ArticleScientific reports2021
Detecting pelvic fracture on 3D-CT using deep convolutional neural networks with multi-orientated slab images.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence for fracture detection on computed tomography: a comprehensive systematic review and meta-analysis of diagnostic test accuracy in non-commercial and commercial solutions.Emergency radiology · 2026Pooled it
- Beyond the pelvic ring: benchmarking multi-bone segmentation for ROI-Guided fracture detection.Scientific reports · 2026Article
- Progress in fully automated abdominal CT interpretation-an update over the past decade.Abdominal radiology (New York) · 2026Review
- Deep learning and hybrid architectures for atypical and complex bone fracture diagnosis: a systematic review of performance and clinical validity.Frontiers in artificial intelligence · 2026Review
- Efficiency of Artificial Intelligence in Three-Dimensional Reconstruction of Medical Imaging.Cureus · 2025Review
- Artificial intelligence for abdominopelvic trauma imaging: trends, gaps, and future directions.Abdominal radiology (New York) · 2025Review
- Automatic pelvic fracture segmentation: a deep learning approach and benchmark dataset.Frontiers in medicine · 2025Article
- AI Revolution in Orthopedic Biomechanics: From Fracture Classification to Real-Time Simulations.The archives of bone and joint surgery · 2025Article
- Interpretable Severity Scoring of Pelvic Trauma Through Automated Fracture Detection and Bayesian Inference.IEEE transactions on medical imaging · 2025Article
- Application and Prospects of Deep Learning Technology in Fracture Diagnosis.Current medical science · 2024Review
- Automated Association for Osteosynthesis Foundation and Orthopedic Trauma Association classification of pelvic fractures on pelvic radiographs using deep learning.Scientific reports · 2024Article
- Enhancing fracture diagnosis in pelvic X-rays by deep convolutional neural network with synthesized images from 3D-CT.Scientific reports · 2024Article
- Artificial intelligence fracture recognition on computed tomography: review of literature and recommendations.European journal of trauma and emergency surgery : official publication of the European Trauma Society · 2023Review
- Rupture Prediction for Microscopic Oocyte Images of Piezo Intracytoplasmic Sperm Injection by Principal Component Analysis.Journal of clinical medicine · 2022Article
- Automated fracture screening using an object detection algorithm on whole-body trauma computed tomography.Scientific reports · 2022Article
- An Extra Set of Intelligent Eyes: Application of Artificial Intelligence in Imaging of Abdominopelvic Pathologies in Emergency Radiology.Diagnostics (Basel, Switzerland) · 2022Review
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Authors and funding
7 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Pelvic fracture is one of the leading causes of death in the elderly, carrying a high risk of death within 1 year of fracture. This study proposes an automated method to detect pelvic fractures on 3-dimensional computed tomography (3D-CT). Deep convolutional neural networks (DCNNs) have been used for lesion detection on 2D and 3D medical images. However, training a DCNN directly using 3D images is complicated, computationally costly, and requires large amounts of training data. We propose a method that evaluates multiple, 2D, real-time object detection systems (YOLOv3 models) in parallel, in which each YOLOv3 model is trained using differently orientated 2D slab images reconstructed from 3D-CT. We assume that an appropriate reconstruction orientation would exist to optimally characterize image features of bone fractures on 3D-CT. Multiple YOLOv3 models in parallel detect 2D fracture candidates in different orientations simultaneously. The 3D fracture region is then obtained by integrating the 2D fracture candidates. The proposed method was validated in 93 subjects with bone fractures. Area under the curve (AUC) was 0.824, with 0.805 recall and 0.907 precision. The AUC with a single orientation was 0.652. This method was then applied to 112 subjects without bone fractures to evaluate over-detection. The proposed method successfully detected no bone fractures in all except 4 non-fracture subjects (96.4%).
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