The Changing Paradigm in the Management of Spinal Deformities

Guidelines for the conservative treatment of spinal deformities - Questionnaire for a Delphi consensus

Background

Spinal deformity is the oldest disease known to humankind. Many types of treatment methods, including both conservative and surgical, are in use.

Objective

We aimed to validate a published guideline protocol based on the conservative treatment of spinal deformities.

Method

A modified Delphi technique was used with a questionnaire sent out to professionals worldwide regarding the conservative treatment of spinal deformities.

Results

Our study was completed after two rounds. A strong level of agreement of 80% and more (consensus cut-off point) was achieved in most questions in the first round. Some statements were below this margin, and they were sent to the participants via email in the second round for re-evaluation. Consensus was achieved in almost all of the statements in the second round. Only two items did not reach the cut-off point but were close to this value.

Conclusion

This proposed Guideline Protocol was approved by the participants using the Delphi method and can be used as a valid tool for the conservative treatment of spinal deformities.

Clinical implications

A conservative treatment guideline in spinal deformity management, will provide consistency in treatment and will facilitate comparability with surgery. It will be useful in determining the cost-effectiveness of treatment and in choosing the right patient for the right method of treatment. This guideline might help in this context, and may also create a systematic method for clinicians to use as a reference in both research and clinical practice.

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

Markerless 3D surface topography for scoliosis diagnosis and brace treatment can avoid repeated radiation known from standard X-ray analysis and possible side effects. Combined with the method of torso asymmetry analysis, curve severity and progression can be evaluated with high reliability. In the current study, a machine learning approach was utilised to classify scoliosis patients based on their trunk surface asymmetry pattern. Frontal X-ray and 3D scanning analysis with a clinical classification based on Cobb angle and spinal curve pattern were performed with 50 patients. Similar as in a previous study, each patient's trunk 3D reconstruction was used for an elastic registration of a reference surface mesh with fixed number of vertices. Subsequently, an asymmetry distance map between original and reflected torso was calculated. A fully connected neural network was then utilised to classify patients regarding their Cobb angle (mild, moderate, severe) and an Augmented Lehnert-Schroth (ALS) classification based on their full torso asymmetry distance map. The results reveal a classification success rate of 90% (SE: 80%, SP: 100%) regarding the curve severity (mild vs moderate-severe) and 50-72% regarding the ALS group. Identifying patient curve severity and treatment group was reasonably possible allowing for a decision support during diagnosis and treatment planning. Graphical abstract.

Innovative decision support for scoliosis brace therapy based on statistical modelling of markerless 3D trunk surface data

Recently markerless 3D scanning methods receive an increased interest for therapy planning and brace treatment of patients with scoliosis. This avoids repeated radiation known from standard X-Ray analysis. Several authors introduced the method of asymmetry distance maps in order to classify curve severity and progression. The current work extends this approach by statistical mean shape 3D models of the human trunk in order to classify patients. 50 patients were included in this study performing frontal X-ray and 3D scanning analysis. All patients were classified by a clinician according to their Cobb angle and spinal curve pattern (Augmented-Lehnert-Schroth ALS). 3D reconstructions of each patient trunk were processed in a way to elastically register a reference surface mesh with fixed number of data points. Mean 3D shape models were generated for each curve pattern. An asymmetry distance map was then calculated for each patient and mean shape model. Single patient 3D reconstructions were classified according to severity and ALS treatment group. Optimal sensitivity and specificity was 97%/39% thoracic and 87%/42% lumbar respectively for detecting mild and moderate-severe patients. Identifying a treatment group was possible for three combined groups allowing to support decisions during diagnosis and therapy planning.