D imaging in brace curve correction. The purpose of the present study was to determine the effect of axial-plane parameters on the outcomes of bracing with a thoracolumbosacral orthosis for adolescent idiopathic scoliosis.

METHODS

This prospective longitudinal cohort study included patients with adolescent idiopathic scoliosis who fulfilled the criteria for bracing according to the Scoliosis Research Society, and was conducted from the time the patient began wearing the brace through a minimum follow-up of 2 years or until a surgical procedure was performed. Radiographs made with use of an EOS Imaging System were used to reconstruct 3D images of the spine at the pre-brace, immediate in-brace, 1-year in-brace, and latest follow-up out-of-brace stages. Univariate and multiple linear regressions were performed to determine the association between axial rotation correction and curve progression at the time of the latest follow-up. Logistic regressions were performed to model the probability of risk of progression.

RESULTS

Fifty-three patients were enrolled, and 46 patients were included in the analysis. At the time of the latest follow-up, 30 patients did not experience curve progression and 16 patients had curve progression. There was no difference in baseline demographic characteristics between groups. For the transverse-plane parameters, there was a significant difference between non-progression and progression groups in pre-brace apical vertebral rotation (4.5° ± 11.2° compared with -2.4° ± 9.8°, respectively; p = 0.044) and in 1-year in-brace apical vertebral rotation correction velocity (2.0° ± 5.0°/year compared with -1.7° ± 4.4°/year, respectively; p = 0.016). Logistic regression analysis showed that pre-brace apical vertebral rotation (odds ratio, 1.063; 95% confidence interval, 1.000 to 1.131; p = 0.049) and 1-year in-brace apical vertebral rotation correction velocity (odds ratio, 1.19; 95% confidence interval, 1.021 to 1.38; p = 0.026) were associated with an increased risk of curve progression. There was no difference in Scoliosis Research Society 22-Item scores between patients who experienced curve progression and those who did not.

CONCLUSIONS

In this prospective study, we demonstrated that axial-plane parameters and the correction of these parameters during bracing are related to the successful use of the brace.

LEVEL OF EVIDENCE

Prognostic Level II. See Instructions for Authors for a complete description of levels of evidence.

Predicting Adolescent Idiopathic Scoliosis among Chinese Children and Adolescents

Objective

Adolescent idiopathic scoliosis (AIS) affects 1%-4% of adolescents in the early stages of puberty, but there is still no effective prediction method. This study aimed to establish a prediction model and validated the accuracy and efficacy of this model in predicting the occurrence of AIS.

Methods

Data was collected from a population-based school scoliosis screening program for AIS in China. A sample of 884 children and adolescents with the radiological lateral Cobb angle ≥ 10° was classified as an AIS case, and 895 non-AIS subjects with a Cobb angle < 10° were randomly selected from the screening system. All selected subjects were screened by visual inspection of clinical signs, the Adam's forward-bending test (FBT), and the measurement of angle of trunk rotation (ATR). LR and receiver operating characteristic (ROC) curves were used to preliminarily screen the influential factors, and LR models with different adjusted weights were established to predict the occurrence of AIS.

Results

Multivariate LR and ROC curves indicated that angle of thoracic rotation (adjusted odds ratios (AOR) = 5.18 − 10.06), angle of thoracolumbar rotation (AOR = 4.67 − 7.22), angle of lumbar rotation (AOR = 6.97 − 8.09), scapular tilt (area under the curve (AUC) = 0.77, 95% CI: 0.75-0.80), shoulder-height difference, lumbar concave, and pelvic tilt were the risk predictors for AIS. LR models with different adjusted weights (by AOR, AUC, and AOR+AUC) performed similarly in predicting the occurrence of AIS compared with multivariate LR. The sensitivity (82.55%-83.27%), specificity (82.59%-83.33%), Youden's index (0.65-0.67), positive predictive value (82.85%-83.58%), negative predictive value (82.29%-83.03%), and total accuracy (82.57%-83.30%) manifested that LR could accurately identify patients with AIS.

Conclusions

LR model is a relatively high accurate and feasible method for predicting AIS. Increased performance of LR models using clinically relevant variables offers the potential to early identify high-risk groups of AIS.