Clinical classification of scoliosis patients using machine learning and markerless 3D surface trunk data

A Novel Classification of Juvenile and Adolescent Idiopathic Scoliosis for Conservative Treatment

BACKGROUND

The operative classification of scoliosis is well-developed but inadequate for guiding conservative treatment. The current conservative classification for juvenile and adolescent idiopathic scoliosis (JAIS) exhibits noticeable deficiencies. This study aimed to establish the Peking Union Medical College Hospital (PUMCH) classification and assess its clinical value in the conservative treatment of JAIS.

METHODS

This study consisted of 2 parts. First, it involved a retrospective analysis of patients treated for JAIS in the Department of Rehabilitation Medicine, the ∗∗∗ Union Medical College Hospital, between January 2013 and June 2020. Second, it involved an ambispective cohort study that enrolled patients with JAIS in the above hospital between July and December 2020.

RESULTS

A total of 989 patients with JAIS were enrolled, with 899 patients for establishing the PUMCH classification and 90 patients with JAIS for validating the PUMCH classification. The classification demonstrated an average reliability of 88.22% with a kappa coefficient of 0.862. After 1 week, the remeasured results presented a mean reproducibility of 92.78% and a kappa coefficient of 0.908. After 1-year follow-up, the Cobb angle decreased significantly from 16.61 ± 2.88° to 12.16°± 9.97° (P = 0.002) in 51 patients with PUMCH-scoliosis-specific exercise (SSE) treatment, while the Cobb angle increased significantly from 15.74 ± 2.75° to 17.64 ± 5.60° (P = 0.014) in 39 patients without PUMCH-SSE treatment.

CONCLUSIONS

The PUMCH-SSE classification demonstrates good inter-observer reliability and intra-observer reproducibility. In addition, the classification may be used to guide the conservative treatment of JAIS in clinical settings.

Application of machine learning standardized integral area algorithm in measuring the scoliosis

This study was to develop a computer vision evaluation method to automatically measure the degree of scoliosis based on the machine learning algorithm. For the X-ray images of 204 patients with idiopathic scoliosis who underwent full-spine radiography, histogram equalization of original image was performed before a flipping method was used to magnify asymmetric elements, search for the global maximum pixel value in each line, and scan local maximal pixel value, with the intersection set of two point sets being regarded as candidate anchor points. All fine anchors were fitted with cubic spline algorithm to obtain the approximate curve of the spine, and the degree of scoliosis was measured by the standardized integral area. All measured data were analyzed. In manual measurement, the Cobb angle was 11.70-25.00 (20.15 ± 3.60), 25.20-44.70 (33.89 ± 5.41), and 45.10-49.40 (46.98 ± 1.25) in the mild, moderate and severe scoliosis group, respectively, whereas the value for the standardized integral area algorithm was 0.072-0.298 (0.185 ± 0.040), 0.100-0.399 (0.245 ± 0.050), and 0.246-0.901 (0.349 ± 0.181) in the mild, moderate and severe scoliosis group, respectively. Correlation analysis between the manual measurement of the Cobb angle and the evaluation of the standardized integral area algorithm demonstrated the Spearman correlation coefficient r = 0.643 (P < 0.001). There was a positive correlation between the manual measurement of the Cobb angle and the measurement of the standardized integral area value. Two methods had good consistency in evaluating the degree of scoliosis. ROC curve analysis of the standardized integral area algorithm to measure the degree of scoliosis showed he cutoff value of the standardized integral area algorithm was 0.20 for the moderate scoliosis with an AUC of 0.865, sensitivity 0.907, specificity 0.635, accuracy 0.779, positive prediction value 0.737 and negative prediction value 0.859, and the cutoff value of the standardized integral area algorithm was 0.40 for the severe scoliosis with an AUC of 0.873, sensitivity 0.188, specificity 1.00, accuracy 0.936, positive prediction value 1 and a negative prediction value 0.935. Using the standardized integral area as an independent variable and the Cobb angle as a dependent variable, a linear regression equation was established as Cobb angle = 13.36 + 70.54 × Standardized area, the model has statistical significance. In conclusion, the integrated area algorithm method of machine learning can quickly and efficiently assess the degree of scoliosis and is suitable for screening the degree of scoliosis in a large dataset as a useful supplement to the fine measurement of scoliosis Cobb angle.

Current Applications of Machine Learning in Spine: From Clinical View

STUDY DESIGN

Narrative review.

OBJECTIVES

This review aims to present current applications of machine learning (ML) in spine domain to clinicians.

METHODS

We conducted a comprehensive PubMed search of peer-reviewed articles that were published between 2006 and 2020 using terms (spine, spinal, lumbar, cervical, thoracic, machine learning) to examine ML in spine. Then exclude research of other domain, case report, review or meta-analysis, and which without available abstract or full text.

RESULTS

Total 1738 articles were retrieved from database, and 292 studies were finally included. Key findings of current applications were compiled and summarized in this review. Main clinical applications of those techniques including image processing, diagnosis, decision supporting, operative assistance, rehabilitation, surgery outcomes, complications, hospitalization and cost.

CONCLUSIONS

ML had achieved excellent performance and hold immense potential in spine. ML could help clinical staff to improve medical level, enhance work efficiency, and reduce adverse events. However more randomized controlled trials and improvement of interpretability are essential to clinicians accepting models' assistance in real work.

Impact of Gensingen brace treatment on Lenke 5 curvatures and chronic low back pain in late adolescent and adult scoliosis patients

Background

Lenke 5C (lumbar and or thoracolumbar) curve patterns lead to loss of lumbar lordosis which is associated with low back pain in later adulthood. We undertook our study to investigate if brace treatment may have an effect on low back pain and on improving the cosmetic appearance in late adolescents and adults.

Objectives

To estimate if conservative treatment may have an effect on pain in single lumbar curvatures in late adolescent and adult patients with Adolescent Idiopathic Scoliosis (AIS) using a Gensingen Brace by Weiss (GBW).

Method

We investigated AIS patients with Lenke 5C pattern who wore a GBW prospectively. The inclusion criteria of our study were age over 15 years, Cobb angle greater than 20° before treatment and Risser 4 or 5. A verbal pain rating scale was used (no pain, mild pain, moderate pain, severe pain, very severe pain).

Results

A total of 26 patients met the inclusion criteria. The average age was 17.7 years and the average Cobb angle was 41.5°. Nineteen patients (73.1%) experienced mild or moderate chronic low back pain before treatment and seven patients (26.9%) were asymptomatic but seeking treatment for cosmetic reasons. At follow-up, a 23% correction of the curve was achieved. All previously symptomatic patients reported that they no longer experienced low back pain after having worn the brace regularly.

Conclusion

High correction bracing seems to have a positive effect on the curve and on chronic low back pain in patients with a scoliosis and a Lenke 5C curve pattern.

Clinical implications

High correction, pattern specific bracing with a GBW may be applied aiming at reducing structural curves and chronic low back pain in late adolescent and adult patients with AIS and with a single lumbar curvature.

Brace treatment for patients with scoliosis: State of the art

Background

Physiotherapy, brace applications or surgery are the treatment options utilised to manage patients with scoliosis. Many different brace applications are used, and the success rates of orthoses vary.

Objectives

Brace applications can have detrimental impacts on the patient leading to physical discomfort, psychological discomfort, and in some instance the use of braces may even be painful. Therefore, future developments in this field should be aimed at improving the success rate and reducing physical distress experienced by the patient while using brace applications. The purpose of this article is to provide recommendations with respect to the most appropriate bracing approach in general.

Method

A narrative review of the scientific literature was carried out to substantiate the statements made in this article.

Results

The most important braces provided for the treatment of patients with scoliosis and the treatment results that can be achieved are presented and discussed, taking into account the most recent systematic reviews. A wide range of success rates have been found for the different brace applications.

Conclusion

Given that brace application may impact the patient leading to physical discomfort and psychological distress, good quality management in brace application for patients with scoliosis is needed to ensure the best possible outcome and the least stressful management.

Clinical implications

Safety in brace application for patients with scoliosis needs improvement. The use of standardised and reliable computer aided design (CAD) libraries and appropriate patient information based on published guidelines is suggested.

Investigating Human Torso Asymmetries: An Observational Longitudinal Study of Fluctuating and Directional Asymmetry in the Scoliotic Torso

The presence of directional and fluctuating asymmetry in adolescent idiopathic scoliosis has not been deeply studied. We aimed to test the presence of both in a scoliosis group and a control group. 24 patients with adolescent idiopathic scoliosis and 24 control subjects were subjected to geometric morphometrics analyses to address our main hypotheses and to make qualitative visualizations of the 3D shape changes in patients with scoliosis. Our results support the hypothesis that both asymmetric traits are present in the scoliosis and control groups, but to a greater degree in patients. A qualitative visualization tool that allows us to measure the impact that directional and fluctuating asymmetry have on the 3D shape of our patients has been developed. Adolescent idiopathic scoliosis is the result of developmental instabilities during growth and the visualization of the 3D shape changes in response to both asymmetric variables has shown different morphological behaviors. Measuring these variables is important, as they can prevent the localization and deformation that is expected to occur during the course of scoliosis in every individual patient and therefore acts as a key clinical finding that may be used in the prognosis of the condition.