Computer-Aided Diagnosis System for Bone Fracture Detection and Classification: A Review on Deep Learning Techniques

Bone fracture detection and classification was a large discussed topic over the last few years and many researchers proposed different technological solutions to tackle this task. Despite this, a universal approach able to support the classification of fractures in the human body still does not exist today. We aim to provide a first discussion concerning a selection of research works done in the technological domain, with a specific focus on Deep Learning. The objective was to underline a picture on the most promising studies for stimulating a knowledge improvement in the specific focus of bone fracture classification, necessary to start the development of an optimal shared framework. The evaluation has been made involving a first qualitative assessment based on strengths and weaknesses, providing a usage scenario evaluation. This could support the development of a helpful Computer Aided Diagnosis (CAD) system able to drive doctors in diagnosis tasks reducing diagnosis time, especially in the most complex tasks, and supporting the reduction of wrong diagnosis issues, especially during stressful working conditions, as what frequently happens in many emergency departments

Fracture Detection in Pediatric Wrist Trauma X-ray Images Using YOLOv8 Algorithm

Hospital emergency departments frequently receive lots of bone fracture cases, with pediatric wrist trauma fracture accounting for the majority of them. Before pediatric surgeons perform surgery, they need to ask patients how the fracture occurred and analyze the fracture situation by interpreting X-ray images. The interpretation of X-ray images often requires a combination of techniques from radiologists and surgeons, which requires time-consuming specialized training. With the rise of deep learning in the field of computer vision, network models applying for fracture detection has become an important research topic. In this paper, YOLOv8 algorithm is used to train models on the GRAZPEDWRI-DX dataset, which includes X-ray images from 6,091 pediatric patients with wrist trauma. The experimental results show that YOLOv8 algorithm models have different advantages for different model sizes, with YOLOv8l model achieving the highest mean average precision (mAP 50) of 63.6\%, and YOLOv8n model achieving the inference time of 67.4ms per X-ray image on one single CPU with low computing power. In this way, we create "Fracture Detection Using YOLOv8 App" to assist surgeons in interpreting X-ray images without the help of radiologists. Our implementation code is released at https://github.com/RuiyangJu/Bone_Fracture_Detection_YOLOv8

Deep learning assisted diagnosis system: improving the diagnostic accuracy of distal radius fractures

ObjectivesTo explore an intelligent detection technology based on deep learning algorithms to assist the clinical diagnosis of distal radius fractures (DRFs), and further compare it with human performance to verify the feasibility of this method.MethodsA total of 3,240 patients (fracture: n = 1,620, normal: n = 1,620) were included in this study, with a total of 3,276 wrist joint anteroposterior (AP) X-ray films (1,639 fractured, 1,637 normal) and 3,260 wrist joint lateral X-ray films (1,623 fractured, 1,637 normal). We divided the patients into training set, validation set and test set in a ratio of 7:1.5:1.5. The deep learning models were developed using the data from the training and validation sets, and then their effectiveness were evaluated using the data from the test set. Evaluate the diagnostic performance of deep learning models using receiver operating characteristic (ROC) curves and area under the curve (AUC), accuracy, sensitivity, and specificity, and compare them with medical professionals.ResultsThe deep learning ensemble model had excellent accuracy (97.03%), sensitivity (95.70%), and specificity (98.37%) in detecting DRFs. Among them, the accuracy of the AP view was 97.75%, the sensitivity 97.13%, and the specificity 98.37%; the accuracy of the lateral view was 96.32%, the sensitivity 94.26%, and the specificity 98.37%. When the wrist joint is counted, the accuracy was 97.55%, the sensitivity 98.36%, and the specificity 96.73%. In terms of these variables, the performance of the ensemble model is superior to that of both the orthopedic attending physician group and the radiology attending physician group.ConclusionThis deep learning ensemble model has excellent performance in detecting DRFs on plain X-ray films. Using this artificial intelligence model as a second expert to assist clinical diagnosis is expected to improve the accuracy of diagnosing DRFs and enhance clinical work efficiency

Pediatric radius torus fractures in x-rays—how computer vision could render lateral projections obsolete

It is an indisputable dogma in extremity radiography to acquire x-ray studies in at least two complementary projections, which is also true for distal radius fractures in children. However, there is cautious hope that computer vision could enable breaking with this tradition in minor injuries, clinically lacking malalignment. We trained three different state-of-the-art convolutional neural networks (CNNs) on a dataset of 2,474 images: 1,237 images were posteroanterior (PA) pediatric wrist radiographs containing isolated distal radius torus fractures, and 1,237 images were normal controls without fractures. The task was to classify images into fractured and non-fractured. In total, 200 previously unseen images (100 per class) served as test set. CNN predictions reached area under the curves (AUCs) up to 98% [95% confidence interval (CI) 96.6%–99.5%], consistently exceeding human expert ratings (mean AUC 93.5%, 95% CI 89.9%–97.2%). Following training on larger data sets CNNs might be able to effectively rule out the presence of a distal radius fracture, enabling to consider foregoing the yet inevitable lateral projection in children. Built into the radiography workflow, such an algorithm could contribute to radiation hygiene and patient comfort

Wrist Fractures Analysis as observed with X-ray imaging

This thesis studies wrist fractures seen on radiographs. Wrist radiographs are anal ysed by two different approaches; first by traditional image processing to extract geometric measurements, then by deep learning to classify risks as normal or abnormal (i.e. fractures or implants). Two data sets are used. The first data set includes wrist radiographs obtained from the Department of Radiology at the University of Exeter. The second data set corresponds to MURA X-ray images (MUsculoskeletal RAdiographs) obtained by the Stanford Machine Learning Team. The MURA data set provides more X-ray images to explore than the first data set. In the first task, a semi-automated geometric image analysis algorithm is proposed to analyse and compare the radiographs of healthy controls and patients with wrist fractures treated by Manipulation under Anaesthesia (MuA). The first dataset was used in this task. Thirty-two geometric and texture measurements were created. Image texture emerged as a metric of the most distinct geometric features from wrist X-rays associated with fractures. In the second task, eleven pre-trained convolutional neural network (CNN) architectures were used. CNN classified the MURA data set into normal and abnormal categories. Transfer learning technique applied to all eleven pre-trained CNNs to deal with wrist X-ray datasets. ResNet-50 and Inception-ResNet-V2 were then explored further using data augmentation strategies. Transfer learning techniques and data augmentation strategies greatly enhance CNN’s ability to classify wrist X-ray images. Class activation mapping (CAM) explores the convolutional neural network’s activation associated with the abnormality within the wrist X-ray image. It shows that CAM can indicate the abnormality area in the wrist’s X-ray image. The graphical heatmap of CAM overlaid on the wrist X-ray image marks the visual point of the area that triggers the CNN’s decision