Generalizability of Deep Learning Classification of Spinal Osteoporotic Compression Fractures on Radiographs Using an Adaptation of the Modified-2 Algorithm-Based Qualitative Criteria

Dong, Qifei; Luo, Gang; Lane, Nancy E.; Lui, Li-Yung; Marshall, Lynn M.; Johnston, Sandra K.; Dabbous, Howard; O’Reilly, Michael; Linnau, Ken F.; Perry, Jessica; Chang, Brian C.; Renslo, Jonathan; Haynor, David; Jarvik, Jeffrey G.; Cross, Nathan M.

doi:10.1016/j.acra.2023.04.023

Cited by 9 publications

(3 citation statements)

References 42 publications

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“…Dong et al. ( 19 ) trained models (GoogLeNet, Inception-ResNet-v2, EfficientNet-B1, and two ensemble algorithms) based on the m2ABQ classification method for OVFs, using ImageNet pre-trained models for transfer learning. The best-performing model achieved excellent results (AUCs of 0.948 for the local test set and 0.936 for the MrOS test set), yet the authors did not analyze misclassified cases nor explore how image features affect the output of each model.…”

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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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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“…It is reported that CNN's prediction algorithm based on image and clinical information outperforms FRAX 20 in predicting osteoporotic fractures through spinal X‐rays. In previous studies, most of the OVCF research has been done by incorporating AI for diagnosis as well as prediction of surgical outcomes 21–23 . Effective predictors of osteoporotic refracture are still lacking.…”

Section: Introductionmentioning

confidence: 99%

“…In previous studies, most of the OVCF research has been done by incorporating AI for diagnosis as well as prediction of surgical outcomes. 21 , 22 , 23 Effective predictors of osteoporotic refracture are still lacking. Therefore, the aim of this study was to comprehensively collect CT and clinical indicators to determine the most sensitive indicators of osteoporotic refracture and to establish a fully automated model of OVCF through deep learning, so that clinicians can take targeted preventive and therapeutic measures for high‐risk patients.…”

Section: Introductionmentioning

confidence: 99%

Fully Automatic Deep Learning Model for Spine Refracture in Patients with OVCF: A Multi‐Center Study

Zhu,

Liu,

Liu

et al. 2024

Orthopaedic Surgery

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BackgroundThe reaserch of artificial intelligence (AI) model for predicting spinal refracture is limited to bone mineral density, X‐ray and some conventional laboratory indicators, which has its own limitations. Besides, it lacks specific indicators related to osteoporosis and imaging factors that can better reflect bone quality, such as computed tomography (CT).ObjectiveTo construct a novel predicting model based on bone turn‐over markers and CT to identify patients who were more inclined to suffer spine refracture.MethodsCT images and clinical information of 383 patients (training set = 240 cases of osteoporotic vertebral compression fractures (OVCF), validation set = 63, test set = 80) were retrospectively collected from January 2015 to October 2022 at three medical centers. The U‐net model was adopted to automatically segment ROI. Three‐dimensional (3D) cropping of all spine regions was used to achieve the final ROI regions including 3D_Full and 3D_RoiOnly. We used the Densenet 121‐3D model to model the cropped region and simultaneously build a T‐NIPT prediction model. Diagnostics of deep learning models were assessed by constructing ROC curves. We generated calibration curves to assess the calibration performance. Additionally, decision curve analysis (DCA) was used to assess the clinical utility of the predictive models.ResultsThe performance of the test model is comparable to its performance on the training set (dice coefficients of 0.798, an mIOU of 0.755, an SA of 0.767, and an OS of 0.017). Univariable and multivariable analysis indicate that T_P1NT was an independent risk factor for refracture. The performance of predicting refractures in different ROI regions showed that 3D_Full model exhibits the highest calibration performance, with a Hosmer–Lemeshow goodness‐of‐fit (HL) test statistic exceeding 0.05. The analysis of the training and test sets showed that the 3D_Full model, which integrates clinical and deep learning results, demonstrated superior performance with significant improvement (p‐value < 0.05) compared to using clinical features independently or using only 3D_RoiOnly.ConclusionT_P1NT was an independent risk factor of refracture. Our 3D‐FULL model showed better performance in predicting high‐risk population of spine refracture than other models and junior doctors do. This model can be applicable to real‐world translation due to its automatic segmentation and detection.

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Explainable Vertebral Fracture Analysis with Uncertainty Estimation Using Differentiable Rule-Based Classification

Wåhlstrand Skärström,

Johansson,

Alvén

et al. 2024

Lecture Notes in Computer Science

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Generalizability of Deep Learning Classification of Spinal Osteoporotic Compression Fractures on Radiographs Using an Adaptation of the Modified-2 Algorithm-Based Qualitative Criteria

Cited by 9 publications

References 42 publications

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

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

Fully Automatic Deep Learning Model for Spine Refracture in Patients with OVCF: A Multi‐Center Study

Explainable Vertebral Fracture Analysis with Uncertainty Estimation Using Differentiable Rule-Based Classification

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