Image-Guided Tethering Spine Surgery With Outcome Prediction Using Spatio-Temporal Dynamic Networks

Investigating the long-term outcomes and efficacy of surgical intervention in patients with adolescent idiopathic scoliosis and Cobb angles ranging between 40 and 50 degrees

PURPOSE

Patients with adolescent idiopathic scoliosis (AIS) are either managed with non-operative strategies or surgery depending on the severity of lateral curvature and impact on quality of life. However, supportive evidence for the appropriate treatment approach is lacking in AIS patients with Cobb angles between 40 and 50 degrees. Therefore, we investigated differences in long-term patient-centered outcomes in AIS patients with Cobb angles between 40 and 50 degrees who received either operative or non-operative management.

METHODS

A total of 919 patients aged 10-21 years old with adolescent idiopathic scoliosis and 40-50 degree Cobb angles were identified from the HARMS Study Group (HSG) registry and dichotomized based on operative or non-operative management. Baseline and 2 year follow-up SRS-22 scores from these patients were analyzed for significant differences between the total score values, domain values, and the magnitude of score change over time using multiple comparisons analyses. Multivariable regressions adjusting for age, body mass index, location of spinal deformity, and management strategy were also performed.

RESULTS

Operative versus non-operative strategy was significantly, independently associated with differences in SRS-22 total and domain scores over time (effect likelihood ratio test, p < 0.03 for all regressions). Operatively managed patients had significantly greater improvement in SRS-22 total and domain scores over the follow-up duration compared to non-operatively managed patients (p < 0.02 for all comparisons).

CONCLUSIONS

This preliminary analysis suggests that operatively managed patients may have had better long-term outcomes than non-operatively managed patients within this AIS subpopulation. These findings support the need for further prospective investigation to determine the optimal management strategy to improve evidence-based, patient-reported outcomes for AIS patients with Cobb angles between 40 and 50 degrees.

LEVEL OF EVIDENCE

Level III.

Prediction of the upright articulated spine shape in the operating room using conditioned neural kernel fields

Anterior vertebral tethering (AVT) is a non-invasive spine surgery technique, treating severe spine deformations and preserving lower back mobility. However, patient positioning and surgical strategies greatly influences postoperative results. Predicting the upright geometry from pediatric spines is needed to optimize patient positioning in the operating room (OR) and improve surgical outcomes, but remains a complex task due to immature bone properties. We propose a framework used in the OR predicting the upright spine geometry at the first visit following surgery in idiopathic scoliosis patients. The approach first creates a 3D model of the spine while the patient is on the operating table. For this, multiview Transformers that combine images from different viewpoints are used to generate the intraoperative pose. The postoperative upright shape is then predicted on-the-fly using implicit neural fields, which are trained from geometries at different time points and conditioned with surgical parameters. A Signed Distance Function for shape constellations is used to handle the variability in spine appearance, capturing a disentangled latent domain of the articulation vectors, with separate encoding vectors representing both articulation and shape parameters. A regularization criterion based on a pre-trained group-wise trajectory of spine transformations generates complete spine models. A training set of 652 patients with 3D models was used to train the model, tested on a distinct cohort of 83 surgical patients. The framework based on neural kernels predicted upright 3D geometries with a mean 3D error of 1.3±0.5mm in landmarks points, and IoU of 95.9% in vertebral shapes when compared to actual postop models, falling within the acceptable margins of error below 2 mm.

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.

Current development and prospects of deep learning in spine image analysis: a literature review

BACKGROUND AND OBJECTIVE

As the spine is pivotal in the support and protection of human bodies, much attention is given to the understanding of spinal diseases. Quick, accurate, and automatic analysis of a spine image greatly enhances the efficiency with which spine conditions can be diagnosed. Deep learning (DL) is a representative artificial intelligence technology that has made encouraging progress in the last 6 years. However, it is still difficult for clinicians and technicians to fully understand this rapidly evolving field due to the diversity of applications, network structures, and evaluation criteria. This study aimed to provide clinicians and technicians with a comprehensive understanding of the development and prospects of DL spine image analysis by reviewing published literature.

METHODS

A systematic literature search was conducted in the PubMed and Web of Science databases using the keywords "deep learning" and "spine". Date ranges used to conduct the search were from 1 January, 2015 to 20 March, 2021. A total of 79 English articles were reviewed.

KEY CONTENT AND FINDINGS

The DL technology has been applied extensively to the segmentation, detection, diagnosis, and quantitative evaluation of spine images. It uses static or dynamic image information, as well as local or non-local information. The high accuracy of analysis is comparable to that achieved manually by doctors. However, further exploration is needed in terms of data sharing, functional information, and network interpretability.

CONCLUSIONS

The DL technique is a powerful method for spine image analysis. We believe that, with the joint efforts of researchers and clinicians, intelligent, interpretable, and reliable DL spine analysis methods will be widely applied in clinical practice in the future.

Intra-operative forecasting of growth modulation spine surgery outcomes with spatio-temporal dynamic networks

PURPOSE

In adolescent idiopathic scoliosis (AIS), non-invasive surgical techniques such as anterior vertebral body tethering (AVBT) enable to treat patients with mild and severe degrees of deformity while maintaining lower lumbar motion by avoiding spinal fusion. However, multiple features and characteristics affect the overall patient outcome, notably the 3D spine geometry and bone maturity, but also from decisions taken intra-operatively such as the selected tethered vertebral levels, which makes it difficult to anticipate the patient response.

METHODS

We propose here a forecasting method which can be used during AVBT surgery, exploiting the spatio-temporal features extracted from a dynamic networks. The model learns the corrective effect from the spine's different segments while taking under account the time differences in the initial diagnosis and between the serial acquisitions taken before and during surgery. Clinical parameters are integrated through an attention-based decoder, allowing to associate geometrical features to patient status. Long-term relationships allow to ensure regularity in geometrical curve prediction, using a manifold-based smoothness term to regularize geometrical outputs, capturing the temporal variations of spine correction.

RESULTS

A dataset of 695 3D spine reconstructions was used to train the network, which was evaluated on a hold-out dataset of 72 scoliosis patients using the baseline 3D reconstruction obtained prior to surgery, yielding an overall reconstruction error of [Formula: see text]mm based on pre-identified landmarks on vertebral bodies. The model was also tested prospectively on a separate cohort of 15 AIS patients, demonstrating the integration within the OR theatre.

CONCLUSION

The proposed predictive network allows to intra-operatively anticipate the geometrical response of the spine to AVBT procedures using the dynamic features.