Vertebral Body Compression Fractures and Bone Density: Automated Detection and Classification on CT Images

Artificial intelligence in musculoskeletal imaging: realistic clinical applications in the next decade

This article will provide a perspective review of the most extensively investigated deep learning (DL) applications for musculoskeletal disease detection that have the best potential to translate into routine clinical practice over the next decade. Deep learning methods for detecting fractures, estimating pediatric bone age, calculating bone measurements such as lower extremity alignment and Cobb angle, and grading osteoarthritis on radiographs have been shown to have high diagnostic performance with many of these applications now commercially available for use in clinical practice. Many studies have also documented the feasibility of using DL methods for detecting joint pathology and characterizing bone tumors on magnetic resonance imaging (MRI). However, musculoskeletal disease detection on MRI is difficult as it requires multi-task, multi-class detection of complex abnormalities on multiple image slices with different tissue contrasts. The generalizability of DL methods for musculoskeletal disease detection on MRI is also challenging due to fluctuations in image quality caused by the wide variety of scanners and pulse sequences used in routine MRI protocols. The diagnostic performance of current DL methods for musculoskeletal disease detection must be further evaluated in well-designed prospective studies using large image datasets acquired at different institutions with different imaging parameters and imaging hardware before they can be fully implemented in clinical practice. Future studies must also investigate the true clinical benefits of current DL methods and determine whether they could enhance quality, reduce error rates, improve workflow, and decrease radiologist fatigue and burnout with all of this weighed against the costs.

The association between computed tomography-based osteosarcopenia and osteoporotic vertebral fractures: a longitudinal study

PURPOSE

Osteoporosis and sarcopenia usually coexist in older population. The concept of osteosarcopenia has been proposed in recent years. However, studies on the relationship between osteosarcopenia and the risk of fracture are rare, and the association is unclear at present. This study aimed to investigate the association between osteosarcopenia evaluated based on chest computed tomography (CT) and osteoporotic vertebral fracture (OVF).

METHODS

This study recruited 7906 individuals aged 50 years and older who did not have OVFs and underwent chest CT for physical examination between July 2016 and September 2019. Subjects were followed up annually until June 2023. Osteosarcopenia was defined by a low muscle area of the erector spinae (< 25.4 cm2) and the bone attenuation (Hounsfield unit, HU < 135). Genant's grades were used to define OVFs. Control subjects were selected by Propensity Score Matching at a ratio 20:1. Cox proportional hazards models were used to assess the associations between osteosarcopenia and OVFs.

RESULTS

Of the 7906 participants included, 95 had a new OVF within a median follow-up of 3 years. A total of 1900 control subjects were matched. Individuals in the osteosarcopenia group had a higher prevalence of spinal fractures than those in normal group (16.4% vs. 0.4%, P < 0.001). Osteosarcopenia was independently associated with OVF (adjusted hazard ratio (aHR): 12.67, 95% confidence interval (CI) 3.79-42.40) and severe OVF (aHR = 14.07, 95% CI 1.84-107.66). Similar trends were observed in males, females and those subjects aged older than 60 years. Osteosarcopenia had good predictive efficacy for OVF (area under the curve = 0.836). A nomogram was also developed for clinical application.

CONCLUSION

Osteosarcopenia assessed based on chest CT was associated with OVF, and osteosarcopenia has good performance for vertebral fracture prediction.

Weakly supervised deep learning for diagnosis of multiple vertebral compression fractures in CT

OBJECTIVE

This study aims to develop a weakly supervised deep learning (DL) model for vertebral-level vertebral compression fracture (VCF) classification using image-level labelled data.

METHODS

The training set included 815 patients with normal (n = 507, 62%) or VCFs (n = 308, 38%). Our proposed model was trained on image-level labelled data for vertebral-level classification. Another supervised DL model was trained with vertebral-level labelled data to compare the performance of the proposed model.

RESULTS

The test set included 227 patients with normal (n = 117, 52%) or VCFs (n = 110, 48%). For a fair comparison of the two models, we compared sensitivities with the same specificities of the proposed model and the vertebral-level supervised model. The specificity for overall L1-L5 performance was 0.981. The proposed model may outperform the vertebral-level supervised model with sensitivities of 0.770 vs 0.705 (p = 0.080), respectively. For vertebral-level analysis, the specificities for each L1-L5 were 0.974, 0.973, 0.970, 0.991, and 0.995, respectively. The proposed model yielded the same or better sensitivity than the vertebral-level supervised model in L1 (0.750 vs 0.694, p = 0.480), L3 (0.793 vs 0.586, p < 0.05), L4 (0.833 vs 0.667, p = 0.480), and L5 (0.600 vs 0.600, p = 1.000), respectively. The proposed model showed lower sensitivity than the vertebral-level supervised model for L2, but there was no significant difference (0.775 vs 0.825, p = 0.617).

CONCLUSIONS

The proposed model may have a comparable or better performance than the supervised model in vertebral-level VCF classification.

CLINICAL RELEVANCE STATEMENT

Vertebral-level vertebral compression fracture classification aids in devising patient-specific treatment plans by identifying the precise vertebrae affected by compression fractures.

KEY POINTS

• Our proposed weakly supervised method may have comparable or better performance than the supervised method for vertebral-level vertebral compression fracture classification. • The weakly supervised model could have classified cases with multiple vertebral compression fractures at the vertebral-level, even if the model was trained with image-level labels. • Our proposed method could help reduce radiologists' labour because it enables vertebral-level classification from image-level labels.

Chest CT-based automated vertebral fracture assessment using artificial intelligence and morphologic features

BACKGROUND

Spinal degeneration and vertebral compression fractures are common among the elderly that adversely affect their mobility, quality of life, lung function, and mortality. Assessment of vertebral fractures in chronic obstructive pulmonary disease (COPD) is important due to the high prevalence of osteoporosis and associated vertebral fractures in COPD.

PURPOSE

We present new automated methods for (1) segmentation and labelling of individual vertebrae in chest computed tomography (CT) images using deep learning (DL), multi-parametric freeze-and-grow (FG) algorithm, and separation of apparently fused vertebrae using intensity autocorrelation and (2) vertebral deformity fracture detection using computed vertebral height features and parametric computational modelling of an established protocol outlined for trained human experts.

METHODS

A chest CT-based automated method was developed for quantitative deformity fracture assessment following the protocol by Genant et al. The computational method was accomplished in the following steps: (1) computation of a voxel-level vertebral body likelihood map from chest CT using a trained DL network; (2) delineation and labelling of individual vertebrae on the likelihood map using an iterative multi-parametric FG algorithm; (3) separation of apparently fused vertebrae in CT using intensity autocorrelation; (4) computation of vertebral heights using contour analysis on the central anterior-posterior (AP) plane of a vertebral body; (5) assessment of vertebral fracture status using ratio functions of vertebral heights and optimized thresholds. The method was applied to inspiratory or total lung capacity (TLC) chest scans from the multi-site Genetic Epidemiology of COPD (COPDGene) (ClinicalTrials.gov: NCT00608764) study, and the performance was examined (n = 3231). One hundred and twenty scans randomly selected from this dataset were partitioned into training (n = 80) and validation (n = 40) datasets for the DL-based vertebral body classifier. Also, generalizability of the method to low dose CT imaging (n = 236) was evaluated.

RESULTS

The vertebral segmentation module achieved a Dice score of .984 as compared to manual outlining results as reference (n = 100); the segmentation performance was consistent across images with the minimum and maximum of Dice scores among images being .980 and .989, respectively. The vertebral labelling module achieved 100% accuracy (n = 100). For low dose CT, the segmentation module produced image-level minimum and maximum Dice scores of .995 and .999, respectively, as compared to standard dose CT as the reference; vertebral labelling at low dose CT was fully consistent with standard dose CT (n = 236). The fracture assessment method achieved overall accuracy, sensitivity, and specificity of 98.3%, 94.8%, and 98.5%, respectively, for 40,050 vertebrae from 3231 COPDGene participants. For generalizability experiments, fracture assessment from low dose CT was consistent with the reference standard dose CT results across all participants.

CONCLUSIONS

Our CT-based automated method for vertebral fracture assessment is accurate, and it offers a feasible alternative to manual expert reading, especially for large population-based studies, where automation is important for high efficiency. Generalizability of the method to low dose CT imaging further extends the scope of application of the method, particularly since the usage of low dose CT imaging in large population-based studies has increased to reduce cumulative radiation exposure.

Development and validation of a radiomics-based model for predicting osteoporosis in patients with lumbar compression fractures

BACKGROUND

Osteoporosis, a metabolic bone disorder, markedly elevates fracture risks, with vertebral compression fractures being predominant. Antiosteoporotic treatments for patients with osteoporotic vertebral compression fractures (OVCF) lessen both the occurrence of subsequent fractures and associated pain. Thus, diagnosing osteoporosis in OVCF patients is vital.

PURPOSE

The aim of this study was to develop a predictive radiographic model using T1 sequence MRI images to accurately determine whether patients with lumbar spine compression fractures also have osteoporosis.

STUDY DESIGN

Retrospective cohort study.

PATIENT SAMPLE

Patients over 45 years of age diagnosed with a fresh lumbar compression fracture.

OUTCOME MEASURES

Diagnostic accuracy of the model (area under the ROC curve).

METHODS

The study retrospectively collected clinical and imaging data (MRI and DEXA) from hospitalized lumbar compression fracture patients (L1-L4) aged 45 years or older between January 2021 and June 2023. Using the pyradiomics package in Python, features from the lumbar compression fracture vertebral region of interest (ROI) were extracted. Downscaling of the extracted features was performed using the Mann-Whitney U test and the least absolute shrinkage selection operator (LASSO) algorithm. Subsequently, six machine learning models (Naive Bayes, Support Vector Machine [SVM], Decision Tree, Random Forest, Extreme Gradient Boosting [XGBoost], and Light Gradient Boosting Machine [LightGBM]) were employed to train and validate these features in predicting osteoporosis comorbidity in OVCF patients.

RESULTS

A total of 128 participants, 79 in the osteoporotic group and 49 in the non-osteoporotic group, met the study's inclusion and exclusion criteria. From the T1 sequence MRI images, 1906 imaging features were extracted in both groups. Utilizing the Mann-Whitney U test, 365 radiologic features were selected out of the initial 1,906. Ultimately, the lasso algorithm identified 14 significant radiological features. These features, incorporated into six conventional machine learning algorithms, demonstrated successful prediction of osteoporosis in the validation set. The NaiveBayes model yielded an area under the receiver operating characteristic curve (AUC) of 0.84, sensitivity of 0.87, specificity of 0.70, and accuracy of 0.81.

CONCLUSIONS

A NaiveBayes machine learning algorithm can predict osteoporosis in OVCF patients using t1-sequence MRI images of lumbar compression fractures. This approach aims to obviate the necessity for further osteoporosis assessments, diminish patient exposure to radiation, and bolster the clinical care of patients with OVCF.

The Evolution of Risk Assessment in Spine Surgery: A Narrative Review

BACKGROUND

Risk assessment is critically important in elective and high-risk interventions, particularly spine surgery. This narrative review describes the evolution of risk assessment from the earliest instruments focused on general surgical risk stratification, to more accurate and spine-specific risk calculators that quantified risk, to the current era of big data.

METHODS

The PubMed and SCOPUS databases were queried on October 11, 2023 using search terms to identify risk assessment tools (RATs) in spine surgery. A total of 108 manuscripts were included after screening with full-text review using the following inclusion criteria: 1) study population of adult spine surgical patients, 2) studies describing validation and subsequent performance of preoperative RATs, and 3) studies published in English.

RESULTS

Early RATs provided stratified patients into broad categories and allowed for improved communication between physicians. Subsequent risk calculators attempted to quantify risk by estimating general outcomes such as mortality, but then evolved to estimate spine-specific surgical complications. The integration of novel concepts such as invasiveness, frailty, genetic biomarkers, and sarcopenia led to the development of more sophisticated predictive models that estimate the risk of spine-specific complications and long-term outcomes.

CONCLUSIONS

RATs have undergone a transformative shift from generalized risk stratification to quantitative predictive models. The next generation of tools will likely involve integration of radiographic and genetic biomarkers, machine learning, and artificial intelligence to improve the accuracy of these models and better inform patients, surgeons, and payers.

Opportunistic Screening for Acute Vertebral Fractures on a Routine Abdominal or Chest Computed Tomography Scans Using an Automated Deep Learning Model

OBJECTIVES

To develop an opportunistic screening model based on a deep learning algorithm to detect recent vertebral fractures in abdominal or chest CTs.

MATERIALS AND METHODS

A total of 1309 coronal reformatted images (504 with a recent fracture from 119 patients, and 805 without fracture from 115 patients), from torso CTs, performed from September 2018 to April 2022, on patients who also had a spine MRI within two months, were included. Two readers participated in image selection and manually labeled the fractured segment on each selected image with Neuro-T (version 2.3.3; Neurocle Inc.) software. We split the images randomly into the training and internal test set (labeled: unlabeled = 480:700) and the secondary interval validation set (24:105). For the observer study, three radiologists reviewed the CT images in the external test set with and without deep learning assistance and scored the likelihood of an acute fracture in each image independently.

RESULTS

For the training and internal test sets, the AI achieved a 99.86% test accuracy, 91.22% precision, and 89.18% F1 score for detection of recent fracture. Then, in the secondary internal validation set, it achieved 99.90%, 74.93%, and 78.30%, respectively. In the observer study, with the assistance of the deep learning algorithm, a significant improvement was observed in the radiology resident's accuracy, from 92.79% to 98.2% (p = 0.04).

CONCLUSION

The model showed a high level of accuracy in the test set and also the internal validation set. If this algorithm is applied opportunistically to daily torso CT evaluation, it will be helpful for the early detection of fractures that require treatment.

Differentiation of Native Vertebral Osteomyelitis: A Comprehensive Review of Imaging Techniques and Future Applications

Native vertebral osteomyelitis, also termed spondylodiscitis, is an antibiotic-resistant disease that requires long-term treatment. Without proper treatment, NVO can lead to severe nerve damage or even death. Therefore, it is important to accurately diagnose the cause of NVO, especially in spontaneous cases. Infectious NVO is characterized by the involvement of 2 adjacent vertebrae and intervertebral discs, and common infectious agents include Staphylococcus aureus, Mycobacterium tuberculosis, Brucella abortus, and fungi. Clinical symptoms are generally nonspecific, and early diagnosis and appropriate treatment can prevent irreversible sequelae. Advances in pathologic histologic imaging have led physicians to look more forward to being able to differentiate between tuberculous and septic spinal discitis. Therefore, research in identifying and differentiating the imaging features of these 4 common NVOs is essential. Due to the diagnostic difficulties, clinical and radiologic diagnosis is the mainstay of provisional diagnosis. With the advent of the big data era and the emergence of convolutional neural network algorithms for deep learning, the application of artificial intelligence (AI) technology in orthopedic imaging diagnosis has gradually increased. AI can assist physicians in imaging review, effectively reduce the workload of physicians, and improve diagnostic accuracy. Therefore, it is necessary to present the latest clinical research on NVO and the outlook for future AI applications.

Practical Applications of Artificial Intelligence in Spine Imaging: A Review

Artificial intelligence (AI), a transformative technology with unprecedented potential in medical imaging, can be applied to various spinal pathologies. AI-based approaches may improve imaging efficiency, diagnostic accuracy, and interpretation, which is essential for positive patient outcomes. This review explores AI algorithms, techniques, and applications in spine imaging, highlighting diagnostic impact and challenges with future directions for integrating AI into spine imaging workflow.

Artificial intelligence diagnostic model for multi-site fracture X-ray images of extremities based on deep convolutional neural networks

Background

The rapid and accurate diagnosis of fractures is crucial for timely treatment of trauma patients. Deep learning, one of the most widely used forms of artificial intelligence (AI), is now commonly employed in medical imaging for fracture detection. This study aimed to construct a deep learning model using big data to recognize multiple-fracture X-ray images of extremity bones.

Methods

Radiographic imaging data of extremities were retrospectively collected from five hospitals between January 2017 and September 2020. The total number of people finally included was 25,635 and the total number of images included was 26,098. After labeling the lesions, the randomized method used 90% of the data as the training set to develop the fracture detection model, and the remaining 10% was used as the validation set to verify the model. The faster region convolutional neural networks (R-CNN) algorithm was adopted to construct diagnostic models for detection. The Dice coefficient was used to evaluate the image segmentation accuracy. The performances of detection models were evaluated with sensitivity, specificity, and area under the receiver operating characteristic curve (AUC).

Results

The free-response receiver operating characteristic (FROC) curve value was 0.886 and 0.843 for the detection of single and multiple fractures, respectively. Additionally, the effective identification AUC for all parts was higher than 0.920. Notably, the AUC for wrist fractures reached 0.952. The average accuracy in detecting bone fracture regions in the extremities was 0.865. When analyzing single and multiple lesions at the patient level, the sensitivity was 0.957 for patients with multiple lesions and 0.852 for those with single lesions. In the segmentation task, the training set (the data set used by the machine learning model to train and learn) and the validation set (the data set used to evaluate the performance of the model) reached 0.996 and 0.975, respectively.

Conclusions

The faster R-CNN training algorithm exhibits excellent performance in simultaneously identifying fractures in the hands, feet, wrists, ankles, radius and ulna, and tibia and fibula on X-ray images. It demonstrates high accuracy, low false-negative rates, and controllable false-positive rates. It can serve as a valuable screening tool.

Auto-CA: Automated Cobb Angle Measurement Based on Vertebrae Detection for Assessment of Spinal Curvature Deformity

An accurate identification and localization of vertebrae in X-ray images can assist doctors in measuring Cobb angles for treating patients with adolescent idiopathic scoliosis. It is useful for clinical decision support systems for diagnosis, surgery planning, and spinal health analysis. Currently, publicly available annotated datasets on spinal vertebrae are small, making deep-learning-based detection methods that are highly data-dependent less accurate. In this article, we propose an algorithm based on convolutional neural networks that can be trained to detect vertebrae from a small set of images. This method can display critical information on a patient's spine, display vertebrae and their labels on the thoracic and lumbar, calculate the Cobb angle, and evaluate the severity of spinal deformities. The proposed achieved an average accuracy of 0.958 and 0.962 for classifying spinal deformities (i.e., C-shaped, S-shaped type 1, and S-shaped type 2) and severity of Cobb angle (i.e., normal, mild, moderate, and severe), respectively. The Cobb angle measurement had a median difference of less than 5° from the ground-truth with SMAPE of 5.27% and an error on landmark detection of 19.73. In addition, Lenke classification is used to analyze spinal deformities as types A, B, and C, which have an average accuracy of 0.924. Physicians can use the proposed system in clinical practice by providing X-ray images via the user interface.

Development of a Machine Learning Algorithm to Correlate Lumbar Disc Height on X-rays with Disc Bulging or Herniation

Lumbar disc bulging or herniation (LDBH) is one of the major causes of spinal stenosis and related nerve compression, and its severity is the major determinant for spine surgery. MRI of the spine is the most important diagnostic tool for evaluating the need for surgical intervention in patients with LDBH. However, MRI utilization is limited by its low accessibility. Spinal X-rays can rapidly provide information on the bony structure of the patient. Our study aimed to identify the factors associated with LDBH, including disc height, and establish a clinical diagnostic tool to support its diagnosis based on lumbar X-ray findings. In this study, a total of 458 patients were used for analysis and 13 clinical and imaging variables were collected. Five machine-learning (ML) methods, including LASSO regression, MARS, decision tree, random forest, and extreme gradient boosting, were applied and integrated to identify important variables for predicting LDBH from lumbar spine X-rays. The results showed L4-5 posterior disc height, age, and L1-2 anterior disc height to be the top predictors, and a decision tree algorithm was constructed to support clinical decision-making. Our study highlights the potential of ML-based decision tools for surgeons and emphasizes the importance of L1-2 disc height in relation to LDBH. Future research will expand on these findings to develop a more comprehensive decision-supporting model.

Artificial intelligence in fracture detection with different image modalities and data types: A systematic review and meta-analysis

Artificial Intelligence (AI), encompassing Machine Learning and Deep Learning, has increasingly been applied to fracture detection using diverse imaging modalities and data types. This systematic review and meta-analysis aimed to assess the efficacy of AI in detecting fractures through various imaging modalities and data types (image, tabular, or both) and to synthesize the existing evidence related to AI-based fracture detection. Peer-reviewed studies developing and validating AI for fracture detection were identified through searches in multiple electronic databases without time limitations. A hierarchical meta-analysis model was used to calculate pooled sensitivity and specificity. A diagnostic accuracy quality assessment was performed to evaluate bias and applicability. Of the 66 eligible studies, 54 identified fractures using imaging-related data, nine using tabular data, and three using both. Vertebral fractures were the most common outcome (n = 20), followed by hip fractures (n = 18). Hip fractures exhibited the highest pooled sensitivity (92%; 95% CI: 87-96, p< 0.01) and specificity (90%; 95% CI: 85-93, p< 0.01). Pooled sensitivity and specificity using image data (92%; 95% CI: 90-94, p< 0.01; and 91%; 95% CI: 88-93, p < 0.01) were higher than those using tabular data (81%; 95% CI: 77-85, p< 0.01; and 83%; 95% CI: 76-88, p < 0.01), respectively. Radiographs demonstrated the highest pooled sensitivity (94%; 95% CI: 90-96, p < 0.01) and specificity (92%; 95% CI: 89-94, p< 0.01). Patient selection and reference standards were major concerns in assessing diagnostic accuracy for bias and applicability. AI displays high diagnostic accuracy for various fracture outcomes, indicating potential utility in healthcare systems for fracture diagnosis. However, enhanced transparency in reporting and adherence to standardized guidelines are necessary to improve the clinical applicability of AI. Review Registration: PROSPERO (CRD42021240359).

Machine Learning in Neurosurgery: Toward Complex Inputs, Actionable Predictions, and Generalizable Translations

Machine learning can predict neurosurgical diagnosis and outcomes, power imaging analysis, and perform robotic navigation and tumor labeling. State-of-the-art models can reconstruct and generate images, predict surgical events from video, and assist in intraoperative decision-making. In this review, we will detail the neurosurgical applications of machine learning, ranging from simple to advanced models, and their potential to transform patient care. As machine learning techniques, outputs, and methods become increasingly complex, their performance is often more impactful yet increasingly difficult to evaluate. We aim to introduce these advancements to the neurosurgical audience while suggesting major potential roadblocks to their safe and effective translation. Unlike the previous generation of machine learning in neurosurgery, the safe translation of recent advancements will be contingent on neurosurgeons' involvement in model development and validation.

External validation of a convolutional neural network algorithm for opportunistically detecting vertebral fractures in routine CT scans

The Convolutional Neural Network algorithm achieved a sensitivity of 94% and specificity of 93% in identifying scans with vertebral fractures (VFs). The external validation results suggest that the algorithm provides an opportunity to aid radiologists with the early identification of VFs in routine CT scans of abdomen and chest.

PURPOSE

To evaluate the performance of a previously trained Convolutional Neural Network (CNN) model to automatically detect vertebral fractures (VFs) in CT scans in an external validation cohort.

METHODS

Two Chinese studies and clinical data were used to retrospectively select CT scans of the chest, abdomen and thoracolumbar spine in men and women aged ≥50 years. The CT scans were assessed using the semiquantitative (SQ) Genant classification for prevalent VFs in a process blinded to clinical information. The performance of the CNN model was evaluated against reference standard readings by the area under the receiver operating characteristics curve (AUROC), accuracy, Cohen's kappa, sensitivity, and specificity.

RESULTS

A total of 4,810 subjects were included, with a median age of 62 years (IQR 56-67), of which 2,654 (55.2%) were females. The scans were acquired between January 2013 and January 2019 on 16 different CT scanners from three different manufacturers. 2,773 (57.7%) were abdominal CTs. A total of 628 scans (13.1%) had ≥1 VF (grade 2-3), representing 899 fractured vertebrae out of a total of 48,584 (1.9%) visualized vertebral bodies. The CNN's performance in identifying scans with ≥1 moderate or severe fractures achieved an AUROC of 0.94 (95% CI: 0.93-0.95), accuracy of 93% (95% CI: 93%-94%), kappa of 0.75 (95% CI: 0.72-0.77), a sensitivity of 94% (95% CI: 92-96%) and a specificity of 93% (95% CI: 93-94%).

CONCLUSION

The algorithm demonstrated excellent performance in the identification of vertebral fractures in a cohort of chest and abdominal CT scans of Chinese patients ≥50 years.

Artificial Intelligence-Assisted Detection of Osteoporotic Vertebral Fractures on Lateral Chest Radiographs in Post-Menopausal Women

Osteoporotic vertebral fractures (OVFs) are often not reported by radiologists on routine chest radiographs. This study aims to investigate the clinical value of a newly developed artificial intelligence (AI) tool, Ofeye 1.0, for automated detection of OVFs on lateral chest radiographs in post-menopausal women (>60 years) who were referred to undergo chest x-rays for other reasons. A total of 510 de-identified lateral chest radiographs from three clinical sites were retrieved and analysed using the Ofeye 1.0 tool. These images were then reviewed by a consultant radiologist with findings serving as the reference standard for determining the diagnostic performance of the AI tool for the detection of OVFs. Of all the original radiologist reports, missed OVFs were found in 28.8% of images but were detected using the AI tool. The AI tool demonstrated high specificity of 92.8% (95% CI: 89.6, 95.2%), moderate accuracy of 80.3% (95% CI: 76.3, 80.4%), positive predictive value (PPV) of 73.7% (95% CI: 65.2, 80.8%), and negative predictive value (NPV) of 81.5% (95% CI: 79, 83.8%), but low sensitivity of 49% (95% CI: 40.7, 57.3%). The AI tool showed improved sensitivity compared with the original radiologist reports, which was 20.8% (95% CI: 14.5, 28.4). The new AI tool can be used as a complementary tool in routine diagnostic reports for the reduction in missed OVFs in elderly women.

Towards Improved Identification of Vertebral Fractures in Routine Computed Tomography (CT) Scans: Development and External Validation of a Machine Learning Algorithm

Vertebral fractures (VFs) are the hallmark of osteoporosis, being one of the most frequent types of fragility fracture and an early sign of the disease. They are associated with significant morbidity and mortality. VFs are incidentally found in one out of five imaging studies, however, more than half of the VFs are not identified nor reported in patient computed tomography (CT) scans. Our study aimed to develop a machine learning algorithm to identify VFs in abdominal/chest CT scans and evaluate its performance. We acquired two independent data sets of routine abdominal/chest CT scans of patients aged 50 years or older: a training set of 1011 scans from a non-interventional, prospective proof-of-concept study at the Universitair Ziekenhuis (UZ) Brussel and a validation set of 2000 subjects from an observational cohort study at the Hospital of Holbaek. Both data sets were externally reevaluated to identify reference standard VF readings using the Genant semiquantitative (SQ) grading. Four independent models have been trained in a cross-validation experiment using the training set and an ensemble of four models has been applied to the external validation set. The validation set contained 15.3% scans with one or more VF (SQ2-3), whereas 663 of 24,930 evaluable vertebrae (2.7%) were fractured (SQ2-3) as per reference standard readings. Comparison of the ensemble model with the reference standard readings in identifying subjects with one or more moderate or severe VF resulted in an area under the receiver operating characteristic curve (AUROC) of 0.88 (95% confidence interval [CI], 0.85-0.90), accuracy of 0.92 (95% CI, 0.91-0.93), kappa of 0.72 (95% CI, 0.67-0.76), sensitivity of 0.81 (95% CI, 0.76-0.85), and specificity of 0.95 (95% CI, 0.93-0.96). We demonstrated that a machine learning algorithm trained for VF detection achieved strong performance on an external validation set. It has the potential to support healthcare professionals with the early identification of VFs and prevention of future fragility fractures. © 2023 UCB S.A. and The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

DRCM: a disentangled representation network based on coordinate and multimodal attention for medical image fusion

Recent studies on medical image fusion based on deep learning have made remarkable progress, but the common and exclusive features of different modalities, especially their subsequent feature enhancement, are ignored. Since medical images of different modalities have unique information, special learning of exclusive features should be designed to express the unique information of different modalities so as to obtain a medical fusion image with more information and details. Therefore, we propose an attention mechanism-based disentangled representation network for medical image fusion, which designs coordinate attention and multimodal attention to extract and strengthen common and exclusive features. First, the common and exclusive features of each modality were obtained by the cross mutual information and adversarial objective methods, respectively. Then, coordinate attention is focused on the enhancement of the common and exclusive features of different modalities, and the exclusive features are weighted by multimodal attention. Finally, these two kinds of features are fused. The effectiveness of the three innovation modules is verified by ablation experiments. Furthermore, eight comparison methods are selected for qualitative analysis, and four metrics are used for quantitative comparison. The values of the four metrics demonstrate the effect of the DRCM. Furthermore, the DRCM achieved better results on SCD, Nabf, and MS-SSIM metrics, which indicates that the DRCM achieved the goal of further improving the visual quality of the fused image with more information from source images and less noise. Through the comprehensive comparison and analysis of the experimental results, it was found that the DRCM outperforms the comparison method.

Efficacy of two opportunistic methods for screening osteoporosis in lumbar spine surgery patients

PURPOSE

Hounsfield unit (HU) measurements and vertebral bone quality (VBQ) scores are opportunistic screening methods for evaluating bone quality. Since studies comparing the efficacies of the two methods are rare, this retrospective study aimed to examine the efficacy of VBQ scores compared with that of HU measurements for diagnosing osteoporosis in lumbar spine surgery patients.

METHODS

We selected patients who had undergone spinal surgery between January 2020 and May 2022 from our database. The VBQ scores based on magnetic resonance imaging (MRI) and HU measurements based on computed tomography (CT) were calculated. Correlation analysis of the dual-energy X-ray absorptiometry (DEXA) T score and study parameters was performed. The Delong test and decision curve analysis (DCA) were used to compare the efficacies of the two methods.

RESULTS

We included 118 consecutive patients who underwent selective spinal surgery. The VBQ score and HU measurement were significantly correlated with the DEXA T score. Based on the Delong test, HU measurement predicted osteoporosis more effectively than the VBQ score did. The DCA revealed that the VBQ score performed better than the HU measurement did.

CONCLUSIONS

The calculation of VBQ scores is a novel opportunistic screening method for diagnosing osteoporosis; however, CT-based HU measurements outperform MRI-based VBQ scores. HU measurements can be used as a screening method when pre-operative CT scans are available.

Lumbar Spinal Canal Segmentation in Cases with Lumbar Stenosis Using Deep-U-Net Ensembles

BACKGROUND

Narrowing of the lumbar spinal canal, or lumbar stenosis (LS), may cause debilitating radicular pain or muscle weakness. It is the most frequent indication for spinal surgery in the elderly population. Modern diagnosis relies on magnetic resonance imaging and its inherently subjective interpretation. Diagnostic rigor, accuracy, and speed may be improved by automation. In this work, we aimed to determine whether a deep-U-Net ensemble trained to segment spinal canals on a heterogeneous mix of clinical data is comparable to radiologists' segmentation of these canals in patients with LS.

METHODS

The deep U-nets were trained on spinal canals segmented by physicians on 100 axial T2 lumbar magnetic resonance imaging selected randomly from our institutional database. Test data included a total of 279 elderly patients with LS that were separate from the training set.

RESULTS

Machine-generated segmentations (MA) were qualitatively similar to expert-generated segmentations (ME1, ME2). Machine- and expert-generated segmentations were quantitatively similar, as evidenced by Dice scores (MA vs. ME1: 0.88 ± 0.04, MA vs. ME2: 0.89 ± 0.04), the Hausdorff distance (MA vs. ME1: 11.7 mm ± 13.8, MA vs. ME2: 13.1 mm ± 16.3), and average surface distance (MAvs. ME1: 0.18 mm ± 0.13, MA vs. ME2 0.18 mm ± 0.16) metrics. These metrics are comparable to inter-rater variation (ME1 vs. ME2 Dice scores: 0.94 ± 0.02, the Hausdorff distances: 9.3 mm ± 15.6, average surface distances: 0.08 mm ± 0.09).

CONCLUSION

We conclude that machine learning algorithms can segment lumbar spinal canals in LS patients, and automatic delineations are both qualitatively and quantitatively comparable to expert-generated segmentations.

Machine Learning Applications in Spine Surgery

This literature review sought to identify and evaluate the current applications of artificial intelligence (AI)/machine learning (ML) in spine surgery that can effectively guide clinical decision-making and surgical planning. By using specific keywords to maximize search sensitivity, a thorough literature research was conducted in several online databases: Scopus, PubMed, and Google Scholar, and the findings were filtered according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 46 studies met the requirements and were included in this review. According to this study, AI/ML models were sufficiently accurate with a mean overall value of 74.9%, and performed best at preoperative patient selection, cost prediction, and length of stay. Performance was also good at predicting functional outcomes and postoperative mortality. Regression analysis was the most frequently utilized application whereas deep learning/artificial neural networks had the highest sensitivity score (81.5%). Despite the relatively brief history of engagement with AI/ML, as evidenced by the fact that 77.5% of studies were published after 2018, the outcomes have been promising. In light of the Big Data era, the increasing prevalence of National Registries, and the wide-ranging applications of AI, such as exemplified by ChatGPT (OpenAI, San Francisco, California), it is highly likely that the field of spine surgery will gradually adopt and integrate AI/ML into its clinical practices. Consequently, it is of great significance for spine surgeons to acquaint themselves with the fundamental principles of AI/ML, as these technologies hold the potential for substantial improvements in overall patient care.

Using Artificial Intelligence to Diagnose Osteoporotic Vertebral Fractures on Plain Radiographs

Osteoporotic vertebral fracture (OVF) is a risk factor for morbidity and mortality in elderly population, and accurate diagnosis is important for improving treatment outcomes. OVF diagnosis suffers from high misdiagnosis and underdiagnosis rates, as well as high workload. Deep learning methods applied to plain radiographs, a simple, fast, and inexpensive examination, might solve this problem. We developed and validated a deep-learning-based vertebral fracture diagnostic system using area loss ratio, which assisted a multitasking network to perform skeletal position detection and segmentation and identify and grade vertebral fractures. As the training set and internal validation set, we used 11,397 plain radiographs from six community centers in Shanghai. For the external validation set, 1276 participants were recruited from the outpatient clinic of the Shanghai Sixth People's Hospital (1276 plain radiographs). Radiologists performed all X-ray images and used the Genant semiquantitative tool for fracture diagnosis and grading as the ground truth data. Accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were used to evaluate diagnostic performance. The AI_OVF_SH system demonstrated high accuracy and computational speed in skeletal position detection and segmentation. In the internal validation set, the accuracy, sensitivity, and specificity with the AI_OVF_SH model were 97.41%, 84.08%, and 97.25%, respectively, for all fractures. The sensitivity and specificity for moderate fractures were 88.55% and 99.74%, respectively, and for severe fractures, they were 92.30% and 99.92%. In the external validation set, the accuracy, sensitivity, and specificity for all fractures were 96.85%, 83.35%, and 94.70%, respectively. For moderate fractures, the sensitivity and specificity were 85.61% and 99.85%, respectively, and 93.46% and 99.92% for severe fractures. Therefore, the AI_OVF_SH system is an efficient tool to assist radiologists and clinicians to improve the diagnosing of vertebral fractures. © 2023 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

Artificial Intelligence in Neuroradiology: A Review of Current Topics and Competition Challenges

There is an expanding body of literature that describes the application of deep learning and other machine learning and artificial intelligence methods with potential relevance to neuroradiology practice. In this article, we performed a literature review to identify recent developments on the topics of artificial intelligence in neuroradiology, with particular emphasis on large datasets and large-scale algorithm assessments, such as those used in imaging AI competition challenges. Numerous applications relevant to ischemic stroke, intracranial hemorrhage, brain tumors, demyelinating disease, and neurodegenerative/neurocognitive disorders were discussed. The potential applications of these methods to spinal fractures, scoliosis grading, head and neck oncology, and vascular imaging were also reviewed. The AI applications examined perform a variety of tasks, including localization, segmentation, longitudinal monitoring, diagnostic classification, and prognostication. While research on this topic is ongoing, several applications have been cleared for clinical use and have the potential to augment the accuracy or efficiency of neuroradiologists.

Opportunistic Computed Tomography Screening for Osteoporosis and Fracture

Osteoporosis is underdiagnosed and undertreated, leading to loss of treatment for the patient and high costs for the health care system. Routine thoracic and/or abdominal computed tomography (CT) performed for other indications can screen opportunistically for osteoporosis with no extra cost, time, or irradiation. Various methods can quantify fracture risk on opportunistic clinical CT: vertebral Hounsfield unit bone mineral density (BMD), usually of L1; BMD measurement with asynchronous or internal calibration; quantitative CT; bone texture assessment; and finite element analysis. Screening for osteoporosis and vertebral fractures on opportunistic CT is a promising approach, providing automated fracture risk scores by means of artificial intelligence, thus enabling earlier management.

Opportunistic CT Screening-Machine Learning Algorithm Identifies Majority of Vertebral Compression Fractures: A Cohort Study

Vertebral compression fractures (VCF) are common in patients older than 50 years but are often undiagnosed. Zebra Medical Imaging developed a VCF detection algorithm, with machine learning, to detect VCFs from CT images of the chest and/or abdomen/pelvis. In this study, we evaluated the diagnostic performance of the algorithm in identifying VCF. We conducted a blinded validation study to estimate the operating characteristics of the algorithm in identifying VCFs using previously completed CT scans from 1200 women and men aged 50 years and older at a tertiary-care center. Each scan was independently evaluated by two of three neuroradiologists to identify and grade VCF. Disagreements were resolved by a senior neuroradiologist. The algorithm evaluated the CT scans in a separate workstream. The VCF algorithm was not able to evaluate CT scans for 113 participants. Of the remaining 1087 study participants, 588 (54%) were women. Median age was 73 years (range 51-102 years; interquartile range 66-81). For the 1087 algorithm-evaluated participants, the sensitivity and specificity of the VCF algorithm in diagnosing any VCF were 0.66 (95% confidence interval [CI] 0.59-0.72) and 0.90 (95% CI 0.88-0.92), respectively, and for diagnosing moderate/severe VCF were 0.78 (95% CI 0.70-0.85) and 0.87 (95% CI 0.85-0.89), respectively. Implementing this VCF algorithm within radiology systems may help to identify patients at increased fracture risk and could support the diagnosis of osteoporosis and facilitate appropriate therapy. © 2023 Amgen, Inc. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.

Fatty Degeneration of the Autochthonous Muscles Is Significantly Associated with Incidental Non-Traumatic Vertebral Body Fractures of the Lower Thoracic Spine in Elderly Patients

PURPOSE

We investigated loco-regional degenerative changes' association with incidentally found non-traumatic vertebral body fractures of the lower thoracic and lumbar spine in older patients. Methods: The patient collective included patients in the age range of 50 to 90 years. Vertebral bodies from T7 to L5 were included. Vertebral body fractures were classified according to Genant. The following loco-regional osseous and extra-osseous degenerative changes were included: osteochondrosis, spondylarthritis, facet joint asymmetries, spondylolisthesis, scoliosis as well as fatty degeneration and asymmetry of the autochthonous back muscles. Patients with traumatic and tumor-related vertebral body fractures were excluded. Non-traumatic fractures of the lower thoracic and lumbar spine were evaluated separately. The Mann-Whitney U-test was used, and relative risks (RRs) were calculated for statistics. Pearson's correlations (Rs) were used to correlate grades of degenerative changes and fracture severities. Results: 105 patients were included. Fatty deposits in the autochthonous muscles of the lower thoracic and the lumbar spine were associated with non-traumatic vertebral body fractures in the lower thoracic spine (p = 0.005, RR = 4.92). In contrast, muscle fatness of the autochthonous muscles was not a risk factor for lumbar spine fractures (p = 0.157, RR = 2.04). Additionally, we found a moderate correlation between fatty degeneration of the autochthonous muscles and the severity of fractures in the lower thoracic spine (RR = 0.34, p < 0.001). The other degenerative changes did not present any significant difference or correlation between the evaluated groups. Conclusions: Fatty degeneration of the autochthonous spinal musculature is associated with incidentally found non-traumatic fractures of the lower thoracic spine.

Efficacy and confounding factors of CT attenuation value differences in distinguishing acute and old vertebral compression fractures: a retrospective study

PURPOSE

To evaluate the influence of various factors on CT attenuation values (HUs) of acute and old fracture vertebra, and to determine the efficacy of HU differences (△HUs) in the differentiation of the two type of fractures.

MATERIALS AND METHODS

A total of 113 acute and 71 old fracture vertebrae confirmed by MRI were included. Four HUs measured at the mid-sagittal, upper 1/3 axial, mid-axial, and lower 1/3 axial planes of each vertebra were obtained. The △HUs between fracture vertebra and its control counterpart was calculated. Receiver operating characteristic (ROC) curve analysis was used and the areas under the ROC curve (AUC) were calculated to evaluate the efficacy of HUs and △HUs. To evaluate the effect of height reduction, region, age and gender on HUs and △HUs, one-way analysis of variance, Pearson correlation analysis and t-test were used.

RESULTS

The HUs and △HUs at the upper 1/3 axial plane achieved the highest AUCs of 0.801 and 0.839, respectively. The HUs decreased gradually from Thoracic to Lumbar in control group of acute fracture. While no significant differences were found in the HUs among the 3 localizations in both fracture groups (all P > 0.05). The HUs were negatively correlated with age in all groups. The HUs of male were significantly higher than female patients in all groups (all P < 0.05). While △HU was not significantly different between males and females (all P > 0.05).

CONCLUSION

The vertebral HUs at the upper 1/3 axial plane are more likely to identify acute fractures. △HUs were beneficial in eliminating interfering factors.

Benign vs malignant vertebral compression fractures with MRI: a comparison between automatic deep learning network and radiologist's assessment

OBJECTIVE

To test the diagnostic performance of a deep-learning Two-Stream Compare and Contrast Network (TSCCN) model for differentiating benign and malignant vertebral compression fractures (VCFs) based on MRI.

METHODS

We tested a deep-learning system in 123 benign and 86 malignant VCFs. The median sagittal T1-weighted images (T1WI), T2-weighted images with fat suppression (T2WI-FS), and a combination of both (thereafter, T1WI/T2WI-FS) were used to validate TSCCN. The receiver operator characteristic (ROC) curve was analyzed to evaluate the performance of TSCCN. The accuracy, sensitivity, and specificity of TSCCN in differentiating benign and malignant VCFs were calculated and compared with radiologists' assessments. Intraclass correlation coefficients (ICCs) were tested to find intra- and inter-observer agreement of radiologists in differentiating malignant from benign VCFs.

RESULTS

The AUC of the ROC plots of TSCCN according to T1WI, T2WI-FS, and T1WI/T2WI-FS images were 99.2%, 91.7%, and 98.2%, respectively. The accuracy of T1W, T2WI-FS, and T1W/T2WI-FS based on TSCCN was 95.2%, 90.4%, and 96.2%, respectively, greater than that achieved by radiologists. Further, the specificity of T1W, T2WI-FS, and T1W/T2WI-FS based on TSCCN was higher at 98.4%, 94.3%, and 99.2% than that achieved by radiologists. The intra- and inter-observer agreements of radiologists were 0.79-0.85 and 0.79-0.80 for T1WI, 0.65-0.72 and 0.70-0.74 for T2WI-FS, and 0.83-0.88 and 0.83-0.84 for T1WI/T2WI-FS.

CONCLUSION

The TSCCN model showed better diagnostic performance than radiologists for automatically identifying benign or malignant VCFs, and is a potentially helpful tool for future clinical application.

CLINICAL RELEVANCE STATEMENT

TSCCN-assisted MRI has shown superior performance in distinguishing benign and malignant vertebral compression fractures compared to radiologists. This technology has the value to enhance diagnostic accuracy, sensitivity, and specificity. Further integration into clinical practice is required to optimize patient management.

KEY POINTS

• The Two-Stream Compare and Contrast Network (TSCCN) model showed better diagnostic performance than radiologists for identifying benign vs malignant vertebral compression fractures. • The processing of TSCCN is fast and stable, better than the subjective evaluation by radiologists in diagnosing vertebral compression fractures. • The TSCCN model provides options for developing a fully automated, streamlined artificial intelligence diagnostic tool.

The role of Artificial intelligence in the assessment of the spine and spinal cord

Artificial intelligence (AI) application development is underway in all areas of radiology where many promising tools are focused on the spine and spinal cord. In the past decade, multiple spine AI algorithms have been created based on radiographs, computed tomography, and magnetic resonance imaging. These algorithms have wide-ranging purposes including automatic labeling of vertebral levels, automated description of disc degenerative changes, detection and classification of spine trauma, identification of osseous lesions, and the assessment of cord pathology. The overarching goals for these algorithms include improved patient throughput, reducing radiologist workload burden, and improving diagnostic accuracy. There are several pre-requisite tasks required in order to achieve these goals, such as automatic image segmentation, facilitating image acquisition and postprocessing. In this narrative review, we discuss some of the important imaging AI solutions that have been developed for the assessment of the spine and spinal cord. We focus on their practical applications and briefly discuss some key requirements for the successful integration of these tools into practice. The potential impact of AI in the imaging assessment of the spine and cord is vast and promises to provide broad reaching improvements for clinicians, radiologists, and patients alike.

Can an artificial intelligence powered software reliably assess pelvic radiographs?

PURPOSE

Despite advances of three-dimensional imaging pelvic radiographs remain the cornerstone in the evaluation of the hip joint. However, large inter- and intra-rater variabilities were reported due to subjective landmark setting. Artificial intelligence (AI)-powered software applications could improve the reproducibility of pelvic radiograph evaluation by providing standardized measurements. The aim of this study was to evaluate the reliability and agreement of a newly developed AI algorithm for the evaluation of pelvic radiographs.

METHODS

Three-hundred pelvic radiographs from 280 patients with different degrees of acetabular coverage and osteoarthritis (Tönnis Grade 0 to 3) were evaluated. Reliability and agreement between manual measurements and the outputs of the AI software were assessed for the lateral-center-edge (LCE) angle, neck-shaft angle, sharp angle, acetabular index, as well as the femoral head extrusion index.

RESULTS

The AI software provided reliable results in 94.3% (283/300). The ICC values ranged between 0.73 for the Acetabular Index to 0.80 for the LCE Angle. Agreement between readers and AI outputs, given by the standard error of measurement (SEM), was good for hips with normal coverage (LCE-SEM: 3.4°) and no osteoarthritis (LCE-SEM: 3.3°) and worse for hips with undercoverage (LCE-SEM: 5.2°) or severe osteoarthritis (LCE-SEM: 5.1°).

CONCLUSION

AI-powered applications are a reliable alternative to manual evaluation of pelvic radiographs. While being accurate for patients with normal acetabular coverage and mild signs of osteoarthritis, it needs improvement in the evaluation of patients with hip dysplasia and severe osteoarthritis.

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.

Opportunistic Screening Techniques for Analysis of CT Scans

PURPOSE OF REVIEW

Opportunistic screening is a combination of techniques to identify subjects of high risk for osteoporotic fracture using routine clinical CT scans prescribed for diagnoses unrelated to osteoporosis. The two main components are automated detection of vertebral fractures and measurement of bone mineral density (BMD) in CT scans, in which a phantom for calibration of CT to BMD values is not used. This review describes the particular challenges of opportunistic screening and provides an overview and comparison of current techniques used for opportunistic screening. The review further outlines the performance of opportunistic screening.

RECENT FINDINGS

A wide range of technologies for the automatic detection of vertebral fractures have been developed and successfully validated. Most of them are based on artificial intelligence algorithms. The automated differentiation of osteoporotic from traumatic fractures and vertebral deformities unrelated to osteoporosis, the grading of vertebral fracture severity, and the detection of mild vertebral fractures is still problematic. The accuracy of automated fracture detection compared to classical radiological semi-quantitative Genant scoring is about 80%. Accuracy errors of alternative BMD calibration methods compared to simultaneous phantom-based calibration used in standard quantitative CT (QCT) range from below 5% to about 10%. The impact of contrast agents, frequently administered in clinical CT on the determination of BMD and on fracture risk determination is still controversial. Opportunistic screening, the identification of vertebral fracture and the measurement of BMD using clinical routine CT scans, is feasible but corresponding techniques still need to be integrated into the clinical workflow and further validated with respect to the prediction of fracture risk.

Vertebral trabecular bone texture analysis in opportunistic MRI and CT scan can distinguish patients with and without osteoporotic vertebral fracture: A preliminary study

PURPOSE

To investigate the potential of texture parameters from opportunistic MRI and CT for the detection of patients with vertebral fragility fracture, to design a decision tree and to compute a Random Forest analysis for the prediction of fracture risk.

METHODS

One hundred and eighty vertebrae of sixty patients with at least one (30) or without (30) a fragility fracture were retrospectively assessed. Patients had a DXA, an MRI and a CT scan from the three first lumbar vertebrae. Vertebrae texture analysis was performed in routine abdominal or lumbar CT and lumbar MRI using 1st and 2nd order texture parameters. Hounsfield Unit Bone density (HU BD) was also measured on CT-scan images.

RESULTS

Twelve texture parameters, Z-score and HU BD were significantly different between the two groups whereas T score and BMD were not. The inter observer reproducibility was good to excellent. Decision tree showed that age and HU BD were the most relevant factors to predict the fracture risk with a 93 % sensitivity and 56 % specificity. AUC was 0.91 in MRI and 0.92 in CT-scan using the Random Forest analysis. The corresponding sensitivity and specificity were 72 % and 93 % in MRI and 83 and 89 % in CT.

CONCLUSIONS

This study is the first to compare texture indices computed from opportunistic CT and MR images. Age and HU-BD together with selected texture parameters could be used to assess risk fracture. Machine learning algorithm can detect fracture risk in opportunistic CT and MR imaging and might be of high interest for the diagnosis of osteoporosis.

Interpretable vertebral fracture quantification via anchor-free landmarks localization

Vertebral body compression fractures are early signs of osteoporosis. Though these fractures are visible on Computed Tomography (CT) images, they are frequently missed by radiologists in clinical settings. Prior research on automatic methods of vertebral fracture classification proves its reliable quality; however, existing methods provide hard-to-interpret outputs and sometimes fail to process cases with severe abnormalities such as highly pathological vertebrae or scoliosis. We propose a new two-step algorithm to localize the vertebral column in 3D CT images and then detect individual vertebrae and quantify fractures in 2D simultaneously. We train neural networks for both steps using a simple 6-keypoints based annotation scheme, which corresponds precisely to the current clinical recommendation. Our algorithm has no exclusion criteria, processes 3D CT in 2 seconds on a single GPU, and provides an interpretable and verifiable output. The method approaches expert-level performance and demonstrates state-of-the-art results in vertebrae 3D localization (the average error is 1mm), vertebrae 2D detection (precision and recall are 0.99), and fracture identification (ROC AUC at the patient level is up to 0.96). Our anchor-free vertebra detection network shows excellent generalizability on a new domain by achieving ROC AUC 0.95, sensitivity 0.85, specificity 0.9 on a challenging VerSe dataset with many unseen vertebra types.

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.

Artificial intelligence, machine learning and deep learning in musculoskeletal imaging: Current applications

Artificial intelligence is rapidly expanding in all technological fields. The medical field, and especially diagnostic imaging, has been showing the highest developmental potential. Artificial intelligence aims at human intelligence simulation through the management of complex problems. This review describes the technical background of artificial intelligence, machine learning, and deep learning. The first section illustrates the general potential of artificial intelligence applications in the context of request management, data acquisition, image reconstruction, archiving, and communication systems. In the second section, the prospective of dedicated tools for segmentation, lesion detection, automatic diagnosis, and classification of musculoskeletal disorders is discussed.

Bone Fracture Detection Using Deep Supervised Learning from Radiological Images: A Paradigm Shift

Bone diseases are common and can result in various musculoskeletal conditions (MC). An estimated 1.71 billion patients suffer from musculoskeletal problems worldwide. Apart from musculoskeletal fractures, femoral neck injuries, knee osteoarthritis, and fractures are very common bone diseases, and the rate is expected to double in the next 30 years. Therefore, proper and timely diagnosis and treatment of a fractured patient are crucial. Contrastingly, missed fractures are a common prognosis failure in accidents and emergencies. This causes complications and delays in patients’ treatment and care. These days, artificial intelligence (AI) and, more specifically, deep learning (DL) are receiving significant attention to assist radiologists in bone fracture detection. DL can be widely used in medical image analysis. Some studies in traumatology and orthopaedics have shown the use and potential of DL in diagnosing fractures and diseases from radiographs. In this systematic review, we provide an overview of the use of DL in bone imaging to help radiologists to detect various abnormalities, particularly fractures. We have also discussed the challenges and problems faced in the DL-based method, and the future of DL in bone imaging.

A Classification Method for Thoracolumbar Vertebral Fractures due to Basketball Sports Injury Based on Deep Learning

Objective

There are more and more basketball competitions, to propose a classification method of thoracolumbar fractures to assist in the diagnosis of basketball injuries, to analyze the feasibility of its clinical application, and to improve the recovery rate.

Methods

From February 2015 to May 2022, 1130 CT images of thoracolumbar fractures admitted to our hospital and affiliated hospital units due to basketball injuries were collected, and the image labeling system uniformly labeled them. All CT images were classified according to the AO spine classification of thoracolumbar injuries. In the ABC-type classification, 935 CT images were used for training and validation to optimize the deep learning system, including 815 training sets and 120 validation sets; the remaining 198 CT images were used as test sets for comparing the deep learning system and clinician's diagnosis. In the classification of subtype A, a total of 523 CT scans can be performed for training and validation to optimize the deep learning system, including 500 training sets and 23 validation sets; the remaining 94 CT images are used as test sets for comparing depth learning systems and clinicians' diagnostic results.

Results

The deep learning system had a correct rate of ABC classification of fractures in 86.4%, with a kappa coefficient of 0.850 (P < 0.001); the correct rate of subtype A was 85.3%, with a kappa coefficient of 0.815 (P < 0.001).

Conclusion

The classification accuracy of thoracolumbar fractures based on deep learning is high. The method can assist in diagnosing CT images of thoracolumbar fractures and improve the current manual and complex diagnosis process.

Machine Learning for the Orthopaedic Surgeon: Uses and Limitations

➤

Machine learning is a subset of artificial intelligence in which computer algorithms are trained to make classifications and predictions based on patterns in data. The utilization of these techniques is rapidly expanding in the field of orthopaedic research.

➤

There are several domains in which machine learning has application to orthopaedics, including radiographic diagnosis, gait analysis, implant identification, and patient outcome prediction.

➤

Several limitations prevent the widespread use of machine learning in the daily clinical environment. However, future work can overcome these issues and enable machine learning tools to be a useful adjunct for orthopaedic surgeons in their clinical decision-making.

A deep learning-based method for the diagnosis of vertebral fractures on spine MRI: retrospective training and validation of ResNet

PURPOSE

To improve the performance of less experienced clinicians in the diagnosis of benign and malignant spinal fracture on MRI, we applied the ResNet50 algorithm to develop a decision support system.

METHODS

A total of 190 patients, 50 with malignant and 140 with benign fractures, were studied. The visual diagnosis was made by one senior MSK radiologist, one fourth-year resident, and one first-year resident. The MSK radiologist also gave the binary score for 15 qualitative imaging features. Deep learning was implemented using ResNet50, using one abnormal spinal segment selected from each patient as input. The T1W and T2W images of the lesion slice and its two neighboring slices were considered. The diagnostic performance was evaluated using tenfold cross-validation.

RESULTS

The overall reading accuracy was 98, 96, and 66% for the senior MSK radiologist, fourth-year resident, and first-year resident, respectively. Of the 15 imaging features, 10 showed a significant difference between benign and malignant groups with p <  = 0.001. The accuracy achieved by using the ResNet50 deep learning model for the identified abnormal vertebral segment was 92%. Compared to the first-year resident's reading, the model improved the sensitivity from 78 to 94% (p < 0.001) and the specificity from 61 to 91% (p < 0.001).

CONCLUSION

Our deep learning-based model may provide information to assist less experienced clinicians in the diagnosis of spinal fractures on MRI. Other findings away from the vertebral body need to be considered to improve the model, and further investigation is required to generalize our findings to real-world settings.

A software program for automated compressive vertebral fracture detection on elderly women's lateral chest radiograph: Ofeye 1.0

Background

Because osteoporotic vertebral fracture (OVF) on chest radiographs is commonly missed in radiological reports, we aimed to develop a software program which offers automated detection of compressive vertebral fracture (CVF) on lateral chest radiographs, and which emphasizes CVF detection specificity with a low false positivity rate.

Methods

For model training, we retrieved 3,991 spine radiograph cases and 1,979 chest radiograph cases from 16 sources, with among them in total 1,404 cases had OVF. For model testing, we retrieved 542 chest radiograph cases and 162 spine radiograph cases from four independent clinics, with among them 215 cases had OVF. All cases were female subjects, and except for 31 training data cases which were spine trauma cases, all the remaining cases were post-menopausal women. Image data included DICOM (Digital Imaging and Communications in Medicine) format, hard film scanned PNG (Portable Network Graphics) format, DICOM exported PNG format, and PACS (Picture Archiving and Communication System) downloaded resolution reduced DICOM format. OVF classification included: minimal and mild grades with <20% or ≥20–25% vertebral height loss respectively, moderate grade with ≥25–40% vertebral height loss, severe grade with ≥40%–2/3 vertebral height loss, and collapsed grade with ≥2/3 vertebral height loss. The CVF detection base model was mainly composed of convolution layers that include convolution kernels of different sizes, pooling layers, up-sampling layers, feature merging layers, and residual modules. When the model loss function could not be further decreased with additional training, the model was considered to be optimal and termed ‘base-model 1.0’. A user-friendly interface was also developed, with the synthesized software termed ‘Ofeye 1.0’.

Results

Counting cases and with minimal and mild OVFs included, base-model 1.0 demonstrated a specificity of 97.1%, a sensitivity of 86%, and an accuracy of 93.9% for the 704 testing cases. In total, 33 OVFs in 30 cases had a false negative reading, which constituted a false negative rate of 14.0% (30/215) by counting all OVF cases. Eighteen OVFs in 15 cases had OVFs of ≥ moderate grades missed, which constituted a false negative rate of 7.0% (15/215, i.e., sensitivity 93%) if only counting cases with ≥ moderate grade OVFs missed. False positive reading was recorded in 13 vertebrae in 13 cases (one vertebra in each case), which constituted a false positivity rate of 2.7% (13/489). These vertebrae with false positivity labeling could be readily differentiated from a true OVF by a human reader. The software Ofeye 1.0 allows ‘batch processing’, for example, 100 radiographs can be processed in a single operation. This software can be integrated into hospital PACS, or installed in a standalone personal computer.

Conclusions

A user-friendly software program was developed for CVF detection on elderly women’s lateral chest radiographs. It has an overall low false positivity rate, and for moderate and severe CVFs an acceptably low false negativity rate. The integration of this software into radiological practice is expected to improve osteoporosis management for elderly women.

Diagnostic accuracy and potential covariates of artificial intelligence for diagnosing orthopedic fractures: a systematic literature review and meta-analysis

OBJECTIVES

To systematically quantify the diagnostic accuracy and identify potential covariates affecting the performance of artificial intelligence (AI) in diagnosing orthopedic fractures.

METHODS

PubMed, Embase, Web of Science, and Cochrane Library were systematically searched for studies on AI applications in diagnosing orthopedic fractures from inception to September 29, 2021. Pooled sensitivity and specificity and the area under the receiver operating characteristic curves (AUC) were obtained. This study was registered in the PROSPERO database prior to initiation (CRD 42021254618).

RESULTS

Thirty-nine were eligible for quantitative analysis. The overall pooled AUC, sensitivity, and specificity were 0.96 (95% CI 0.94-0.98), 90% (95% CI 87-92%), and 92% (95% CI 90-94%), respectively. In subgroup analyses, multicenter designed studies yielded higher sensitivity (92% vs. 88%) and specificity (94% vs. 91%) than single-center studies. AI demonstrated higher sensitivity with transfer learning (with vs. without: 92% vs. 87%) or data augmentation (with vs. without: 92% vs. 87%), compared to those without. Utilizing plain X-rays as input images for AI achieved results comparable to CT (AUC 0.96 vs. 0.96). Moreover, AI achieved comparable results to humans (AUC 0.97 vs. 0.97) and better results than non-expert human readers (AUC 0.98 vs. 0.96; sensitivity 95% vs. 88%).

CONCLUSIONS

AI demonstrated high accuracy in diagnosing orthopedic fractures from medical images. Larger-scale studies with higher design quality are needed to validate our findings.

KEY POINTS

• Multicenter study design, application of transfer learning, and data augmentation are closely related to improving the performance of artificial intelligence models in diagnosing orthopedic fractures. • Utilizing plain X-rays as input images for AI to diagnose fractures achieved results comparable to CT (AUC 0.96 vs. 0.96). • AI achieved comparable results to humans (AUC 0.97 vs. 0.97) but was superior to non-expert human readers (AUC 0.98 vs. 0.96, sensitivity 95% vs. 88%) in diagnosing fractures.

Understanding current UK practice for the incidental identification of vertebral fragility fractures from CT scans: an expert elicitation study

Background

There is an emerging interest in using automated approaches to enable the incidental identification of vertebral fragility fractures (VFFs) on existing medical images visualising the spine.

Aim

To quantify values, and the degree of uncertainty associated with them, for the incidental identification of VFFs from computed tomography (CT) scans in current practice.

Methods

An expert elicitation exercise was conducted to generate point estimates and measures of uncertainty for four values representing the probability of: VFF being correctly reported by the radiologist; the absence of VFF being correctly assessed by the radiologist; being referred for management when a VFF is identified; having a dual-energy X-ray absorptiometry (DXA) scan after general practitioner (GP) referral. Data from a sample of seven experts in the diagnosis and management of people with VFFs were pooled using mathematical aggregation.

Results

The estimated mean values for each probability parameter were: VFF being correctly reported by the radiologist = 0.25 (standard deviation (SD): 0.21); absence of VFF being correctly assessed by the radiologist = 0.89 (0.10); being referred for management when a VFF is identified by the radiologist = 0.15 (0.12); having a DXA scan after GP referral = 0.66 (0.28).

Discussion

These estimates could be used to facilitate the subsequent early economic evaluation of potential new approaches to improve the health outcomes of people with VFFs.

Conclusion

In the absence of epidemiological studies, this study produced point estimates and measures of uncertainty for key parameters needed to describe current pathways for the incidental diagnosis of VFFs.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40520-022-02124-w.

Artificial intelligence in orthopaedic surgery: transforming technological innovation in patient care and surgical training

Artificial intelligence (AI) is an exciting field combining computer science with robust data sets to facilitate problem-solving. It has the potential to transform education, practice and delivery of healthcare especially in orthopaedics. This review article outlines some of the already used AI pathways as well as recent technological advances in orthopaedics. Additionally, this article further explains how potentially these two entities could be combined in the future to improve surgical education, training and ultimately patient care and outcomes.

Ground truth generalizability affects performance of the artificial intelligence model in automated vertebral fracture detection on plain lateral radiographs of the spine

BACKGROUND CONTEXT

Computer-aided diagnosis with artificial intelligence (AI) has been used clinically, and ground truth generalizability is important for AI performance in medical image analyses. The AI model was trained on one specific group of older adults (aged≧60) has not yet been shown to work equally well in a younger adult group (aged 18-59).

PURPOSE

To compare the performance of the developed AI model with ensemble method trained with the ground truth for those aged 60 years or older in identifying vertebral fractures (VFs) on plain lateral radiographs of spine (PLRS) between younger and older adult populations.

STUDY DESIGN/SETTING

Retrospective analysis of PLRS in a single medical institution.

OUTCOME MEASURES

Accuracy, sensitivity, specificity, and interobserver reliability (kappa value) were used to compare diagnostic performance of the AI model and subspecialists' consensus between the two groups.

METHODS

Between January 2016 and December 2018, the ground truth of 941 patients (one PLRS per person) aged 60 years and older with 1101 VFs and 6358 normal vertebrae was used to set up the AI model. The framework of the developed AI model includes: object detection with You Only Look Once Version 3 (YOLOv3) at T0-L5 levels in the PLRS, data pre-preprocessing with image-size and quality processing, and AI ensemble model (ResNet34, DenseNet121, and DenseNet201) for identifying or grading VFs. The reported overall accuracy, sensitivity and specificity were 92%, 91% and 93%, respectively, and external validation was also performed. Thereafter, patients diagnosed as VFs and treated in our institution during October 2019 to August 2020 were the study group regardless of age. In total, 258 patients (339 VFs and 1725 normal vertebrae) in the older adult population (mean age 78±10.4; range, 60-106) were enrolled. In the younger adult population (mean age 36±9.43; range, 20-49), 106 patients (120 VFs and 728 normal vertebrae) were enrolled. After identification and grading of VFs based on the Genant method with consensus between two subspecialists', VFs in each PLRS with human labels were defined as the testing dataset. The corresponding CT or MRI scan was used for labeling in the PLRS. The bootstrap method was applied to the testing dataset.

RESULTS

The model for clinical application, Digital Imaging and Communications in Medicine (DICOM) format, is uploaded directly (available at: http://140.113.114.104/vght_demo/svf-model (grading) and http://140.113.114.104/vght demo/svf-model2 (labeling). Overall accuracy, sensitivity and specificity in the older adult population were 93.36% (95% CI 93.34%-93.38%), 88.97% (95% CI 88.59%-88.99%) and 94.26% (95% CI 94.23%-94.29%), respectively. Overall accuracy, sensitivity and specificity in the younger adult population were 93.75% (95% CI 93.7%-93.8%), 65.00% (95% CI 64.33%-65.67%) and 98.49% (95% CI 98.45%-98.52%), respectively. Accuracy reached 100% in VFs grading once the VFs were labeled accurately. The unique pattern of limbus-like VFs, 43 (35.8%) were investigated only in the younger adult population. If limbus-like VFs from the dataset were not included, the accuracy increased from 93.75% (95% CI 93.70%-93.80%) to 95.78% (95% CI 95.73%-95.82%), sensitivity increased from 65.00% (95% CI 64.33%-65.67%) to 70.13% (95% CI 68.98%-71.27%) and specificity remained unchanged at 98.49% (95% CI 98.45%-98.52%), respectively. The main causes of false negative results in older adults were patients' lung markings, diaphragm or bowel airs (37%, n=14) followed by type I fracture (29%, n=11). The main causes of false negatives in younger adults were limbus-like VFs (45%, n=19), followed by type I fracture (26%, n=11). The overall kappa between AI discrimination and subspecialists' consensus in the older and younger adult populations were 0.77 (95% CI, 0.733-0.805) and 0.72 (95% CI, 0.6524-0.80), respectively.

CONCLUSIONS

The developed VF-identifying AI ensemble model based on ground truth of older adults achieved better performance in identifying VFs in older adults and non-fractured thoracic and lumbar vertebrae in the younger adults. Different age distribution may have potential disease diversity and implicate the effect of ground truth generalizability on the AI model performance.

Automated prediction of the Thoracolumbar Injury Classification and Severity Score from CT using a novel deep learning algorithm

OBJECTIVE

Damage to the thoracolumbar spine can confer significant morbidity and mortality. The Thoracolumbar Injury Classification and Severity Score (TLICS) is used to categorize injuries and determine patients at risk of spinal instability for whom surgical intervention is warranted. However, calculating this score can constitute a bottleneck in triaging and treating patients, as it relies on multiple imaging studies and a neurological examination. Therefore, the authors sought to develop and validate a deep learning model that can automatically categorize vertebral morphology and determine posterior ligamentous complex (PLC) integrity, two critical features of TLICS, using only CT scans.

METHODS

All patients who underwent neurosurgical consultation for traumatic spine injury or degenerative pathology resulting in spine injury at a single tertiary center from January 2018 to December 2019 were retrospectively evaluated for inclusion. The morphology of injury and integrity of the PLC were categorized on CT scans. A state-of-the-art object detection region-based convolutional neural network (R-CNN), Faster R-CNN, was leveraged to predict both vertebral locations and the corresponding TLICS. The network was trained with patient CT scans, manually labeled vertebral bounding boxes, TLICS morphology, and PLC annotations, thus allowing the model to output the location of vertebrae, categorize their morphology, and determine the status of PLC integrity.

RESULTS

A total of 111 patients were included (mean ± SD age 62 ± 20 years) with a total of 129 separate injury classifications. Vertebral localization and PLC integrity classification achieved Dice scores of 0.92 and 0.88, respectively. Binary classification between noninjured and injured morphological scores demonstrated 95.1% accuracy. TLICS morphology accuracy, the true positive rate, and positive injury mismatch classification rate were 86.3%, 76.2%, and 22.7%, respectively. Classification accuracy between no injury and suspected PLC injury was 86.8%, while true positive, false negative, and false positive rates were 90.0%, 10.0%, and 21.8%, respectively.

CONCLUSIONS

In this study, the authors demonstrate a novel deep learning method to automatically predict injury morphology and PLC disruption with high accuracy. This model may streamline and improve diagnostic decision support for patients with thoracolumbar spinal trauma.

Machine Learning Applications of Surgical Imaging for the Diagnosis and Treatment of Spine Disorders: Current State of the Art

Recent developments in machine learning (ML) methods demonstrate unparalleled potential for application in the spine. The ability for ML to provide diagnostic faculty, produce novel insights from existing capabilities, and augment or accelerate elements of surgical planning and decision making at levels equivalent or superior to humans will tremendously benefit spine surgeons and patients alike. In this review, we aim to provide a clinically relevant outline of ML-based technology in the contexts of spinal deformity, degeneration, and trauma, as well as an overview of commercial-level and precommercial-level surgical assist systems and decisional support tools. Furthermore, we briefly discuss potential applications of generative networks before highlighting some of the limitations of ML applications. We conclude that ML in spine imaging represents a significant addition to the neurosurgeon's armamentarium-it has the capacity to directly address and manifest clinical needs and improve diagnostic and procedural quality and safety-but is yet subject to challenges that must be addressed before widespread implementation.

Deep Scale-Variant Network for Femur Trochanteric Fracture Classification with HP Loss

Achieving automatic classification of femur trochanteric fracture from the edge computing device is of great importance and value for remote diagnosis and treatment. Nevertheless, designing a highly accurate classification model on 31A1/31A2/31A3 fractures from the X-ray is still limited due to the failure of capturing the scale-variant and contextual information. As a result, this paper proposes a deep scale-variant (DSV) network with a hybrid and progressive (HP) loss function to aggregate more influential representations of the fracture regions. More specifically, the DSV network is based on the ResNet and integrated with the designed scale-variant (SV) layer and HP loss, where the SV layer aims to enhance the representation ability to extract the scale-variant features, and HP loss is intended to force the network to condense more contextual clues. Furthermore, to evaluate the effect of the proposed DSV network, we carry out a series of experiments on the real X-ray images for comparison and evaluation, and the experimental results demonstrate that the proposed DSV network could outperform other classification methods on this classification task.

Epidemiology and reporting of osteoporotic vertebral fractures in patients with long-term hospital records based on routine clinical CT imaging

Osteoporotic vertebral fractures signify an increased risk of future fractures and mortality and can manifest the diagnosis of osteoporosis. We investigated the prevalence of vertebral fractures in routine CT of patients with long-term hospital records. Three out of ten patients showed osteoporotic vertebral fractures (VFs) corresponding to the highest rates reported in European population-based studies.

INTRODUCTION

VFs are a common manifestation of osteoporosis, which influences future fracture risk. Their epidemiology has been investigated in population-based studies. However, few studies report the prevalence of osteoporotic VF in patients seen in clinical routine and include all common fracture levels of the thoracolumbar spine. The purpose of this study was to investigate the prevalence of osteoporotic VF in patients with CT scans and long-term hospital records and identify clinical factors associated with prevalent VFs.

METHODS

All patients aged 45 years and older with a CT scan and prior hospital record of at least 5 years that were seen in the study period between September 2008 and May 2017 were reviewed. Imaging requirements were a CT scan with sagittal reformations including at least T6-L4. Patients with multiple myeloma were excluded. Fracture reading was performed using the Genant semi-quantitative method. Medical notes were reviewed for established diagnoses of osteoporosis and clinical information. Clinical factors (e.g. drug intake, chemotherapy, and mobility level) associated with prevalent VF were identified in logistic regression.

RESULTS

The study population consisted of 718 patients (228 women and 490 men; mean age 69.3 ± 10.1 years) with mainly cancer staging and angiography CT imaging. The overall prevalence of VFs was 30.5%, with non-significantly more men showing a fracture (32.5%) compared to women (26.3%; p > 0.05). Intake of metamizole for ≥ 3 months was significantly associated with a prevalent VF. Medical records did not include information about bone health in 90% of all patients. CT reports did mention a VF in only 24.7% of patients with a prevalent VF on CT review.

CONCLUSION

Approximately 30% of elderly patients with CT imaging and long-term hospital records showed VFs. Only one-quarter of these patients had VFs mentioned in CT reports. Osteoporosis management could be improved by consequent reporting of VFs in CT, opportunistic bone density measurements, and early involvement of fracture liaison services.