Vertebral Deformity Measurements at MRI, CT, and Radiography Using Deep Learning

An automated algorithm for quantitative morphometry of thoracic and lumbar vertebral bodies in lateral radiographs

This exploratory study developed and evaluated an artificial intelligence (AI)-based algorithm for quantitative morphometry to assess vertebral body deformities indicative of fractures. To achieve this, 709 radiographs from 355 cases were utilized for algorithm development and performance evaluation. The proposed algorithm integrates a first-stage AI model to identify the positions of thoracic and lumber vertebral bodies in lateral radiographs and a second-stage AI model to annotate 6 landmarks for calculating vertebral body height ratios (C/A, C/P, and A/P). The first-stage AI model achieved a sensitivity of 97.6%, a precision of 95.1%, and an average false-positive ratio of 0.43 per image for vertebral body detection. In the second stage, the algorithm's performance was evaluated using an independent dataset of vertebrae annotated by 2 spine surgeons and 1 radiologist. The average landmark errors ranged from 2.9% to 3.3% on the X-axis and 2.9% to 4.0% on the Y-axis, with errors increasing in more severely collapsed vertebrae, particularly at central landmarks. Spearman's correlation coefficients were 0.519-0.589 for C/A, 0.558-0.647 for C/P, and 0.735-0.770 for A/P, comparable with correlations observed among human evaluators. Bland-Altman analysis revealed systematic bias in some cases, indicating that the algorithm underestimated anterior and central height collapse in deformed vertebrae. However, the mean differences and limits of agreement between the algorithm and external evaluators were similar to those among the evaluators. Additionally, the algorithm processed each image within 10 s. These findings suggest that the algorithm performs comparably with human evaluators, demonstrating sufficient accuracy for clinical use. The proposed approach has the potential to enhance patient care by being widely adopted in clinical settings.

Quantification and classification of lumbar disc herniation on axial magnetic resonance images using deep learning models

PURPOSE

Application of a deep learning model visualization plugin for rapid and accurate automatic quantification and classification of lumbar disc herniation (LDH) types on axial T2-weighted MRIs.

METHODS

Retrospective analysis of 2500 patients, with the training set comprising data from 2120 patients (25,554 images), an internal test set covering data from 80 patients (784 images), and an external test set including data from 300 patients (3285 images). To enhance implementation, this study categorized normal and bulging discs as a grade without significant abnormalities, defining the region and severity grades of LDH based on the relationship between the disc and the spinal canal. The automated detection training and validation process employed the YOLOv8 object detection model for target area localization, the YOLOv8-seg segmentation model for disc recognition, and the YOLOv8-pose keypoint detection model for positioning. Finally, the stability of the detection results was verified using metrics such as Intersection over Union (IoU), mean error (ME), precision (P), F1 score (F1), Kappa coefficient (kappa), and 95% confidence interval (95%CI).

RESULTS

The segmentation model achieved an mAP50:95 of 98.12% and an IoU of 98.36% in the training set, while the keypoint detection model achieved an mAP50:95 of 93.58% with a mean error (ME) of 0.208 mm. For the internal and external test sets, the segmentation model's IoU was 97.58 and 97.49%, respectively, while the keypoint model's ME was 0.219 mm and 0.221 mm, respectively. In the quantification validation of the extent of LDH, P, F1, and kappa were measured. For LDH classification (18 categories), the internal and external test sets showed P = 81.21% and 74.50%, F1 = 81.26% and 74.42%, and kappa = 0.75 (95%CI 0.68, 0.82, p = 0.00) and 0.69 (95%CI 0.65, 0.73, p = 0.00), respectively. For the severity grades of LDH (four categories), the internal and external test sets showed P = 92.51% and 90.07%, F1 = 92.36% and 89.66%, and kappa = 0.88 (95%CI 0.80, 0.96, p = 0.00) and 0.85 (95%CI 0.81, 0.89, p = 0.00), respectively. For the regions of LDH (eight categories), the internal and external test sets showed P = 83.34% and 77.87%, F1 = 83.85% and 78.21%, and kappa = 0.77 (95%CI 0.70, 0.85, p = 0.00) and 0.71 (95%CI 0.67, 0.75, p = 0.00), respectively.

CONCLUSION

The automated aided diagnostic model achieved high performance in detecting and classifying LDH and demonstrated substantial consistency with expert classification.

Normative vertebral deformity measurements in a clinically relevant population using magnetic resonance imaging

BACKGROUND

Osteoporosis is the leading cause of vertebral fractures. Dual-energy X-ray absorptiometry (DXA) and radiographs are traditionally used to detect osteoporosis and vertebral fractures/deformities. Magnetic resonance imaging (MRI) can be utilized to detect the relative severity of vertebral deformities using three-dimensional information not available in traditional DXA and lateral two-dimensional radiography imaging techniques.

AIM

To generate normative vertebral parameters in women using MRI and DXA scans, determine the correlations between MRI-calculated vertebral deformities and age, DXA T-scores, and DXA Z-scores, and compare MRI vertebral deformity values with radiography values previously published in the literature.

METHODS

This study is a retrospective vertebral morphometric analysis conducted at our institution. The patient sample included MR images from 1638 female patients who underwent both MR and DXA imaging between 2005 and 2014. Biconcavity, wedge, crush, anterior height (Ha)/posterior height (Hp), and middle height (Hm)/posterior height values were calculated from the MR images of the patient's vertebrae. Associations between vertebral deformity values, patient age, and DXA T-scores were analyzed using Spearman correlation. The MRI-derived measurements were compared with radiograph-based calculations from population-based data compiled from multiple studies.

RESULTS

Age was positively correlated with lumbar Hm/Hp (P = 0.04) and thoracic wedge (P = 0.03) and biconcavity (P = 0.001) and negatively correlated with thoracic Ha/Hp (P = 0.002) and Hm/Hp (P = 0.001) values. DXA T-scores correlated positively with lumbar Hm/Hp (P < 0.0001) and negatively with lumbar wedge (P = 0.046), biconcavity (P < 0.0001), and Ha/Hp (P = 0.046) values. Qualitative analysis revealed that Ha/Hp differed between MRI and radiography population-based data by no more than 0.3 and Hm/Hp by a maximum of 1.2.

CONCLUSION

Compared with traditional imaging techniques, MRI detects vertebral deformities with high accuracy and reliability. It may be a sensitive, ionizing, radiation-free tool for use in clinical settings.

Lumbar Spinal Stenosis Grading in Multiple Level Magnetic Resonance Imaging Using Deep Convolutional Neural Networks

STUDY DESIGN

Retrospective magnetic resonance imaging grading with comparison between experts and deep convolutional neural networks (CNNs).

OBJECTIVE

The application of deep learning to clinical diagnosis has gained popularity. This approach can accelerate image interpretation and serve as a screening tool to help doctors.

METHODS

A comparison was conducted between retrospective magnetic resonance imaging (MRI) grading performed by experts and grading obtained using CNN classifiers. Data were collected from the lumbar axial dataset in the DICOM format. Two experts labeled the sampled images using the same diagnostic tools: localization of patches near the spinal canal, rootlet leveling, and stenosis grading. Comprehensive comparisons were presented for both rootlet cord classification and stenosis grading.

RESULTS

Rootlet-cord classification for the two analyzers was 90.3% and the F1 score was 86.6%. The agreement of Analyzers-Classifiers was 92.7% and 96.8% for data with 90.6% and 95.6% F1 scores, respectively. For stenosis grading, there was an agreement of 89.2% between the two analyzers, resulting in an F1 score of 76.5%. The grades of the Analyzers-Classifiers agreed on 91.5/89.4% of the data, with an F1 score of 78.4/75.7%. Analyzer1 and Analyzer2 classified >74% as grade A (78.8% and 74.4%, respectively), 15.4% and 18.6% as grade B, 4.2% and 6.0% as grade C, and 1.6% and 2.0% as grade D, respectively.

CONCLUSIONS

The fully automated deep learning model showed competitive results in stenosis grade diagnosis and rootlet cord classification under similar anatomical conditions. However, abrupt anatomical changes can lead to a puzzle diagnosis based only on images.

An Automated Vertebrae Localization, Segmentation, and Osteoporotic Compression Fracture Detection Pipeline for Computed Tomographic Imaging

Osteoporosis is the most common chronic metabolic bone disease worldwide. Vertebral compression fracture (VCF) is the most common type of osteoporotic fracture. Approximately 700,000 osteoporotic VCFs are diagnosed annually in the USA alone, resulting in an annual economic burden of ~$13.8B. With an aging population, the rate of osteoporotic VCFs and their associated burdens are expected to rise. Those burdens include pain, functional impairment, and increased medical expenditure. Therefore, it is of utmost importance to develop an analytical tool to aid in the identification of VCFs. Computed Tomography (CT) imaging is commonly used to detect occult injuries. Unlike the existing VCF detection approaches based on CT, the standard clinical criteria for determining VCF relies on the shape of vertebrae, such as loss of vertebral body height. We developed a novel automated vertebrae localization, segmentation, and osteoporotic VCF detection pipeline for CT scans using state-of-the-art deep learning models to bridge this gap. To do so, we employed a publicly available dataset of spine CT scans with 325 scans annotated for segmentation, 126 of which also graded for VCF (81 with VCFs and 45 without VCFs). Our approach attained 96% sensitivity and 81% specificity in detecting VCF at the vertebral-level, and 100% accuracy at the subject-level, outperforming deep learning counterparts tested for VCF detection without segmentation. Crucially, we showed that adding predicted vertebrae segments as inputs significantly improved VCF detection at both vertebral and subject levels by up to 14% Sensitivity and 20% Specificity (p-value = 0.028).

3D U-Net Neural Network Architecture-Assisted LDCT to Acquire Vertebral Morphology Parameters: A Vertebral Morphology Comprehensive Analysis in a Chinese Population

To evaluate the feasibility of acquiring vertebral height from chest low-dose computed tomography (LDCT) images using an artificial intelligence (AI) system based on 3D U-Net vertebral segmentation technology and the correlation and features of vertebral morphology with sex and age of the Chinese population. Patients who underwent chest LDCT between September 2020 and April 2023 were enrolled. The Altman and Pearson's correlation analyses were used to compare the correlation and consistency between the AI software and manual measurement of vertebral height. The anterior height (Ha), middle height (Hm), posterior height (Hp), and vertebral height ratios (VHRs) (Ha/Hp and Hm/Hp) were measured from T1 to L2 using an AI system. The VHR is the ratio of Ha to Hp or the ratio of Hm to Hp of the vertebrae, which can reflect the shape of the anterior wedge and biconcave vertebrae. Changes in these parameters, particularly the VHR, were analysed at different vertebral levels in different age and sex groups. The results of the AI methods were highly consistent and correlated with manual measurements. The Pearson's correlation coefficients were 0.855, 0.919, and 0.846, respectively. The trend of VHRs showed troughs at T7 and T11 and a peak at T9; however, Hm/Hp showed slight fluctuations. Regarding the VHR, significant sex differences were found at L1 and L2 in all age bands. This innovative study focuses on vertebral morphology for opportunistic analysis in the mainland Chinese population and the distribution tendency of vertebral morphology with ageing using a chest LDCT aided by an AI system based on 3D U-Net vertebral segmentation technology. The AI system demonstrates the potential to automatically perform opportunistic vertebral morphology analyses using LDCT scans obtained during lung cancer screening. We advocate the use of age-, sex-, and vertebral level-specific criteria for the morphometric evaluation of vertebral osteoporotic fractures for a more accurate diagnosis of vertebral fractures and spinal pathologies.

Applications of Artificial Intelligence and Machine Learning in Spine MRI

Diagnostic imaging, particularly MRI, plays a key role in the evaluation of many spine pathologies. Recent progress in artificial intelligence and its subset, machine learning, has led to many applications within spine MRI, which we sought to examine in this review. A literature search of the major databases (PubMed, MEDLINE, Web of Science, ClinicalTrials.gov) was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The search yielded 1226 results, of which 50 studies were selected for inclusion. Key data from these studies were extracted. Studies were categorized thematically into the following: Image Acquisition and Processing, Segmentation, Diagnosis and Treatment Planning, and Patient Selection and Prognostication. Gaps in the literature and the proposed areas of future research are discussed. Current research demonstrates the ability of artificial intelligence to improve various aspects of this field, from image acquisition to analysis and clinical care. We also acknowledge the limitations of current technology. Future work will require collaborative efforts in order to fully exploit new technologies while addressing the practical challenges of generalizability and implementation. In particular, the use of foundation models and large-language models in spine MRI is a promising area, warranting further research. Studies assessing model performance in real-world clinical settings will also help uncover unintended consequences and maximize the benefits for patient care.

Artificial Intelligence in Spinal Imaging and Patient Care: A Review of Recent Advances

Artificial intelligence (AI) is transforming spinal imaging and patient care through automated analysis and enhanced decision-making. This review presents a clinical task-based evaluation, highlighting the specific impact of AI techniques on different aspects of spinal imaging and patient care. We first discuss how AI can potentially improve image quality through techniques like denoising or artifact reduction. We then explore how AI enables efficient quantification of anatomical measurements, spinal curvature parameters, vertebral segmentation, and disc grading. This facilitates objective, accurate interpretation and diagnosis. AI models now reliably detect key spinal pathologies, achieving expert-level performance in tasks like identifying fractures, stenosis, infections, and tumors. Beyond diagnosis, AI also assists surgical planning via synthetic computed tomography generation, augmented reality systems, and robotic guidance. Furthermore, AI image analysis combined with clinical data enables personalized predictions to guide treatment decisions, such as forecasting spine surgery outcomes. However, challenges still need to be addressed in implementing AI clinically, including model interpretability, generalizability, and data limitations. Multicenter collaboration using large, diverse datasets is critical to advance the field further. While adoption barriers persist, AI presents a transformative opportunity to revolutionize spinal imaging workflows, empowering clinicians to translate data into actionable insights for improved patient care.

Artificial Intelligence to Preoperatively Predict Proximal Junction Kyphosis Following Adult Spinal Deformity Surgery: Soft Tissue Imaging May Be Necessary for Accurate Models

STUDY DESIGN

Retrospective cohort.

OBJECTIVE

In a cohort of patients undergoing adult spinal deformity (ASD) surgery, we used artificial intelligence to compare three models of preoperatively predicting radiographic proximal junction kyphosis (PJK) using: (1) traditional demographics and radiographic measurements, (2) raw preoperative scoliosis radiographs, and (3) raw preoperative thoracic magnetic resonance imaging (MRI).

SUMMARY OF BACKGROUND DATA

Despite many proposed risk factors, PJK following ASD surgery remains difficult to predict.

MATERIALS AND METHODS

A single-institution, retrospective cohort study was undertaken for patients undergoing ASD surgery from 2009 to 2021. PJK was defined as a sagittal Cobb angle of upper-instrumented vertebra (UIV) and UIV+2>10° and a postoperative change in UIV/UIV+2>10°. For model 1, a support vector machine was used to predict PJK within 2 years postoperatively using clinical and traditional sagittal/coronal radiographic variables and intended levels of instrumentation. Next, for model 2, a convolutional neural network (CNN) was trained on raw preoperative lateral and posterior-anterior scoliosis radiographs. Finally, for model 3, a CNN was trained on raw preoperative thoracic T1 MRIs.

RESULTS

A total of 191 patients underwent ASD surgery with at least 2-year follow-up and 89 (46.6%) developed radiographic PJK within 2 years. Model 1: Using clinical variables and traditional radiographic measurements, the model achieved a sensitivity: 57.2% and a specificity: 56.3%. Model 2: a CNN with raw scoliosis x-rays predicted PJK with a sensitivity: 68.2% and specificity: 58.3%. Model 3: a CNN with raw thoracic MRIs predicted PJK with average sensitivity: 73.1% and specificity: 79.5%. Finally, an attention map outlined the imaging features used by model 3 elucidated that soft tissue features predominated all true positive PJK predictions.

CONCLUSIONS

The use of raw MRIs in an artificial intelligence model improved the accuracy of PJK prediction compared with raw scoliosis radiographs and traditional clinical/radiographic measurements. The improved predictive accuracy using MRI may indicate that PJK is best predicted by soft tissue degeneration and muscle atrophy.

Conquering the Cobb Angle: A Deep Learning Algorithm for Automated, Hardware-Invariant Measurement of Cobb Angle on Radiographs in Patients with Scoliosis

Scoliosis is a disease estimated to affect more than 8% of adults in the United States. It is diagnosed with use of radiography by means of manual measurement of the angle between maximally tilted vertebrae on a radiograph (ie, the Cobb angle). However, these measurements are time-consuming, limiting their use in scoliosis surgical planning and postoperative monitoring. In this retrospective study, a pipeline (using the SpineTK architecture) was developed that was trained, validated, and tested on 1310 anterior-posterior images obtained with a low-dose stereoradiographic scanning system and radiographs obtained in patients with suspected scoliosis to automatically measure Cobb angles. The images were obtained at six centers (2005-2020). The algorithm measured Cobb angles on hold-out internal (n = 460) and external (n = 161) test sets with less than 2° error (intraclass correlation coefficient, 0.96) compared with ground truth measurements by two experienced radiologists. Measurements, produced in less than 0.5 second, did not differ significantly (P = .05 cutoff) from ground truth measurements, regardless of the presence or absence of surgical hardware (P = .80), age (P = .58), sex (P = .83), body mass index (P = .63), scoliosis severity (P = .44), or image type (low-dose stereoradiographic image vs radiograph; P = .51) in the patient. These findings suggest that the algorithm is highly robust across different clinical characteristics. Given its automated, rapid, and accurate measurements, this network may be used for monitoring scoliosis progression in patients. Keywords: Cobb Angle, Convolutional Neural Network, Deep Learning Algorithms, Pediatrics, Machine Learning Algorithms, Scoliosis, Spine Supplemental material is available for this article. © RSNA, 2023.

SpineQ: Unsupervised 3D Lumbar Quantitative Assessment

Most lumbar quantitative assessment methods can only analyze the image from one view and require laborious manual annotation. We aim to develop an unsupervised pipeline for 3D quantitative assessment of the lumbar spine that can assess the MRI with different views. We combine rule-based and deep learning methods to generate multi-tissue segmentation, and parameters can be measured from segmentation results using the anatomical and geometric prior. Preliminary testing demonstrates that our proposed method can generate accurate segmentation and measurement results.Clinical Relevance- The proposed unsupervised 3D lumbar quantitative assessment pipeline can significantly improve the efficiency and consistency of clinical diagnosis and surgical planning.

Automatic vertebral fracture and three-column injury diagnosis with fracture visualization by a multi-scale attention-guided network

Deep learning methods have the potential to improve the efficiency of diagnosis for vertebral fractures with computed tomography (CT) images. Most existing intelligent vertebral fracture diagnosis methods only provide dichotomized results at a patient level. However, a fine-grained and more nuanced outcome is clinically needed. This study proposed a novel network, a multi-scale attention-guided network (MAGNet), to diagnose vertebral fractures and three-column injuries with fracture visualization at a vertebra level. By imposing attention constraints through a disease attention map (DAM), a fusion of multi-scale spatial attention maps, the MAGNet can get task highly relevant features and localize fractures. A total of 989 vertebrae were studied here. After four-fold cross-validation, the area under the ROC curve (AUC) of our model for vertebral fracture dichotomized diagnosis and three-column injury diagnosis was 0.884 ± 0.015 and 0.920 ± 0.104, respectively. The overall performance of our model outperformed classical classification models, attention models, visual explanation methods, and attention-guided methods based on class activation mapping. Our work can promote the clinical application of deep learning to diagnose vertebral fractures and provide a way to visualize and improve the diagnosis results with attention constraints.

Performance of a deep convolutional neural network for MRI-based vertebral body measurements and insufficiency fracture detection

OBJECTIVES

The aim is to validate the performance of a deep convolutional neural network (DCNN) for vertebral body measurements and insufficiency fracture detection on lumbar spine MRI.

METHODS

This retrospective analysis included 1000 vertebral bodies in 200 patients (age 75.2 ± 9.8 years) who underwent lumbar spine MRI at multiple institutions. 160/200 patients had ≥ one vertebral body insufficiency fracture, 40/200 had no fracture. The performance of the DCNN and that of two fellowship-trained musculoskeletal radiologists in vertebral body measurements (anterior/posterior height, extent of endplate concavity, vertebral angle) and evaluation for insufficiency fractures were compared. Statistics included (a) interobserver reliability metrics using intraclass correlation coefficient (ICC), kappa statistics, and Bland-Altman analysis, and (b) diagnostic performance metrics (sensitivity, specificity, accuracy). A statistically significant difference was accepted if the 95% confidence intervals did not overlap.

RESULTS

The inter-reader agreement between radiologists and the DCNN was excellent for vertebral body measurements, with ICC values of > 0.94 for anterior and posterior vertebral height and vertebral angle, and good to excellent for superior and inferior endplate concavity with ICC values of 0.79-0.85. The performance of the DCNN in fracture detection yielded a sensitivity of 0.941 (0.903-0.968), specificity of 0.969 (0.954-0.980), and accuracy of 0.962 (0.948-0.973). The diagnostic performance of the DCNN was independent of the radiological institution (accuracy 0.964 vs. 0.960), type of MRI scanner (accuracy 0.957 vs. 0.964), and magnetic field strength (accuracy 0.966 vs. 0.957).

CONCLUSIONS

A DCNN can achieve high diagnostic performance in vertebral body measurements and insufficiency fracture detection on heterogeneous lumbar spine MRI.

KEY POINTS

• A DCNN has the potential for high diagnostic performance in measuring vertebral bodies and detecting insufficiency fractures of the lumbar spine.

Localization and Edge-Based Segmentation of Lumbar Spine Vertebrae to Identify the Deformities Using Deep Learning Models

The lumbar spine plays a very important role in our load transfer and mobility. Vertebrae localization and segmentation are useful in detecting spinal deformities and fractures. Understanding of automated medical imagery is of main importance to help doctors in handling the time-consuming manual or semi-manual diagnosis. Our paper presents the methods that will help clinicians to grade the severity of the disease with confidence, as the current manual diagnosis by different doctors has dissimilarity and variations in the analysis of diseases. In this paper we discuss the lumbar spine localization and segmentation which help for the analysis of lumbar spine deformities. The lumber spine is localized using YOLOv5 which is the fifth variant of the YOLO family. It is the fastest and the lightest object detector. Mean average precision (mAP) of 0.975 is achieved by YOLOv5. To diagnose the lumbar lordosis, we correlated the angles with region area that is computed from the YOLOv5 centroids and obtained 74.5% accuracy. Cropped images from YOLOv5 bounding boxes are passed through HED U-Net, which is a combination of segmentation and edge detection frameworks, to obtain the segmented vertebrae and its edges. Lumbar lordortic angles (LLAs) and lumbosacral angles (LSAs) are found after detecting the corners of vertebrae using a Harris corner detector with very small mean errors of 0.29° and 0.38°, respectively. This paper compares the different object detectors used to localize the vertebrae, the results of two methods used to diagnose the lumbar deformity, and the results with other researchers.