The Influence of Baseline Clinical Status and Surgical Strategy on Early Good to Excellent Result in Spinal Lumbar Arthrodesis: A Machine Learning Approach

Predicting pediatric patient rehabilitation outcomes after spinal deformity surgery with artificial intelligence

BACKGROUND

Adolescent idiopathic scoliosis (AIS) is the most common type of scoliosis, affecting 1-4% of adolescents. The Scoliosis Research Society-22R (SRS-22R), a health-related quality-of-life instrument for AIS, has allowed orthopedists to measure subjective patient outcomes before and after corrective surgery beyond objective radiographic measurements. However, research has revealed that there is no significant correlation between the correction rate in major radiographic parameters and improvements in patient-reported outcomes (PROs), making it difficult to incorporate PROs into personalized surgical planning.

METHODS

The objective of this study is to develop an artificial intelligence (AI)-enabled surgical planning and counseling support system for post-operative patient rehabilitation outcomes prediction in order to facilitate personalized AIS patient care. A unique multi-site cohort of 455 pediatric patients undergoing spinal fusion surgery at two Shriners Children's hospitals from 2010 is investigated in our analysis. In total, 171 pre-operative clinical features are used to train six machine-learning models for post-operative outcomes prediction. We further employ explainability analysis to quantify the contribution of pre-operative radiographic and questionnaire parameters in predicting patient surgical outcomes. Moreover, we enable responsible AI by calibrating model confidence for human intervention and mitigating health disparities for algorithm fairness.

RESULTS

The best prediction model achieves an area under receiver operating curve (AUROC) performance of 0.86, 0.85, and 0.83 for individual SRS-22R question response prediction over three-time horizons from pre-operation to 6-month, 1-year, and 2-year post-operation, respectively. Additionally, we demonstrate the efficacy of our proposed prediction method to predict other patient rehabilitation outcomes based on minimal clinically important differences (MCID) and correction rates across all three-time horizons.

CONCLUSIONS

Based on the relationship analysis, we suggest additional attention to sagittal parameters (e.g., lordosis, sagittal vertical axis) and patient self-image beyond major Cobb angles to improve surgical decision-making for AIS patients. In the age of personalized medicine, the proposed responsible AI-enabled clinical decision-support system may facilitate pre-operative counseling and shared decision-making within real-world clinical settings.

Discogenic Low Back Pain: Anatomic and Pathophysiologic Characterization, Clinical Evaluation, Biomarkers, AI, and Treatment Options

Discogenic low back pain (LBP) is a significant clinical condition arising from degeneration of the intervertebral disc, a common yet complex cause of chronic pain, defined by fissuring in the annulus fibrosus resulting in vascularization of growing granulation tissue and growth of nociceptive nerve fibers along the laceration area. This paper delves into the anatomical and pathophysiological underpinnings of discogenic LBP, emphasizing the role of intervertebral disc degeneration in the onset of pain. The pathogenesis is multifactorial, involving processes like mitochondrial dysfunction, accumulation of advanced glycation end products, and pyroptosis, all contributing to disc degeneration and subsequent pain. Despite its prevalence, diagnosing discogenic LBP is challenging due to the overlapping symptoms with other forms of LBP and the absence of definitive diagnostic criteria. Current diagnostic approaches include clinical evaluations, imaging techniques, and the exploration of potential biomarkers. Treatment strategies range from conservative management, such as physical therapy and pharmacological interventions, to more invasive procedures such as spinal injections and surgery. Emerging therapies targeting molecular pathways involved in disc degeneration are under investigation and hold potential for future clinical application. This paper highlights the necessity of a multidisciplinary approach combining clinical, imaging, and molecular data to enhance the accuracy of diagnosis and the effectiveness of treatment for discogenic LBP, ultimately aiming to improve patient outcomes.

Patient-reported outcome of lumbar decompression with instrumented fusion for low-grade spondylolisthesis: influence of pathology and baseline symptoms

INTRODUCTION

Low-grade isthmic and degenerative spondylolisthesis (DS) of the lumbar spine are distinct pathologies but both can be treated with lumbar decompression with fusion. In a very large cohort, we compared patient-reported outcome in relation to the pathology and chief complaint at baseline.

METHODS

This was a retrospective analysis using the EUROSPINE Spine Tango Registry. We included 582 patients (age 60 ± 15 years; 65% female), divided into four groups based on two variables: type of spondylolisthesis and chief pain complaint (leg pain (LP) versus back pain). Patients completed the COMI preoperatively and up to 5 years follow-up (FU), and rated global treatment outcome (GTO). Regression models were used to predict COMI-scores at FU. Pain scores and satisfaction ratings were analysed.

RESULTS

All patients experienced pronounced reductions in COMI scores. Relative to the other groups, the DS-LP group showed between 5% and 11% greater COMI score reduction (p < 0.01 up to 2 years' FU). This group also performed best with respect to pain outcomes and satisfaction. Long-term GTO was 93% at the 5 year FU, compared with between 82% and 86% in the other groups.

CONCLUSION

Regardless of the type of spondylolisthesis, all groups experienced an improvement in COMI score after surgery. Patients with DS and LP as their chief complaint appear to benefit more than other patients. These results are the first to show that the type of the spondylolisthesis and its chief complaint have an impact on surgical outcome. They will be informative for the consent process prior to surgery and can be used to build predictive models for individual outcome.

Artificial Intelligence for Clinically Meaningful Outcome Prediction in Orthopedic Research: Current Applications and Limitations

PURPOSE OF REVIEW

Patient-reported outcome measures (PROM) play a critical role in evaluating the success of treatment interventions for musculoskeletal conditions. However, predicting which patients will benefit from treatment interventions is complex and influenced by a multitude of factors. Artificial intelligence (AI) may better anticipate the propensity to achieve clinically meaningful outcomes through leveraging complex predictive analytics that allow for personalized medicine. This article provides a contemporary review of current applications of AI developed to predict clinically significant outcome (CSO) achievement after musculoskeletal treatment interventions.

RECENT FINDINGS

The highest volume of literature exists in the subspecialties of total joint arthroplasty, spine, and sports medicine, with only three studies identified in the remaining orthopedic subspecialties combined. Performance is widely variable across models, with most studies only reporting discrimination as a performance metric. Given the complexity inherent in predictive modeling for this task, including data availability, data handling, model architecture, and outcome selection, studies vary widely in their methodology and results. Importantly, the majority of studies have not been externally validated or demonstrate important methodological limitations, precluding their implementation into clinical settings. A substantial body of literature has accumulated demonstrating variable internal validity, limited scope, and low potential for clinical deployment. The majority of studies attempt to predict the MCID-the lowest bar of clinical achievement. Though a small proportion of models demonstrate promise and highlight the utility of AI, important methodological limitations need to be addressed moving forward to leverage AI-based applications for clinical deployment.

The influence of peri-operative depressive symptoms on medium-term spine surgery outcome: a prospective study

PURPOSE

To investigate the role of depressive symptoms on clinical outcomes in patients undergoing spinal surgery up to 2-year follow-up.

METHODS

The study used data from an institutional spine surgery registry (January 2016, through March 2022) to identify patients (> 18 years) undergoing spine surgery. Patients with Oswestry Disability Index (ODI) < 20/100 at baseline or undergoing surgery on the cervical spine or for idiopathic spinal deformity and trauma patients were excluded. The patients were divided into two groups based on the pre-operative Mental Component Summary (MCS) score of the SF-36: depression group (MCS ≤ 35) or non-depression group (MCS > 35). The ODI and MCS scores trajectory were wined over the 24-month post-surgery between groups. Additionally, a secondary subgroup analysis was conducted comparing outcomes between those with depressive symptoms (persistent-depression subgroup) and those without depressive symptoms (never-depression subgroup) at 3 months after surgery.

RESULTS

A total of 2164 patients who underwent spine surgery were included. The pre-operative depression group reported higher ODI total scores and lower MCS than the pre-operative non-depression group at all time points (P < 0.001). The persistent-depression subgroup reported higher ODI total scores and lower MCS than the never-depression subgroup at all follow-ups (P < 0.001).

CONCLUSION

Functional disability and mental health status improve in patients with depression symptoms undergoing spinal surgery. Despite this improvement, they do not reach the values of non-depressed subjects. Over the 2-year follow-up time, patients with depression show a different trajectory of ODI and MCS. Caregivers should be aware of these results to counsel patients with depression symptoms.

MAS-Net:Multi-modal Assistant Segmentation Network For Lumbar Intervertebral Disc

Objective.Despite advancements in medical imaging technology, the diagnosis and positioning of lumbar disc diseases still heavily rely on the expertise and experience of medical professionals. This process is often time-consuming, labor-intensive, and susceptible to subjective factors. Achieving automatic positioning and segmentation of lumbar intervertebral disc (LID) is the first and critical step in intelligent diagnosis of lumbar disc diseases. However, due to the complexity of the vertebral body and the ambiguity of the soft tissue boundaries of the LID, accurate and intelligent segmentation of LIDs remains challenging. The study aims to accurately and intelligently segment and locate LIDs by fully utilizing multi-modal lumbar magnetic resonance Images (MRIs).Approach.A novel multi-modal assistant segmentation network (MAS-Net) is proposed in this paper. The architecture consists of four key components: the multi-branch fusion encoder (MBFE), the cross-modality correlation evaluation (CMCE), the channel fusion transformer (CFT), and the selective Kernel (SK) based decoder. The MBFE module captures and integrates various modal features, while the CMCE module facilitates the fusion process between the MBFE and decoder. The CFT module selectively guides the flow of information between the MBFE and decoder and effectively utilizes skip connections from multiple layers. The SK module computes the significance of each channel using global pooling operations and applies weights to the input feature maps to improve the models recognition of important features.Main results.The proposed MAS-Net achieved a dice coefficient of 93.08% on IVD3Seg and 93.22% on DualModalDisc dataset, outperforming the current state-of-the-art network, accurately segmenting the LIDs, and generating a 3D model that can precisely display the LIDs.Significance.MAS-Net automates the diagnostics process and addresses challenges faced by doctors. Simplifying and enhancing the clarity of visual representation, multi-modal MRI allows for better information complementation and LIDs segmentation. By successfully integrating data from various modalities, the accuracy of LID segmentation is improved.

Quality Control for Spine Registries: Development and Application of a New Protocol

Registries are gaining importance both in clinical practice and for research purposes. However, quality control is paramount to ensure that data are consistent and reliable. Quality control protocols have been proposed for arthroplasty registries, but these are not directly applicable to the spine setting. This study aims to develop a new quality control protocol for spine registries. Based on the available protocols for arthroplasty registries, a new protocol for spine registries was developed. The items included in the protocol were completeness (yearly enrollment rate and rate of assessment completion), consistency, and internal validity (coherence between registry data and medical records for blood loss, body mass index, and treated levels). All aspects were then applied to the spine registry of the Institution to verify its quality for each of the 5 years in which the registry has been used (2016-2020). Regarding completeness, the yearly enrollment rate ranged from 78 to 86%; the completion of preoperative assessment from 79% to 100%. The yearly consistency rate varied from 83% to 86%. Considering internal validity, the interclass correlation coefficient ranged from 0.1 to 0.8 for blood loss and from 0.3 to 0.9 for body mass index. The coherency for treated levels ranged from 25% to 82%. Overall, all 3 items showed an improvement over time. All 3 analyzed domains showed good to excellent results. The overall quality of the registered data improved over time.

Development of a machine-learning based model for predicting multidimensional outcome after surgery for degenerative disorders of the spine

BACKGROUND

It is clear that individual outcomes of spine surgery can be quite heterogeneous. When consenting a patient for surgery, it is important to be able to offer an individualized prediction regarding the likely outcome. This study used a comprehensive set of data collected over 12 years in an in-house registry to develop a parsimonious model to predict the multidimensional outcome of patients undergoing surgery for degenerative pathologies of the thoracic, lumbar or cervical spine.

METHODS

Data from 8374 patients (mean age 63.9 (14.9-96.3) y, 53.4% female) were used to develop a model to predict the 12-month scores for the Core Outcome Measures Index (COMI) and its subdomain scores. The data were split 80:20 into a training and test set. The top predictors were selected by applying recursive feature elimination based on LASSO cross validation models. Based on the 111 top predictors (contained within 20 variables), Ridge cross validation models were trained, validated, and tested for each of 9 outcome domains, for patients with either "Back" (thoracic/lumbar spine) or "Neck" (cervical spine) problems (total 18 models).

RESULTS

Among the strongest outcome predictors in most models were: preoperative scores for almost all COMI items (especially axial pain (back or neck) and peripheral pain (leg/buttock or arm/shoulder)), catastrophizing, fear avoidance beliefs, comorbidity, age, BMI, nationality, previous spine surgery, type and spinal level of intervention, number of affected levels, and surgeon seniority. The R² of the models on the validation/test sets averaged 0.16/0.13. A preliminary online tool was programmed to present the predicted outcomes for individual patients, based on their presenting characteristics. https://linkup.kws.ch/prognostictool .

CONCLUSION

The models provided estimates to enable a bespoke prediction of the outcome of surgery for individual patients with varying degenerative pathologies and baseline characteristics. The models form the basis of a simple, freely-available online prognostic tool developed to improve access to and usability of prognostic information in clinical practice. It is hoped that, following confirmation of its validity and practical utility, the tool will ultimately serve to facilitate decision-making and the management of patients' expectations.

An Evolution Gaining Momentum—The Growing Role of Artificial Intelligence in the Diagnosis and Treatment of Spinal Diseases

In recent years, applications using artificial intelligence have been gaining importance in the diagnosis and treatment of spinal diseases. In our review, we describe the basic features of artificial intelligence which are currently applied in the field of spine diagnosis and treatment, and we provide an orientation of the recent technical developments and their applications. Furthermore, we point out the possible limitations and challenges in dealing with such technological advances. Despite the momentary limitations in practical application, artificial intelligence is gaining ground in the field of spine treatment. As an applying physician, it is therefore necessary to engage with it in order to benefit from those advances in the interest of the patient and to prevent these applications being misused by non-medical partners.

Artificial Intelligence-Driven Prediction Modeling and Decision Making in Spine Surgery Using Hybrid Machine Learning Models

Healthcare systems worldwide generate vast amounts of data from many different sources. Although of high complexity for a human being, it is essential to determine the patterns and minor variations in the genomic, radiological, laboratory, or clinical data that reliably differentiate phenotypes or allow high predictive accuracy in health-related tasks. Convolutional neural networks (CNN) are increasingly applied to image data for various tasks. Its use for non-imaging data becomes feasible through different modern machine learning techniques, converting non-imaging data into images before inputting them into the CNN model. Considering also that healthcare providers do not solely use one data modality for their decisions, this approach opens the door for multi-input/mixed data models which use a combination of patient information, such as genomic, radiological, and clinical data, to train a hybrid deep learning model. Thus, this reflects the main characteristic of artificial intelligence: simulating natural human behavior. The present review focuses on key advances in machine and deep learning, allowing for multi-perspective pattern recognition across the entire information set of patients in spine surgery. This is the first review of artificial intelligence focusing on hybrid models for deep learning applications in spine surgery, to the best of our knowledge. This is especially interesting as future tools are unlikely to use solely one data modality. The techniques discussed could become important in establishing a new approach to decision-making in spine surgery based on three fundamental pillars: (1) patient-specific, (2) artificial intelligence-driven, (3) integrating multimodal data. The findings reveal promising research that already took place to develop multi-input mixed-data hybrid decision-supporting models. Their implementation in spine surgery may hence be only a matter of time.