Deep Learning Classification of Spinal Osteoporotic Compression Fractures on Radiographs using an Adaptation of the Genant Semiquantitative Criteria

Dong, Qifei; Luo, Gang; Lane, Nancy E.; Lui, Li‐Yung; Marshall, Lynn M.; Kado, Deborah M.; Cawthon, Peggy M.; Perry, Jessica; Johnston, Sandra K.; Haynor, David R.; Jarvik, Jeffrey G.; Cross, Nathan M.

doi:10.1016/j.acra.2022.02.020

Cited by 24 publications

(25 citation statements)

References 37 publications

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“…After removing duplicates and screening titles and abstracts, 378 articles remained. According to a full-text review, 53 articles13–67 were included. Fifty-three articles presented the development of one or more prediction models for osteoporotic fracture, while 26 articles described the validation of one or more models.…”

Section: Resultsmentioning

confidence: 99%

“…Its performance increases with the amount of data 70. Deep learning has been successfully applied to assist in the diagnosis and prediction of osteoporotic fractures 34 63. CNN, a core algorithm of deep learning, is widely used in the field of data analysis and disease prediction with high accuracy 71.…”

Section: Discussionmentioning

confidence: 99%

“…Some limitations still need to be considered in the present study. Due to incomplete reporting of indicators, several studies were only included in the systematic review and were excluded from subsequent meta-analyses 61 63. Studies conducted in either Western or Asian populations lack external validation, and thus external validations in other populations are needed to widen the application of ML models.…”

Section: Discussionmentioning

confidence: 99%

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Predictive value of machine learning on fracture risk in osteoporosis: a systematic review and meta-analysis

Wu,

Chao,

Bao

et al. 2023

BMJ Open

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ObjectivesEarly identification of fracture risk in patients with osteoporosis is essential. Machine learning (ML) has emerged as a promising technique to predict the risk, whereas its predictive performance remains controversial. Therefore, we conducted this systematic review and meta-analysis to explore the predictive efficiency of ML for the risk of fracture in patients with osteoporosis.MethodsRelevant studies were retrieved from four databases (PubMed, Embase, Cochrane Library and Web of Science) until 31 May 2023. A meta-analysis of the C-index was performed using a random-effects model, while a bivariate mixed-effects model was used for the meta-analysis of sensitivity and specificity. In addition, subgroup analysis was performed according to the types of ML models and fracture sites.ResultsFifty-three studies were included in our meta-analysis, involving 15 209 268 patients, 86 prediction models specifically developed for the osteoporosis population and 41 validation sets. The most commonly used predictors in these models encompassed age, BMI, past fracture history, bone mineral density T-score, history of falls, BMD, radiomics data, weight, height, gender and other chronic diseases. Overall, the pooled C-index of ML was 0.75 (95% CI: 0.72, 0.78) and 0.75 (95% CI: 0.71, 0.78) in the training set and validation set, respectively; the pooled sensitivity was 0.79 (95% CI: 0.72, 0.84) and 0.76 (95% CI: 0.80, 0.81) in the training set and validation set, respectively; and the pooled specificity was 0.81 (95% CI: 0.75, 0.86) and 0.83 (95% CI: 0.72, 0.90) in the training set and validation set, respectively.ConclusionsML has a favourable predictive performance for fracture risk in patients with osteoporosis. However, most current studies lack external validation. Thus, external validation is required to verify the reliability of ML models.PROSPERO registration numberCRD42022346896.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Predictive value of machine learning on fracture risk in osteoporosis: a systematic review and meta-analysis

Wu,

Chao,

Bao

et al. 2023

BMJ Open

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“…Acknowledged by both national and international scientific communities, the consensus is that achieving a comprehensive and precise classification of OVFs holds significant value for early diagnosis, treatment, and prognosis assessment ( 5 ). Several classification methods for OVFs have been put forth, such as the Genant semi-quantitative method ( 6 ), Heini classification ( 7 ), Osteoporotic Fracture Classification ( 8 ), and ASTLOF ( 9 ). However, none of these methods have garnered international acceptance ( 10 ).…”

Section: Introductionmentioning

confidence: 99%

Exploring deep learning radiomics for classifying osteoporotic vertebral fractures in X-ray images

Zhang,

Xia,

Liu

et al. 2024

Front. Endocrinol.

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PurposeTo develop and validate a deep learning radiomics (DLR) model that uses X-ray images to predict the classification of osteoporotic vertebral fractures (OVFs).Material and methodsThe study encompassed a cohort of 942 patients, involving examinations of 1076 vertebrae through X-ray, CT, and MRI across three distinct hospitals. The OVFs were categorized as class 0, 1, or 2 based on the Assessment System of Thoracolumbar Osteoporotic Fracture. The dataset was divided randomly into four distinct subsets: a training set comprising 712 samples, an internal validation set with 178 samples, an external validation set containing 111 samples, and a prospective validation set consisting of 75 samples. The ResNet-50 architectural model was used to implement deep transfer learning (DTL), undergoing -pre-training separately on the RadImageNet and ImageNet datasets. Features from DTL and radiomics were extracted and integrated using X-ray images. The optimal fusion feature model was identified through least absolute shrinkage and selection operator logistic regression. Evaluation of the predictive capabilities for OVFs classification involved eight machine learning models, assessed through receiver operating characteristic curves employing the “One-vs-Rest” strategy. The Delong test was applied to compare the predictive performance of the superior RadImageNet model against the ImageNet model.ResultsFollowing pre-training separately on RadImageNet and ImageNet datasets, feature selection and fusion yielded 17 and 12 fusion features, respectively. Logistic regression emerged as the optimal machine learning algorithm for both DLR models. Across the training set, internal validation set, external validation set, and prospective validation set, the macro-average Area Under the Curve (AUC) based on the RadImageNet dataset surpassed those based on the ImageNet dataset, with statistically significant differences observed (P<0.05). Utilizing the binary “One-vs-Rest” strategy, the model based on the RadImageNet dataset demonstrated superior efficacy in predicting Class 0, achieving an AUC of 0.969 and accuracy of 0.863. Predicting Class 1 yielded an AUC of 0.945 and accuracy of 0.875, while for Class 2, the AUC and accuracy were 0.809 and 0.692, respectively.ConclusionThe DLR model, based on the RadImageNet dataset, outperformed the ImageNet model in predicting the classification of OVFs, with generalizability confirmed in the prospective validation set.

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“…Deep learning algorithms are powerful algorithms for medical image analysis, such as image classification, object detection, and image segmentation ( 17 , 18 ). Some studies have applied deep learning to the detection of vertebral fractures on X-ray images, and it can assist in measuring vertebral height loss and identifying fresh fractures ( 19 , 20 ). There are also some explorations on the use of deep learning in identifying osteoporotic vertebral fractures and differentiating benign and malignant vertebral fractures on CT ( 21 , 22 ).…”

Section: Introductionmentioning

confidence: 99%

Automated detection and classification of acute vertebral body fractures using a convolutional neural network on computed tomography

Zhang

Liu²,

Xu³

et al. 2023

Front. Endocrinol.

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BackgroundAcute vertebral fracture is usually caused by low-energy injury with osteoporosis and high-energy trauma. The AOSpine thoracolumbar spine injury classification system (AO classification) plays an important role in the diagnosis and treatment of the disease. The diagnosis and description of vertebral fractures according to the classification scheme requires a great deal of time and energy for radiologists.PurposeTo design and validate a multistage deep learning system (multistage AO system) for the automatic detection, localization and classification of acute thoracolumbar vertebral body fractures according to AO classification on computed tomography.Materials and MethodsThe CT images of 1,217 patients who came to our hospital from January 2015 to December 2019 were collected retrospectively. The fractures were marked and classified by 2 junior radiology residents according to the type A standard in the AO classification. Marked fracture sites included the upper endplate, lower endplate and posterior wall. When there were inconsistent opinions on classification labels, the final result was determined by a director radiologist. We integrated different networks into different stages of the overall framework. U-net and a graph convolutional neural network (U-GCN) are used to realize the location and classification of the thoracolumbar spine. Next, a classification network is used to detect whether the thoracolumbar spine has a fracture. In the third stage, we detect fractures in different parts of the thoracolumbar spine by using a multibranch output network and finally obtain the AO types.ResultsThe mean age of the patients was 61.87 years with a standard deviation of 17.04 years, consisting of 760 female patients and 457 male patients. On vertebrae level, sensitivity for fracture detection was 95.23% in test dataset, with an accuracy of 97.93% and a specificity of 98.35%. For the classification of vertebral body fractures, the balanced accuracy was 79.56%, with an AUC of 0.904 for type A1, 0.945 for type A2, 0.878 for type A3 and 0.942 for type A4.ConclusionThe multistage AO system can automatically detect and classify acute vertebral body fractures in the thoracolumbar spine on CT images according to AO classification with high accuracy.

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Deep Learning Classification of Spinal Osteoporotic Compression Fractures on Radiographs using an Adaptation of the Genant Semiquantitative Criteria

Cited by 24 publications

References 37 publications

Predictive value of machine learning on fracture risk in osteoporosis: a systematic review and meta-analysis

Predictive value of machine learning on fracture risk in osteoporosis: a systematic review and meta-analysis

Exploring deep learning radiomics for classifying osteoporotic vertebral fractures in X-ray images

Automated detection and classification of acute vertebral body fractures using a convolutional neural network on computed tomography

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