Current Applications of Machine Learning in Spine: From Clinical View

A high-quality dataset featuring classified and annotated cervical spine X-ray atlas

Recent research in computational imaging largely focuses on developing machine learning (ML) techniques for image recognition in the medical field, which requires large-scale and high-quality training datasets consisting of raw images and annotated images. However, suitable experimental datasets for cervical spine X-ray are scarce. We fill the gap by providing an open-access Cervical Spine X-ray Atlas (CSXA), which includes 4963 raw PNG images and 4963 annotated images with JSON format (JavaScript Object Notation). Every image in the CSXA is enriched with gender, age, pixel equivalent, asymptomatic and symptomatic classifications, cervical curvature categorization and 118 quantitative parameters. Subsequently, an efficient algorithm has developed to transform 23 keypoints in images into 77 quantitative parameters for cervical spine disease diagnosis and treatment. The algorithm's development is intended to assist future researchers in repurposing annotated images for the advancement of machine learning techniques across various image recognition tasks. The CSXA and algorithm are open-access with the intention of aiding the research communities in experiment replication and advancing the field of medical imaging in cervical spine.

Reference Data for Diagnosis of Spondylolisthesis and Disc Space Narrowing Based on NHANES-II X-rays

Robust reference data, representing a large and diverse population, are needed to objectively classify measurements of spondylolisthesis and disc space narrowing as normal or abnormal. The reference data should be open access to drive standardization across technology developers. The large collection of radiographs from the 2nd National Health and Nutrition Examination Survey was used to establish reference data. A pipeline of neural networks and coded logic was used to place landmarks on the corners of all vertebrae, and these landmarks were used to calculate multiple disc space metrics. Descriptive statistics for nine SPO and disc metrics were tabulated and used to identify normal discs, and data for only the normal discs were used to arrive at reference data. A spondylolisthesis index was developed that accounts for important variables. These reference data facilitate simplified and standardized reporting of multiple intervertebral disc metrics.

Knowledge distillation on individual vertebrae segmentation exploiting 3D U-Net

Recent advances in medical imaging have highlighted the critical development of algorithms for individual vertebral segmentation on computed tomography (CT) scans. Essential for diagnostic accuracy and treatment planning in orthopaedics, neurosurgery and oncology, these algorithms face challenges in clinical implementation, including integration into healthcare systems. Consequently, our focus lies in exploring the application of knowledge distillation (KD) methods to train shallower networks capable of efficiently segmenting vertebrae in CT scans. This approach aims to reduce segmentation time, enhance suitability for emergency cases, and optimize computational and memory resource efficiency. Building upon prior research in the field, a two-step segmentation approach was employed. Firstly, the spine's location was determined by predicting a heatmap, indicating the probability of each voxel belonging to the spine. Subsequently, an iterative segmentation of vertebrae was performed from the top to the bottom of the CT volume over the located spine, using a memory instance to record the already segmented vertebrae. KD methods were implemented by training a teacher network with performance similar to that found in the literature, and this knowledge was distilled to a shallower network (student). Two KD methods were applied: (1) using the soft outputs of both networks and (2) matching logits. Two publicly available datasets, comprising 319 CT scans from 300 patients and a total of 611 cervical, 2387 thoracic, and 1507 lumbar vertebrae, were used. To ensure dataset balance and robustness, effective data augmentation methods were applied, including cleaning the memory instance to replicate the first vertebra segmentation. The teacher network achieved an average Dice similarity coefficient (DSC) of 88.22% and a Hausdorff distance (HD) of 7.71 mm, showcasing performance similar to other approaches in the literature. Through knowledge distillation from the teacher network, the student network's performance improved, with an average DSC increasing from 75.78% to 84.70% and an HD decreasing from 15.17 mm to 8.08 mm. Compared to other methods, our teacher network exhibited up to 99.09% fewer parameters, 90.02% faster inference time, 88.46% shorter total segmentation time, and 89.36% less associated carbon (CO2) emission rate. Regarding our student network, it featured 75.00% fewer parameters than our teacher, resulting in a 36.15% reduction in inference time, a 33.33% decrease in total segmentation time, and a 42.96% reduction in CO2 emissions. This study marks the first exploration of applying KD to the problem of individual vertebrae segmentation in CT, demonstrating the feasibility of achieving comparable performance to existing methods using smaller neural networks.

The value of machine learning technology and artificial intelligence to enhance patient safety in spine surgery: a review

Machine learning algorithms have the potential to significantly improve patient safety in spine surgeries by providing healthcare professionals with valuable insights and predictive analytics. These algorithms can analyze preoperative data, such as patient demographics, medical history, and imaging studies, to identify potential risk factors and predict postoperative complications. By leveraging machine learning, surgeons can make more informed decisions, personalize treatment plans, and optimize surgical techniques to minimize risks and enhance patient outcomes. Moreover, by harnessing the power of machine learning, healthcare providers can make data-driven decisions, personalize treatment plans, and optimize surgical interventions, ultimately enhancing the quality of care in spine surgery. The findings highlight the potential of integrating artificial intelligence in healthcare settings to mitigate risks and enhance patient safety in surgical practices. The integration of machine learning holds immense potential for enhancing patient safety in spine surgeries. By leveraging advanced algorithms and predictive analytics, healthcare providers can optimize surgical decision-making, mitigate risks, and personalize treatment strategies to improve outcomes and ensure the highest standard of care for patients undergoing spine procedures. As technology continues to evolve, the future of spine surgery lies in harnessing the power of machine learning to transform patient safety and revolutionize surgical practices. The present review article was designed to discuss the available literature in the field of machine learning techniques to enhance patient safety in spine surgery.

Clinical Decision Support System Used in Spinal Disorders: Scoping Review

BACKGROUND

Spinal disorders are highly prevalent worldwide with high socioeconomic costs. This cost is associated with the demand for treatment and productivity loss, prompting the exploration of technologies to improve patient outcomes. Clinical decision support systems (CDSSs) are computerized systems that are increasingly used to facilitate safe and efficient health care. Their applications range in depth and can be found across health care specialties.

OBJECTIVE

This scoping review aims to explore the use of CDSSs in patients with spinal disorders.

METHODS

We used the Joanna Briggs Institute methodological guidance for this scoping review and reported according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) statement. Databases, including PubMed, Embase, Cochrane, CINAHL, Web of Science, Scopus, ProQuest, and PsycINFO, were searched from inception until October 11, 2022. The included studies examined the use of digitalized CDSSs in patients with spinal disorders.

RESULTS

A total of 4 major CDSS functions were identified from 31 studies: preventing unnecessary imaging (n=8, 26%), aiding diagnosis (n=6, 19%), aiding prognosis (n=11, 35%), and recommending treatment options (n=6, 20%). Most studies used the knowledge-based system. Logistic regression was the most commonly used method, followed by decision tree algorithms. The use of CDSSs to aid in the management of spinal disorders was generally accepted over the threat to physicians' clinical decision-making autonomy.

CONCLUSIONS

Although the effectiveness was frequently evaluated by examining the agreement between the decisions made by the CDSSs and the health care providers, comparing the CDSS recommendations with actual clinical outcomes would be preferable. In addition, future studies on CDSS development should focus on system integration, considering end user's needs and preferences, and external validation and impact studies to assess effectiveness and generalizability.

TRIAL REGISTRATION

OSF Registries osf.io/dyz3f; https://osf.io/dyz3f.

Using Augmented Reality Technology to Optimize Transfacet Lumbar Interbody Fusion: A Case Report

The transfacet minimally invasive transforaminal lumbar interbody fusion (MIS-TLIF) is a novel approach available for the management of lumbar spondylolisthesis. It avoids the need to manipulate either of the exiting or traversing nerve roots, both protected by the bony boundaries of the approach. With the advancement in operative technologies such as navigation, mapping, segmentation, and augmented reality (AR), surgeons are prompted to utilize these technologies to enhance their surgical outcomes. A 36-year-old male patient was complaining of chronic progressive lower back pain. He was found to have grade 2 L4/5 spondylolisthesis. We studied the feasibility of a trans-Kambin or a transfacet MIS-TLIF, and decided to proceed with the latter given the wider corridor it provides. Preoperative trajectory planning and level segmentation in addition to intraoperative navigation and image merging were all utilized to provide an AR model to guide us through the surgery. The use of AR can build on the safety and learning of novel surgical approaches to spine pathologies. However, larger high-quality studies are needed to further objectively analyze its impact on surgical outcomes and to expand on its application.

Automated Detection of Cervical Spinal Stenosis and Cord Compression via Vision Transformer and Rules-Based Classification

BACKGROUND AND PURPOSE

Cervical spinal cord compression, defined as spinal cord deformity and severe narrowing of the spinal canal in the cervical region, can lead to severe clinical consequences, including intractable pain, sensory disturbance, paralysis, and even death, and may require emergent intervention to prevent negative outcomes. Despite the critical nature of cord compression, no automated tool is available to alert clinical radiologists to the presence of such findings. This study aims to demonstrate the ability of a vision transformer (ViT) model for the accurate detection of cervical cord compression.

MATERIALS AND METHODS

A clinically diverse cohort of 142 cervical spine MRIs was identified, 34% of which were normal or had mild stenosis, 31% with moderate stenosis, and 35% with cord compression. Utilizing gradient-echo images, slices were labeled as no cord compression/mild stenosis, moderate stenosis, or severe stenosis/cord compression. Segmentation of the spinal canal was performed and confirmed by neuroradiology faculty. A pretrained ViT model was fine-tuned to predict section-level severity by using a train:validation:test split of 60:20:20. Each examination was assigned an overall severity based on the highest level of section severity, with an examination labeled as positive for cord compression if ≥1 section was predicted in the severe category. Additionally, 2 convolutional neural network (CNN) models (ResNet50, DenseNet121) were tested in the same manner.

RESULTS

The ViT model outperformed both CNN models at the section level, achieving section-level accuracy of 82%, compared with 72% and 78% for ResNet and DenseNet121, respectively. ViT patient-level classification achieved accuracy of 93%, sensitivity of 0.90, positive predictive value of 0.90, specificity of 0.95, and negative predictive value of 0.95. Receiver operating characteristic area under the curve was greater for ViT than either CNN.

CONCLUSIONS

This classification approach using a ViT model and rules-based classification accurately detects the presence of cervical spinal cord compression at the patient level. In this study, the ViT model outperformed both conventional CNN approaches at the section and patient levels. If implemented into the clinical setting, such a tool may streamline neuroradiology workflow, improving efficiency and consistency.

Machine Learning and Deep Learning in Spinal Injury: A Narrative Review of Algorithms in Diagnosis and Prognosis

Spinal injuries, including cervical and thoracolumbar fractures, continue to be a major public health concern. Recent advancements in machine learning and deep learning technologies offer exciting prospects for improving both diagnostic and prognostic approaches in spinal injury care. This narrative review systematically explores the practical utility of these computational methods, with a focus on their application in imaging techniques such as computed tomography (CT) and magnetic resonance imaging (MRI), as well as in structured clinical data. Of the 39 studies included, 34 were focused on diagnostic applications, chiefly using deep learning to carry out tasks like vertebral fracture identification, differentiation between benign and malignant fractures, and AO fracture classification. The remaining five were prognostic, using machine learning to analyze parameters for predicting outcomes such as vertebral collapse and future fracture risk. This review highlights the potential benefit of machine learning and deep learning in spinal injury care, especially their roles in enhancing diagnostic capabilities, detailed fracture characterization, risk assessments, and individualized treatment planning.

Machine Learning in Spine Surgery: A Narrative Review

Artificial intelligence and machine learning (ML) can offer revolutionary advances in their application to the field of spine surgery. Within the past 5 years, novel applications of ML have assisted in surgical decision-making, intraoperative imaging and navigation, and optimization of clinical outcomes. ML has the capacity to address many different clinical needs and improve diagnostic and surgical techniques. This review will discuss current applications of ML in the context of spine surgery by breaking down its implementation preoperatively, intraoperatively, and postoperatively. Ethical considerations to ML and challenges in ML implementation must be addressed to maximally benefit patients, spine surgeons, and the healthcare system. Areas for future research in augmented reality and mixed reality, along with limitations in generalizability and bias, will also be highlighted.

Predictive modeling of perioperative blood transfusion in lumbar posterior interbody fusion using machine learning

Background: Accurate estimation of perioperative blood transfusion risk in lumbar posterior interbody fusion is essential to reduce the number, cost, and complications associated with blood transfusions. Machine learning algorithms have the potential to outperform traditional prediction methods in predicting perioperative blood transfusion. This study aimed to construct a machine learning-based perioperative transfusion risk prediction model for lumbar posterior interbody fusion in order to improve the efficacy of surgical decision-making. Methods: We retrospectively collected clinical data on 1905 patients who underwent lumbar posterior interbody fusion surgery at the Second Hospital of Shanxi Medical University between January 2021 and March 2023. All the data was randomly divided into a training set and a validation set, and the "feature_importances" method provided by eXtreme Gradient Boosting (XGBoost) algorithm was applied to select statistically significant features on the training set to establish five machine learning prediction models. The optimal model was identified by utilizing the area under the curve (AUC) and the probability calibration curve on the validation set. Shapley additive explanations (SHAP) and local interpretable model-agnostic explanations (LIME) were employed for interpretable analysis of the optimal model. Results: In the postoperative outcomes of patients, the number of hospital days in the transfusion group was longer than that in the non-transfusion group. Additionally, the transfusion group experienced higher total hospital costs, 90-day readmission rates, and complication rates within 90 days after surgery than the non-transfusion group. A total of 9 features were selected for the models. The XGBoost model performed best with an AUC value of 0.958. The SHAP values showed that intraoperative blood loss, intraoperative fluid infusion, and number of fused segments were the top 3 most important features affecting perioperative blood transfusion in lumbar posterior interbody fusion. The LIME algorithm was used to interpret the individualized prediction. Conclusion: Surgery, ASA class, levels fused, total intraoperative blood loss, operative time, and preoperative Hb are viable predictors of perioperative blood transfusion in lumbar posterior interbody fusion. The XGBoost model has demonstrated superior predictive efficacy compared to the traditional logistic regression model, making it a more effective decision-making tool for perioperative blood transfusion.

Predictive value of texture analysis on lumbar MRI in patients with chronic low back pain

PURPOSE

The aim of this study was to determine whether MRI texture analysis could predict the prognosis of patients with non-specific chronic low back pain.

METHODS

A prospective observational study was conducted on 100 patients with non-specific chronic low back pain, who underwent a conventional MRI, followed by rehabilitation treatment, and revisited after 6 months. Sociodemographic variables, numeric pain scale (NPS) value, and the degree of disability as measured by the Roland-Morris disability questionnaire (RMDQ), were collected. The MRI analysis included segmentation of regions of interest (vertebral endplates and intervertebral disks from L3-L4 to L5-S1, paravertebral musculature at the L4-L5 space) to extract texture variables (PyRadiomics software). The classification random forest algorithm was applied to identify individuals who would improve less than 30% in the NPS or would score more than 4 in the RMDQ at the end of the follow-up. Sensitivity, specificity, and the area under the ROC curve were calculated.

RESULTS

The final series included 94 patients. The predictive model for classifying patients whose pain did not improve by 30% or more offered a sensitivity of 0.86, specificity 0.57, and area under the ROC curve 0.71. The predictive model for classifying patients with a RMDQ score 4 or more offered a sensitivity of 0.83, specificity of 0.20, and area under the ROC curve of 0.52.

CONCLUSION

The texture analysis of lumbar MRI could help identify patients who are more likely to improve their non-specific chronic low back pain through rehabilitation programs, allowing a personalized therapeutic plan to be established.

Interpretable Machine Learning Model to Predict Bone Cement Leakage in Percutaneous Vertebral Augmentation for Osteoporotic Vertebral Compression Fracture Based on SHapley Additive exPlanations

STUDY DESIGN

Retrospective study.

OBJECTIVES

Our objective is to create comprehensible machine learning (ML) models that can forecast bone cement leakage in percutaneous vertebral augmentation (PVA) for individuals with osteoporotic vertebral compression fracture (OVCF) while also identifying the associated risk factors.

METHODS

We incorporated data from patients (n = 425) which underwent PVA. To predict cement leakage, we devised six models based on a variety of parameters. Evaluate and juxtapose the predictive performances relied on measures of discrimination, calibration, and clinical utility. SHapley Additive exPlanations (SHAP) methodology was used to interpret model and evaluate the risk factors associated with cement leakage.

RESULTS

The occurrence rate of cement leakage was established at 50.4%. A binary logistic regression analysis identified cortical disruption (OR 6.880, 95% CI 4.209-11.246), the basivertebral foramen sign (OR 2.142, 95% CI 1.303-3.521), the fracture type (OR 1.683, 95% CI 1.083-2.617), and the volume of bone cement (OR 1.198, 95% CI 1.070-1.341) as independent predictors of cement leakage. The XGBoost model outperformed all others in predicting cement leakage in the testing set, with AUC of .8819, accuracy of .8025, recall score of .7872, F1 score of .8315, and a precision score of .881. Several important factors related to cement leakage were drawn based on the analysis of SHAP values and their clinical significance.

CONCLUSION

The ML based predictive model demonstrated significant accuracy in forecasting bone cement leakage for patients with OVCF undergoing PVA. When combined with SHAP, ML facilitated a personalized prediction and offered a visual interpretation of feature importance.

Developing a new tool for scoliosis screening in a tertiary specialistic setting using artificial intelligence: a retrospective study on 10,813 patients: 2023 SOSORT award winner

PURPOSE

The study aims to assess if the angle of trunk rotation (ATR) in combination with other readily measurable clinical parameters allows for effective non-invasive scoliosis screening.

METHODS

We analysed 10,813 patients (4-18 years old) who underwent clinical and radiological evaluation for scoliosis in a tertiary clinic specialised in spinal deformities. We considered as predictors ATR, Prominence (mm), visible asymmetry of the waist, scapulae and shoulders, familiarity, sex, BMI, age, menarche, and localisation of the curve. We implemented a Logistic Regression model to classify the Cobb angle of the major curve according to thresholds of 15, 20, 25, 30, and 40 degrees, by randomly splitting the dataset into 80-20% for training and testing, respectively.

RESULTS

The model showed accuracies of 74, 81, 79, 79, and 84% for 15-, 20-, 25-, 30- and 40-degrees thresholds, respectively. For all the thresholds ATR, Prominence, and visible asymmetry of the waist were the top five most important variables for the prediction. Samples that were wrongly classified as negatives had always statistically significant (p ≪ 0.01) lower values of ATR and Prominence. This confirmed that these two parameters were very important for the correct classification of the Cobb angle. The model showed better performances than using the 5 and 7 degrees ATR thresholds to prescribe a radiological examination.

CONCLUSIONS

Machine-learning-based classification models have the potential to effectively improve the non-invasive screening for AIS. The results of the study constitute the basis for the development of easy-to-use tools enabling physicians to decide whether to prescribe radiographic imaging.

Revolutionizing Spinal Care: Current Applications and Future Directions of Artificial Intelligence and Machine Learning

Artificial intelligence (AI) and machine learning (ML) are rapidly becoming integral components of modern healthcare, offering new avenues for diagnosis, treatment, and outcome prediction. This review explores their current applications and potential future in the field of spinal care. From enhancing imaging techniques to predicting patient outcomes, AI and ML are revolutionizing the way we approach spinal diseases. AI and ML have significantly improved spinal imaging by augmenting detection and classification capabilities, thereby boosting diagnostic accuracy. Predictive models have also been developed to guide treatment plans and foresee patient outcomes, driving a shift towards more personalized care. Looking towards the future, we envision AI and ML further ingraining themselves in spinal care with the development of algorithms capable of deciphering complex spinal pathologies to aid decision making. Despite the promise these technologies hold, their integration into clinical practice is not without challenges. Data quality, integration hurdles, data security, and ethical considerations are some of the key areas that need to be addressed for their successful and responsible implementation. In conclusion, AI and ML represent potent tools for transforming spinal care. Thoughtful and balanced integration of these technologies, guided by ethical considerations, can lead to significant advancements, ushering in an era of more personalized, effective, and efficient healthcare.

Machine Learning Prediction Model and Risk Factor Analysis of Reoperation in Recurrent Lumbar Disc Herniation Patients After Percutaneous Endoscopic Lumbar Discectomy

OBJECTIVE

To investigate the risk factors of reoperation after percutaneous endoscopic lumbar discectomy (PELD) due to recurrent lumbar disc herniation (rLDH) and to establish a set of individualized prediction models.

METHODS

Patients who underwent PELD successfully from January 2016 to February 2022 in a single institution were enrolled in this study. Six methods of machine learning (ML) were used to establish an individualized prediction model for reoperation in rLDH patients after PELD, and these models were compared with logistics regression model to select optimal model.

RESULTS

A total of 2603 patients were enrolled in this study. 57 patients had repeated operation due to rLDH and 114 patients were selected from the remaining 2546 nonrecurrent patients as matched controls. Multivariate logistic regression analysis showed that disc herniation type (P < .001), Modic changes (type II) (P = .003), sagittal range of motion (sROM) (P = .022), facet orientation (FO) (P = .028) and fat infiltration (FI) (P = .001) were independent risk factors for reoperation in rLDH patients after PELD. The XGBoost AUC was of 90.71%, accuracy was approximately 88.87%, sensitivity was 70.81%, specificity was 97.19%. The traditional logistic regression AUC was 77.4%, accuracy was about 77.73%, sensitivity was 47.15%, specificity was 92.12%.

CONCLUSION

This study showed that disc herniation type (extrusion, sequestration), Modic changes (type II), a large sROM, a large FO and high FI were independent risk factors for reoperation in LDH patients after PELD. The prediction efficiency of XGBoost model was higher than traditional Logistic regression analysis model.

Predictive factors for degenerative lumbar spinal stenosis: a model obtained from a machine learning algorithm technique

BACKGROUND

Degenerative lumbar spinal stenosis (DLSS) is the most common spine disease in the elderly population. It is usually associated with lumbar spine joints/or ligaments degeneration. Machine learning technique is an exclusive method for handling big data analysis; however, the development of this method for spine pathology is rare. This study aims to detect the essential variables that predict the development of symptomatic DLSS using the random forest of machine learning (ML) algorithms technique.

METHODS

A retrospective study with two groups of individuals. The first included 165 with symptomatic DLSS (sex ratio 80 M/85F), and the second included 180 individuals from the general population (sex ratio: 90 M/90F) without lumbar spinal stenosis symptoms. Lumbar spine measurements such as vertebral or spinal canal diameters from L1 to S1 were conducted on computerized tomography (CT) images. Demographic and health data of all the participants (e.g., body mass index and diabetes mellitus) were also recorded.

RESULTS

The decision tree model of ML demonstrate that the anteroposterior diameter of the bony canal at L5 (males) and L4 (females) levels have the greatest stimulus for symptomatic DLSS (scores of 1 and 0.938). In addition, combination of these variables with other lumbar spine features is mandatory for developing the DLSS.

CONCLUSIONS

Our results indicate that combination of lumbar spine characteristics such as bony canal and vertebral body dimensions rather than the presence of a sole variable is highly associated with symptomatic DLSS onset.

Coronal and sagittal spinopelvic alignment in the patients with unilateral developmental dysplasia of the hip: a prospective study

BACKGROUND

How the hip dysplasia affects the spinopelvic alignment in developmental dysplasia of the hip (DDH) patients is unclear, but it is an essential part for the management of this disease. This study aimed to investigate the coronal and sagittal spinopelvic alignment and the correlations between the spinopelvic parameters and the extent of hip dysplasia or the low back pain in unilateral DDH patients.

METHODS

From September 2016 to March 2021, 22 unilateral patients were enrolled in the DDH group with an average age of 43.6 years and 20 recruited healthy volunteers were assigned to the control group with an average age of 41.4 years. The Cobb angle, seventh cervical vertebra plumbline-central sacral vertical line (C7PL-CSVL), third lumbar vertebra inclination angle (L3IA), pelvic incidence (PI), pelvic tilt (PT), sacral slope (SS), thoracic kyphosis (TK), thoracolumbar kyphosis (TLK) and lumbar lordosis (LL) were measured on the standing anteroposterior and lateral full-length standing spine radiographs. Additionally, the Oswestry Disability Index (ODI) and Japanese Orthopaedic Association Back Pain Evaluation Questionnaire (JOABPEQ) were used to assess the degree of low back pain.

RESULTS

Cobb angle (8.68 ± 6.21° vs. 2.31 ± 0.12°), L3IA (4.80 ± 5.47° vs. 0.83 ± 0.51°), C7PL-CSVL (1.65 ± 1.57 cm vs. 0.48 ± 0.33 cm), PT (15.02 ± 9.55° vs. 9.99 ± 2.97°) and TLK (7.69 ± 6.66° vs. 3.54 ± 1.63°) were significantly larger in DDH patients, whereas LL (37.41 ± 17.17° vs. 48.79 ± 7.75°) was significantly smaller (P < 0.05). No correlation was found between significantly different spinopelvic parameters and the extent of dysplasia. Statistical analysis revealed correlations between ODI and Cobb angle (r = 0.59, P < 0.01), PT (r = 0.49, P = 0.02), TK (r =  -0.46, P = 0.03) and TLK (r = 0.44, P = 0.04). Correlations between JOABPQE score and the Cobb angle (r = -0.44, P = 0.04), L3IA (r = -0.53, P = 0.01), PT (r = -0.44, P = 0.04), and TK (r = 0.46, P = 0.03) were also observed.

CONCLUSIONS

Cobb angle, L3IA, C7PL-CSVL in coronal plane and PT, TLK in sagittal plane increased, while LL decreased in unilateral DDH patients. These significantly different spinopelvic parameters have no correlation with the extent of dysplasia. Changes in coronal and sagittal plane including Cobb angle, L3IA, PT, TK and TLK were associated with the low back pain in the patients with unilateral DDH.