Prediction of Worse Functional Status After Surgery for Degenerative Cervical Myelopathy: A Machine Learning Approach

Artificial neural network analysis of factors affecting functional independence recovery in patients with lumbar stenosis after neurosurgery treatment: An observational cohort study

Background and aim

Lumbar spinal stenosis (LSS) is a leading cause of low back pain and lower limbs pain often associated with functional impairment which entails the loss or the impairment of independence in older adults. Conservative treatment is effective in a small percentage of patients, while a significant percentage undergo surgery, even if often without a complete resolution of clinical symptoms and motor deficits. The aim of the study is to identify clinical and demographic prognostic factors characterising the patients who would benefit most from surgical treatment in relation to the functional independence recovery using an innovative approach based on an artificial neural network.

Methods

Adult patients with LSS and indication of neurosurgical treatment were enrolled in the study. Clinical evaluation was performed in the preoperative-phase (into the 48 h before surgery) and after two months. Clinical battery investigated the motor, functional, cognitive, behavioural, and pain status. Demographics and clinical characteristics were analysed via Artificial Neural Network (ANN) using 24 input variables, 2 hidden layers and a single final output layer to predict the outcome. ANN results were compared with those of a multiple linear regression.

Results

108 patients were included in the study and 90 of them [66.5 ± 12.8 years; 27.8 % F] were submitted to surgery treatment and completed longitudinal evaluation. Statistically significant improvement was recorded in all clinical scales comparing pre- and post-surgery. The ANN results showed a prediction ability up to 81 %. Disability, functional limitations, and pain concerning clinical assessment and stature, onset and age about demographic characteristics are the main variables impacting on surgical outcome.

Conclusions

ANN can support clinical decision making, using clinical and demographic characteristics of patients with LSS identifying the characteristics of those who might benefit more from the surgical treatment in terms of global functional recovery.

Long-Term Outcomes of One-Stage Combined Posteroanterior Procedure and Laminoplasty-Alone for Multilevel Degenerative Cervical Myelopathy With Concomitant Anterior and Posterior Compression: A Propensity Score Matching Analysis of 8-year Follow-Up

STUDY DESIGN

Retrospective cohort study.

OBJECTIVE

This study aimed to investigate the long-term outcomes of laminoplasty-alone (LP) and combined procedure (CP), consisting of laminoplasty and single-level anterior cervical discectomy and fusion, in comparable patients who had multilevel degenerative cervical myelopathy (MDCM) with concomitant anterior and posterior compression (CAPC).

METHODS

Consecutive MDCM patients with CAPC underwent LP or CP between 2012 and 2015 from a same surgical group were enrolled and followed up for a minimum of 8 years. Preoperative demographic, radiological, and clinical variables were collected. Propensity score matching (PSM) analysis was performed to match patients with comparable conditions. The outcomes were evaluated by postoperative Japanese Orthopedic Association (JOA) score improvement, JOA recovery rate (JOARR) and complications.

RESULTS

A total of 230 patients were included, of whom 146 underwent LP and 84 underwent CP. After PSM, 84 pairs of comparable patients were matched. The matched groups presented fair comparability in preoperative conditions. The CP group had significantly prolonged surgery time and greater blood loss. At the final follow-up, the postoperative JOA scores of LP and CP groups were 14.51 ± 1.79 and 15.47 ± 1.81 (P < 0.001) and the JOARR were 42.5% ± 53.3% and 68.5% ± 35.4%, respectively (P < 0.001). Three (3.6%) patients in the LP group underwent reoperations because of recurrent symptoms (P = 0.081).

CONCLUSION

Both LP and CP demonstrated considerable long-term neurological recovery in patients with CAPC. The CP showed a significantly higher JOA improvement and JOARR. The combined decompression might be a safe and effective alternative in treating MDCM with CAPC in experienced hands.

Cervical Sagittal Alignment and Related Factor Analysis and Prediction Model in Patients Undergoing Revision Surgery After Anterior Cervical Fusion

INTRODUCTION

Patients with myelopathy or radiculopathy commonly undergo anterior cervical fusion surgery (ACFS), which has a notable failure rate on occasion. The goal of this study was to compare revision and nonrevision surgery patients in cervical sagittal alignment (CSA) subsequent to ACFS; additionally, to identify the best CSA parameters for predicting clinical outcome after ACFS; and furthermore, to create an equation model to assist surgeons in making decisions on patients undergoing ACFS.

METHODS

The data of 99 patients with symptomatic cervical myelopathy/radiculopathy who underwent ACFS were analyzed. Patients were divided into group A (underwent revision surgery after the first surgery failed) and group B (underwent only the first surgery). We measured and analyzed both preoperative and postoperative CSA parameters, including C2 slope, T1 slope, cervical lordosis C2-C7 (CL), C2-C7 sagittal vertical axis (C2C7 SVA), occiput-C2 lordosis angle (C0-C2), and chin brow vertical angle, and we further computed the correlation between the CSA parameters and created a prediction model.

RESULTS

The (T1S-CL)-C2S mismatch differed significantly between groups A and B ([9.95 ± 9.95] 0 , [3.79 ± 6.58] 0 , P < 0.05, respectively). A significant correlation was observed between C2 slope and T1CL in group B relative to group A postoperatively (R 2 = 0.42 versus R 2 = 0.09, respectively). Compared with group B, patients in group A had significantly higher C2C7SVA values, more levels of fusion, and more smokers. The sensitivity, specificity, accuracy, and discrimination of the model were, respectively, 73.5%, 84%, 78.8%, and 85.65%.

CONCLUSION

The causes of revision surgery in cervical myelopathic patients after anterior cervical corpectomy and fusion/anterior cervical diskectomy and fusion are multifactorial. (T1S-CL)-C2S mismatch and high C2C7SVA are the best cervical sagittal parameters that increase the odds of revision surgery, and the effect is more enhanced when comorbidities such as smoking, low bone-mineral density, and increased levels of fusion are taken into account.

Developing Machine Learning-Based Predictive Models for Hallux Valgus Recurrence Based on Measurements From Radiographs

BACKGROUND

Machine learning (ML) is increasingly used to predict the prognosis of numerous diseases. This retrospective analysis aimed to develop a prediction model using ML algorithms and to identify predictors associated with the recurrence of hallux valgus (HV) following surgery.

METHODS

A total of 198 symptomatic feet that underwent chevron osteotomy combined with a distal soft tissue procedure were enrolled and analyzed from 2 independent medical centers. The feet were grouped according to nonrecurrence or recurrence based on 1-year follow-up outcomes. Preoperative weightbearing radiographs and immediate postoperative nonweightbearing radiographs were obtained for each HV foot. Radiographic measurements (eg, HV angle and intermetatarsal angle) were acquired and used for ML model training. A total of 9 commonly used ML models were trained on the data obtained from one institute (108 feet), and tested on the other data set from another independent institute (90 feet) for external validation. Optimal feature sets for each model were identified based on a 2000-resample bootstrap-based internal validation via an exhaustive search. The performance of each model was then tested on the external validation set. The area under the curve (AUC), classification accuracy, sensitivity, and specificity of each model were calculated to evaluate the performance of each model.

RESULTS

The support vector machine (SVM) model showed the highest predictive accuracy compared to other methods, with an AUC of 0.88 and an accuracy of 75.6%. Preoperative hallux valgus angle, tibial sesamoid position, postoperative intermetatarsal angle, and postoperative tibial sesamoid position were identified as the most selected features by several ML models.

CONCLUSION

ML classifiers such as SVM could predict the recurrence of HV (an HVA >20 degrees) at a 1-year follow-up while identifying associated predictors in a multivariate manner. This study holds the potential for foot and ankle surgeons to effectively identify individuals at higher risk of HV recurrence postsurgery.

Artificial intelligence: a new cutting-edge tool in spine surgery

The purpose of this narrative review was to comprehensively elaborate the various components of artificial intelligence (AI), their applications in spine surgery, practical concerns, and future directions. Over the years, spine surgery has been continuously transformed in various aspects, including diagnostic strategies, surgical approaches, procedures, and instrumentation, to provide better-quality patient care. Surgeons have also augmented their surgical expertise with rapidly growing technological advancements. AI is an advancing field that has the potential to revolutionize many aspects of spine surgery. We performed a comprehensive narrative review of the various aspects of AI and machine learning in spine surgery. To elaborate on the current role of AI in spine surgery, a review of the literature was performed using PubMed and Google Scholar databases for articles published in English in the last 20 years. The initial search using the keywords "artificial intelligence" AND "spine," "machine learning" AND "spine," and "deep learning" AND "spine" extracted a total of 78, 60, and 37 articles and 11,500, 4,610, and 2,270 articles on PubMed and Google Scholar. After the initial screening and exclusion of unrelated articles, duplicates, and non-English articles, 405 articles were identified. After the second stage of screening, 93 articles were included in the review. Studies have shown that AI can be used to analyze patient data and provide personalized treatment recommendations in spine care. It also provides valuable insights for planning surgeries and assisting with precise surgical maneuvers and decisionmaking during the procedures. As more data become available and with further advancements, AI is likely to improve patient outcomes.

Utility of somatosensory- and motor-evoked potential change thresholds in surgical treatment for thoracic spinal stenosis based on different pathologies

BACKGROUND CONTEXT

Thoracic spinal stenosis (TSS) is secondary to different pathologies that differ in clinical characteristics and surgical outcomes.

PURPOSE

This study aimed to determine the optimal warning thresholds for combined somatosensory-evoked potentials (SSEP) and motor-evoked potentials (MEP) for predicting postoperative neurological deterioration in surgical treatment for TSS based on different pathologies. Additionally, we explored the correlation between SSEP/MEP monitoring and postoperative spinal neurological function.

STUDY SETTING

Retrospective study.

PATIENT SAMPLE

Two hundred and five patients.

OUTCOME MEASURES

We obtained perioperative modified Japanese Orthopedic Association (mJOA) scores to assess spinal neurological function.

METHODS

The data collected in this study included demographic data, intraoperative neurophysiological monitoring (IONM) signals, and perioperative neurological function assessments. To determine the optimal IONM warning threshold, a receiver operating characteristic (ROC) curve was used. Additionally, Pearson correlation analysis was conducted to determine the correlation between IONM signals and clinical neurological conditions.

RESULTS

A total of 205 consecutive patients were eligible. Forty-one patients had thoracic disc herniation (TDH), 14 had ossification of the posterior longitudinal ligament (OPLL), 124 had ossification of the ligamentum flavum (OLF), and 26 had OPLL+OLF. The mean mJOA scores before surgery and 3 months after surgery were 7.0 and 7.9, respectively, resulting in a mean mJOA recovery rate (RR) of 23.1%. The average postoperative mJOA RRs for patients with TDH, OPLL, OLF, and OPLL+OLF were 24.8%, 10.4%, 26.8%, and 11.2%, respectively. Patients with OPLL+OLF exhibited a more stringent threshold for IONM changes. This included a lower amplitude cutoff value (a decrease of 49.0% in the SSEP amplitude and 57.5% in the MEP amplitude for short-term prediction) and a shorter duration of waveform change (19.5 minutes for SSEP and 22.5 minutes for MEP for short-term prediction). On the other hand, patients with TDH had more lenient IONM warning criteria (a decrease of 49.0% in SSEP amplitude and 77.5% in MEP amplitude for short-term prediction; durations of change of 25.5 minutes for SSEP and 32.5 minutes for MEP). However, OPLL patients or OLF patients had moderate and similar IONM warning thresholds. Additionally, there was a stronger correlation between the SSEP amplitude variability ratio and the JOA RR in OPLL+OLF patients, while the correlation was stronger between the MEP amplitude variability ratio and the JOA RR for the other three TSS pathologies.

CONCLUSIONS

Optimal IONM change criteria for prediction vary depending on different TSS pathologies. The optimal monitoring strategy for prediction varies depending on TSS pathologies.

Enabling Personalized Medicine in Orthopaedic Surgery Through Artificial Intelligence: A Critical Analysis Review

» The application of artificial intelligence (AI) in the field of orthopaedic surgery holds potential for revolutionizing health care delivery across 3 crucial domains: (I) personalized prediction of clinical outcomes and adverse events, which may optimize patient selection, surgical planning, and enhance patient safety and outcomes; (II) diagnostic automated and semiautomated imaging analyses, which may reduce time burden and facilitate precise and timely diagnoses; and (III) forecasting of resource utilization, which may reduce health care costs and increase value for patients and institutions.» Computer vision is one of the most highly studied areas of AI within orthopaedics, with applications pertaining to fracture classification, identification of the manufacturer and model of prosthetic implants, and surveillance of prosthesis loosening and failure.» Prognostic applications of AI within orthopaedics include identifying patients who will likely benefit from a specified treatment, predicting prosthetic implant size, postoperative length of stay, discharge disposition, and surgical complications. Not only may these applications be beneficial to patients but also to institutions and payors because they may inform potential cost expenditure, improve overall hospital efficiency, and help anticipate resource utilization.» AI infrastructure development requires institutional financial commitment and a team of clinicians and data scientists with expertise in AI that can complement skill sets and knowledge. Once a team is established and a goal is determined, teams (1) obtain, curate, and label data; (2) establish a reference standard; (3) develop an AI model; (4) evaluate the performance of the AI model; (5) externally validate the model, and (6) reinforce, improve, and evaluate the model's performance until clinical implementation is possible.» Understanding the implications of AI in orthopaedics may eventually lead to wide-ranging improvements in patient care. However, AI, while holding tremendous promise, is not without methodological and ethical limitations that are essential to address. First, it is important to ensure external validity of programs before their use in a clinical setting. Investigators should maintain high quality data records and registry surveillance, exercise caution when evaluating others' reported AI applications, and increase transparency of the methodological conduct of current models to improve external validity and avoid propagating bias. By addressing these challenges and responsibly embracing the potential of AI, the medical field may eventually be able to harness its power to improve patient care and outcomes.

Early surgery compared to nonoperative management for mild degenerative cervical myelopathy: a cost-utility analysis

BACKGROUND CONTEXT

Degenerative cervical myelopathy (DCM) is a form of acquired spinal cord compression and contributes to reduced quality of life secondary to neurological dysfunction and pain. There remains uncertainty regarding optimal management for individuals with mild myelopathy. Specifically, owing to lacking long-term natural history studies in this population, we do not know whether these individuals should be treated with initial surgery or observation.

PURPOSE

We sought to perform a cost-utility analysis to examine early surgery for mild degenerative cervical myelopathy from the healthcare payer perspective.

STUDY DESIGN/SETTING

We utilized data from the prospective observational cohorts included in the Cervical Spondylotic Myelopathy AO Spine International and North America studies to determine health related quality of life estimates and clinical myelopathy outcomes.

PATIENT SAMPLE

We recruited all patients that underwent surgery for DCM enrolled in the Cervical Spondylotic Myelopathy AO Spine International and North America studies between December 2005 and January 2011.

OUTCOME MEASURES

Clinical assessment measures were obtained using the Modified Japanese Orthopedic Association scale and health-related quality of life measures were obtained using the Short Form-6D utility score at baseline (preoperative), 6 months, 12 months and 24 months postsurgery. Cost measures inflated to January 2015 values were obtained using pooled estimates from the hospital payer perspective for surgical patients.

METHODS

We employed a Markov state transition model with Monte Carlo microsimulation using a lifetime horizon to obtain an incremental cost utility ratio associated with early surgery for mild myelopathy. Parameter uncertainty was assessed through deterministic means using one-way and two-way sensitivity analyses and probabilistically using parameter estimate distributions with microsimulation (10,000 trials). Costs and utilities were discounted at 3% per annum.

RESULTS

Initial surgery for mild degenerative cervical myelopathy was associated with an incremental lifetime increase of 1.26 quality-adjusted life years (QALY) compared to observation. The associated cost incurred to the healthcare payer over a lifetime horizon was $12,894.56, resulting in a lifetime incremental cost-utility ratio of $10,250.71/QALY. Utilizing a willingness to pay threshold in keeping with the World Health Organization definition of "very cost-effective" ($54,000 CDN), the probabilistic sensitivity analysis demonstrated that 100% of cases were cost-effective.

CONCLUSIONS

Surgery compared to initial observation for mild degenerative cervical myelopathy was cost-effective from the Canadian healthcare payer perspective and was associated with lifetime gains in health-related quality of life.

Developing predictive models for surgical outcomes in patients with degenerative cervical myelopathy: a comparison of statistical and machine learning approaches

BACKGROUND CONTEXT

Machine learning (ML) is widely used to predict the prognosis of numerous diseases.

PURPOSE

This retrospective analysis aimed to develop a prognostic prediction model using ML algorithms and identify predictors associated with poor surgical outcomes in patients with degenerative cervical myelopathy (DCM).

STUDY DESIGN

A retrospective study.

PATIENT SAMPLE

A total of 406 symptomatic DCM patients who underwent surgical decompression were enrolled and analyzed from three independent medical centers.

OUTCOME MEASURES

We calculated the area under the curve (AUC), classification accuracy, sensitivity, and specificity of each model.

METHODS

The Japanese Orthopedic Association (JOA) score was obtained before and 1 year following decompression surgery, and patients were grouped into good and poor outcome groups based on a cut-off value of 60% based on a previous study. Two datasets were fused for training, 1 dataset was held out as an external validation set. Optimal feature-subset and hyperparameters for each model were adjusted based on a 2,000-resample bootstrap-based internal validation via exhaustive search and grid search. The performance of each model was then tested on the external validation set.

RESULTS

The Support Vector Machine (SVM) model showed the highest predictive accuracy compared to other methods, with an AUC of 0.82 and an accuracy of 75.7%. Age, sex, disease duration, and preoperative JOA score were identified as the most commonly selected features by both the ML and statistical models. Grid search optimization for hyperparameters successfully enhanced the predictive performance of each ML model, and the SVM model still had the best performance with an AUC of 0.93 and an accuracy of 86.4%.

CONCLUSIONS

Overall, the study demonstrated that ML classifiers such as SVM can effectively predict surgical outcomes for patients with DCM while identifying associated predictors in a multivariate manner.

Limitations in Evaluating Machine Learning Models for Imbalanced Binary Outcome Classification in Spine Surgery: A Systematic Review

Clinical prediction models for spine surgery applications are on the rise, with an increasing reliance on machine learning (ML) and deep learning (DL). Many of the predicted outcomes are uncommon; therefore, to ensure the models' effectiveness in clinical practice it is crucial to properly evaluate them. This systematic review aims to identify and evaluate current research-based ML and DL models applied for spine surgery, specifically those predicting binary outcomes with a focus on their evaluation metrics. Overall, 60 papers were included, and the findings were reported according to the PRISMA guidelines. A total of 13 papers focused on lengths of stay (LOS), 12 on readmissions, 12 on non-home discharge, 6 on mortality, and 5 on reoperations. The target outcomes exhibited data imbalances ranging from 0.44% to 42.4%. A total of 59 papers reported the model's area under the receiver operating characteristic (AUROC), 28 mentioned accuracies, 33 provided sensitivity, 29 discussed specificity, 28 addressed positive predictive value (PPV), 24 included the negative predictive value (NPV), 25 indicated the Brier score with 10 providing a null model Brier, and 8 detailed the F1 score. Additionally, data visualization varied among the included papers. This review discusses the use of appropriate evaluation schemes in ML and identifies several common errors and potential bias sources in the literature. Embracing these recommendations as the field advances may facilitate the integration of reliable and effective ML models in clinical settings.

Personalized Medicine in Orthopaedic Surgery: The Case of Spine Surgery

Personalized medicine has made a tremendous impact on patient care. Although initially, it revolutionized pharmaceutical development and targeted therapies in oncology, it has also made an important impact in orthopaedic surgery. The field of spine surgery highlights the effect of personalized medicine because the improved understanding of spinal pathologies and technological innovations has made personalized medicine a key component of patient care. There is evidence for several of these advancements to support their usage in improving patient care. Proper understanding of normative spinal alignment and surgical planning software has enabled surgeons to predict postoperative alignment accurately. Furthermore, 3D printing technologies have demonstrated the ability to improve pedicle screw placement accuracy compared with free-hand techniques. Patient-specific, precontoured rods have shown improved biomechanical properties, which reduces the risk of postoperative rod fractures. Moreover, approaches such as multidisciplinary evaluations tailored to specific patient needs have demonstrated the ability to decrease complications. Personalized medicine has shown the ability to improve care in all phases of surgical management, and several of these approaches are now readily available to orthopaedic surgeons.

Diffusion basis spectrum imaging predicts long-term clinical outcomes following surgery in cervical spondylotic myelopathy

BACKGROUND CONTEXT

A major shortcoming in improving care for cervical spondylotic myelopathy (CSM) patients is the lack of robust quantitative imaging tools to guide surgical decision-making. Diffusion basis spectrum imaging (DBSI), an advanced diffusion-weighted MRI technique, provides objective assessments of white matter tract integrity that may help prognosticate outcomes in patients undergoing surgery for CSM.

PURPOSE

To examine the ability of DBSI to predict clinically important CSM outcome measures at 2-years follow-up.

STUDY DESIGN/SETTING

Prospective cohort study.

PATIENT SAMPLE

Patients undergoing decompressive cervical surgery for CSM.

OUTCOME MEASURES

Neurofunctional status was assessed by the mJOA, MDI, and DASH. Quality-of-life was measured by the SF-36 PCS and SF-36 MCS. The NDI evaluated self-reported neck pain, and patient satisfaction was assessed by the NASS satisfaction index.

METHODS

Fifty CSM patients who underwent cervical decompressive surgery were enrolled. Preoperative DBSI metrics assessed white matter tract integrity through fractional anisotropy, fiber fraction, axial diffusivity, and radial diffusivity. To evaluate extra-axonal diffusion, DBSI measures restricted and nonrestricted fractions. Patient-reported outcome measures were evaluated preoperatively and up to 2-years follow-up. Support vector machine classification algorithms were used to predict surgical outcomes at 2-years follow-up. Specifically, three feature sets were built for each of the seven clinical outcome measures (eg, mJOA), including clinical only, DBSI only, and combined feature sets.

RESULTS

Twenty-seven mild (mJOA 15-17), 12 moderate (12-14) and 11 severe (0-11) CSM patients were enrolled. Twenty-four (60%) patients underwent anterior decompressive surgery compared with 16 (40%) posterior approaches. The mean (SD) follow-up was 23.2 (5.6, range 6.1-32.8) months. Feature sets built on combined data (ie, clinical+DBSI metrics) performed significantly better for all outcome measures compared with those only including clinical or DBSI data. When predicting improvement in the mJOA, the clinically driven feature set had an accuracy of 61.9 [61.6, 62.5], compared with 78.6 [78.4, 79.2] in the DBSI feature set, and 90.5 [90.2, 90.8] in the combined feature set.

CONCLUSIONS

When combined with key clinical covariates, preoperative DBSI metrics predicted improvement after surgical decompression for CSM with high accuracy for multiple outcome measures. These results suggest that DBSI may serve as a noninvasive imaging biomarker for CSM valuable in guiding patient selection and informing preoperative counseling.

LEVEL OF EVIDENCE

II.

Use of random forest machine learning algorithm to predict short term outcomes following posterior cervical decompression with instrumented fusion

Random Forest (RF) is a widely used machine learning algorithm that can be utilized for identification of patient characteristics important for outcome prediction. Posterior cervical decompression with instrumented fusion (PCDF) is a procedure for the management of cervical spondylosis, cervical spinal stenosis, and degenerative disorders that can cause cervical myelopathy or radiculopathy. An RF algorithm was employed to predict and describe length of stay (LOS), readmission, reoperation, transfusion, and infection rates following elective PCDF using The American College of Surgeons National Quality Improvement Program (ACS-NSQIP) database 2008 through 2018. The RF algorithm was tasked with determining the importance of independent clinical variables in predicting our outcomes of interest and importance of each variable based on the reduction in the Gini index. Application of an RF algorithm to the ACS-NSQIP database yielded a highly predictive set of patient characteristics and perioperative events for five outcomes of interest related to elective PCDF. These variables included postoperative infection, increased age, BMI, operative time, and LOS, and decreased preoperative hematocrit and white blood cell count. Risk factors that were predictive for rate of reoperation, readmission, hospital length of stay, transfusion requirement, and post-operative infection were identified with AUC values of 0.781, 0.791, 0.781, 0.902, and 0.724 respectively. Utilization of these findings may assist in risk analysis during the perioperative period and may influence clinical or surgical decision-making.

Influence of clinical and radiological parameters on the likelihood of neurological improvement after surgery for degenerative cervical myelopathy

OBJECTIVE

Degenerative cervical myelopathy (DCM) is routinely treated with surgical decompression, but disparate postoperative outcomes are frequently observed, ranging from complete neurological recovery to persistent decline. Although numerous clinical and radiological factors have been independently associated with failure to improve, the relative impact of these proposed risk factors remains obscure. In this study, the authors assess the combined role of clinical and radiographic parameters in contributing to failure to attain neurological improvement after surgery.

METHODS

A consecutive series of patients who underwent surgery for DCM between July 2013 and August 2018 at a single institution was identified from a prospectively maintained database. Retrospective chart review was undertaken to record perioperative clinical and radiographic parameters. Failure to improve on the last follow-up evaluation after surgery, defined as a change in modified Japanese Orthopaedic Association (mJOA) score less than 2, was the primary outcome in univariate and multivariate analyses.

RESULTS

The authors included 183 patients in the final cohort. In total, 109 (59.6%) patients improved (i.e., responders with ΔmJOA score ≥ 2) after surgery and 74 (40.4%) were nonresponders with ΔmJOA score < 2. Baseline demographic variables and comorbidity rates were similar, whereas baseline Nurick score was the only clinical variable that differed between responders and nonresponders (2.7 vs 3.0, p = 0.02). In contrast, several preoperative radiographic variables differed between the groups, including presence and degree of cervical kyphosis, number of levels with bidirectional cord compression, presence and number of levels with T2-weighted signal change, intramedullary lesion (IML) length, Torg ratio, and both narrowest spinal canal and cord diameter. On multivariate analysis, preoperative degree of kyphosis at C2-7 (OR 1.19, p = 0.004), number of levels with bidirectional compression (OR 1.83, p = 0.003), and IML length (OR 1.14, p < 0.001) demonstrated the highest predictive power for nonresponse (area under the receiver operating characteristic curve 0.818). A risk factor point system that predicted failure of improvement was derived by incorporating these 3 variables.

CONCLUSIONS

When a large spectrum of both clinical and radiographic variables is considered, the degree of cervical kyphosis, number of levels with bidirectional compression, and IML length are the most predictive of nonresponse after surgery for DCM. Assessment of these radiographic factors can help guide surgical decision-making and more appropriately stratify patients in clinical trials.

Utility of Diffusion Basis Spectrum Imaging in Quantifying Baseline Disease Severity and Prognosis of Cervical Spondylotic Myelopathy

STUDY DESIGN

Prospective cohort study.

OBJECTIVE

The aim was to assess the association between diffusion tensor imaging (DTI) and diffusion basis spectrum imaging (DBSI) measures and cervical spondylotic myelopathy (CSM) clinical assessments at baseline and two-year follow-up.

SUMMARY OF BACKGROUND DATA

Despite advancements in diffusion-weighted imaging, few studies have examined associations between diffusion magnetic resonance imaging (MRI) markers and CSM-specific clinical domains at baseline and long-term follow-up.

MATERIALS AND METHODS

A single-center prospective cohort study enrolled 50 CSM patients who underwent surgical decompression and 20 controls from 2018 to 2020. At initial evaluation, all patients underwent diffusion-weighted MRI acquisition, followed by DTI and DBSI analyses. Diffusion-weighted MRI metrics assessed white matter integrity by fractional anisotropy, axial diffusivity, radial diffusivity, and fiber fraction. To improve estimations of intra-axonal anisotropic diffusion, DBSI measures intra-/extra-axonal fraction and intra-axonal axial diffusivity. DBSI also evaluates extra-axonal isotropic diffusion by restricted and nonrestricted fraction. Clinical assessments were performed at baseline and two-year follow-up and included the modified Japanese Orthopedic Association (mJOA); 36-Item Short Form Survey physical component summary (SF-36 PCS); SF-36 mental component summary; neck disability index; myelopathy disability index; and disability of the arm, shoulder, and hand. Pearson correlation coefficients were computed to compare associations between DTI/DBSI and clinical measures. A False Discovery Rate correction was applied for multiple comparisons testing.

RESULTS

At baseline presentation, of 36 correlations analyzed between DTI metrics and CSM clinical measures, only DTI fractional anisotropy showed a positive correlation with SF-36 PCS ( r =0.36, P =0.02). In comparison, there were 30/81 (37%) significant correlations among DBSI and clinical measures. Increased DBSI axial diffusivity, intra-axonal axial diffusivity, intra-axonal fraction, restricted fraction, and extra-axonal anisotropic fraction were associated with worse clinical presentation (decreased mJOA; SF-36 PCS/mental component summary; and increased neck disability index; myelopathy disability index; disability of the arm, shoulder, and hand). At latest follow-up, increased preoperative DBSI intra-axonal axial diffusivity and extra-axonal anisotropic fraction were significantly correlated with improved mJOA.

CONCLUSIONS

This findings demonstrate that DBSI measures may reflect baseline disease burden and long-term prognosis of CSM as compared with DTI. With further validation, DBSI may serve as a noninvasive biomarker following decompressive surgery.

LEVEL OF EVIDENCE

3.

Analysis of combined clinical and diffusion basis spectrum imaging metrics to predict the outcome of chronic cervical spondylotic myelopathy following cervical decompression surgery

OBJECTIVE

Cervical spondylotic myelopathy (CSM) is the most common cause of chronic spinal cord injury, a significant public health problem. Diffusion tensor imaging (DTI) is a neuroimaging technique widely used to assess CNS tissue pathology and is increasingly used in CSM. However, DTI lacks the needed accuracy, precision, and recall to image pathologies of spinal cord injury as the disease progresses. Thus, the authors used diffusion basis spectrum imaging (DBSI) to delineate white matter injury more accurately in the setting of spinal cord compression. It was hypothesized that the profiles of multiple DBSI metrics can serve as imaging outcome predictors to accurately predict a patient's response to therapy and his or her long-term prognosis. This hypothesis was tested by using DBSI metrics as input features in a support vector machine (SVM) algorithm.

METHODS

Fifty patients with CSM and 20 healthy controls were recruited to receive diffusion-weighted MRI examinations. All spinal cord white matter was identified as the region of interest (ROI). DBSI and DTI metrics were extracted from all voxels in the ROI and the median value of each patient was used in analyses. An SVM with optimized hyperparameters was trained using clinical and imaging metrics separately and collectively to predict patient outcomes. Patient outcomes were determined by calculating changes between pre- and postoperative modified Japanese Orthopaedic Association (mJOA) scale scores.

RESULTS

Accuracy, precision, recall, and F1 score were reported for each SVM iteration. The highest performance was observed when a combination of clinical and DBSI metrics was used to train an SVM. When assessing patient outcomes using mJOA scale scores, the SVM trained with clinical and DBSI metrics achieved accuracy and an area under the curve of 88.1% and 0.95, compared with 66.7% and 0.65, respectively, when clinical and DTI metrics were used together.

CONCLUSIONS

The accuracy and efficacy of the SVM incorporating clinical and DBSI metrics show promise for clinical applications in predicting patient outcomes. These results suggest that DBSI metrics, along with the clinical presentation, could serve as a surrogate in prognosticating outcomes of patients with CSM.

The application of artificial intelligence in spine surgery

Due to its obvious advantages in processing big data and image information, the combination of artificial intelligence and medical care may profoundly change medical practice and promote the gradual transition from traditional clinical care to precision medicine mode. In this artical, we reviewed the relevant literatures and found that artificial intelligence was widely used in spine surgery. The application scenarios included etiology, diagnosis, treatment, postoperative prognosis and decision support systems of spinal diseases. The shift to artificial intelligence model in medicine constantly improved the level of doctors' diagnosis and treatment and the development of orthopedics.

Artificial intelligence in spine care: current applications and future utility

PURPOSE

The field of artificial intelligence is ever growing and the applications of machine learning in spine care are continuously advancing. Given the advent of the intelligence-based spine care model, understanding the evolution of computation as it applies to diagnosis, treatment, and adverse event prediction is of great importance. Therefore, the current review sought to synthesize findings from the literature at the interface of artificial intelligence and spine research.

METHODS

A narrative review was performed based on the literature of three databases (MEDLINE, CINAHL, and Scopus) from January 2015 to March 2021 that examined historical and recent advancements in the understanding of artificial intelligence and machine learning in spine research. Studies were appraised for their role in, or description of, advancements within image recognition and predictive modeling for spinal research. Only English articles that fulfilled inclusion criteria were ultimately incorporated in this review.

RESULTS

This review briefly summarizes the history and applications of artificial intelligence and machine learning in spine. Three basic machine learning training paradigms: supervised learning, unsupervised learning, and reinforced learning are also discussed. Artificial intelligence and machine learning have been utilized in almost every facet of spine ranging from localization and segmentation techniques in spinal imaging to pathology specific algorithms which include but not limited to; preoperative risk assessment of postoperative complications, screening algorithms for patients at risk of osteoporosis and clustering analysis to identify subgroups within adolescent idiopathic scoliosis. The future of artificial intelligence and machine learning in spine surgery is also discussed with focusing on novel algorithms, data collection techniques and increased utilization of automated systems.

CONCLUSION

Improvements to modern-day computing and accessibility to various imaging modalities allow for innovative discoveries that may arise, for example, from management. Given the imminent future of AI in spine surgery, it is of great importance that practitioners continue to inform themselves regarding AI, its goals, use, and progression. In the future, it will be critical for the spine specialist to be able to discern the utility of novel AI research, particularly as it continues to pervade facets of everyday spine surgery.

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.

Spinal Cord Motion in Degenerative Cervical Myelopathy: The Level of the Stenotic Segment and Gender Cause Altered Pathodynamics

In degenerative cervical myelopathy (DCM), focally increased spinal cord motion has been observed for C5/C6, but whether stenoses at other cervical segments lead to similar pathodynamics and how severity of stenosis, age, and gender affect them is still unclear. We report a prospective matched-pair controlled trial on 65 DCM patients. A high-resolution 3D T2 sampling perfection with application-optimized contrasts using different flip angle evolution (SPACE) and a phase-contrast magnetic resonance imaging (MRI) sequence were performed and automatically segmented. Anatomical and spinal cord motion data were assessed per segment from C2/C3 to C7/T1. Spinal cord motion was focally increased at a level of stenosis among patients with stenosis at C4/C5 (n = 14), C5/C6 (n = 33), and C6/C7 (n = 10) (p < 0.033). Patients with stenosis at C2/C3 (n = 2) and C3/C4 (n = 6) presented a similar pattern, not reaching significance. Gender was a significant predictor of higher spinal cord dynamics among men with stenosis at C5/C6 (p = 0.048) and C6/C7 (p = 0.033). Age and severity of stenosis did not relate to spinal cord motion. Thus, the data demonstrates focally increased spinal cord motion depending on the specific level of stenosis. Gender-related effects lead to dynamic alterations among men with stenosis at C5/C6 and C6/C7. The missing relation of motion to severity of stenosis underlines a possible additive diagnostic value of spinal cord motion analysis in DCM.