ArticleScientific reports2021
A deep learning algorithm for automated measurement of vertebral body compression from X-ray images.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 19 papers.
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19 citing papers in PubMed.
- Deep learning-driven incidental detection of vertebral fractures in cancer patients: advancing diagnostic precision and clinical management.La Radiologia medica · 2025Article
- The prevention of osteoporotic vertebral fractures in eastern and in western countries.Bone reports · 2025Review
- An accelerated deep learning model can accurately identify clinically important humeral and scapular landmarks on plain radiographs obtained before and after anatomic arthroplasty.International orthopaedics · 2025Article
- Enhanced Detection Performance of Acute Vertebral Compression Fractures Using a Hybrid Deep Learning and Traditional Quantitative Measurement Approach: Beyond the Limitations of Genant Classification.Bioengineering (Basel, Switzerland) · 2025Article
- Detection and Localization of Spine Disorders from Plain Radiography.Journal of imaging informatics in medicine · 2024Article
- Improved Detection Accuracy of Chronic Vertebral Compression Fractures by Integrating Height Loss Ratio and Deep Learning Approaches.Diagnostics (Basel, Switzerland) · 2024Article
- Automated measurement of lumbar pedicle screw parameters using deep learning algorithm on preoperative CT scans.Journal of bone oncology · 2024Article
- Fully Automatic Deep Learning Model for Spine Refracture in Patients with OVCF: A Multi-Center Study.Orthopaedic surgery · 2024Article
- Deep learning application of vertebral compression fracture detection using mask R-CNN.Scientific reports · 2024Article
- Opportunistic Screening for Acute Vertebral Fractures on a Routine Abdominal or Chest Computed Tomography Scans Using an Automated Deep Learning Model.Diagnostics (Basel, Switzerland) · 2024Article
- Development of a shape-based algorithm for identification of asymptomatic vertebral compression fractures: A proof-of-principle study.Osteoporosis and sarcopenia · 2024Article
- Common differential diagnosis of low back pain in contemporary medical practice: a narrative review.Frontiers in medicine · 2024Review
- Artificial intelligence CAD tools in trauma imaging: a scoping review from the American Society of Emergency Radiology (ASER) AI/ML Expert Panel.Emergency radiology · 2023Article
- Diagnosis of osteoporotic vertebral compression fractures and fracture level detection using multitask learning with U-Net in lumbar spine lateral radiographs.Computational and structural biotechnology journal · 2023Article
- Investigation of Deep Spiking Neural Networks Utilizing Gated Schottky Diode as Synaptic Devices.Micromachines · 2022Article
- A Meaningful Journey to Predict Fractures with Deep Learning.Endocrinology and metabolism (Seoul, Korea) · 2022Article
- A software program for automated compressive vertebral fracture detection on elderly women's lateral chest radiograph: Ofeye 1.0.Quantitative imaging in medicine and surgery · 2022Article
- A Classification Method for Thoracolumbar Vertebral Fractures due to Basketball Sports Injury Based on Deep Learning.Computational and mathematical methods in medicine · 2022Article
- Exploring Multiple Application Scenarios of Visual Communication Course Using Deep Learning Under the Digital Twins.Computational intelligence and neuroscience · 2022Article
Corrections and comments
- Erratum issued
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The vertebral compression is a significant factor for determining the prognosis of osteoporotic vertebral compression fractures and is generally measured manually by specialists. The consequent misdiagnosis or delayed diagnosis can be fatal for patients. In this study, we trained and evaluated the performance of a vertebral body segmentation model and a vertebral compression measurement model based on convolutional neural networks. For vertebral body segmentation, we used a recurrent residual U-Net model, with an average sensitivity of 0.934 (± 0.086), an average specificity of 0.997 (± 0.002), an average accuracy of 0.987 (± 0.005), and an average dice similarity coefficient of 0.923 (± 0.073). We then generated 1134 data points on the images of three vertebral bodies by labeling each segment of the segmented vertebral body. These were used in the vertebral compression measurement model based on linear regression and multi-scale residual dilated blocks. The model yielded an average mean absolute error of 2.637 (± 1.872) (%), an average mean square error of 13.985 (± 24.107) (%), and an average root mean square error of 3.739 (± 2.187) (%) in fractured vertebral body data. The proposed algorithm has significant potential for aiding the diagnosis of vertebral compression fractures.
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