scholarly journals Modified deep learning ResNeXt model for Human long bone fracture detection and classification

2021 ◽  
Author(s):  
Ashish Sharma ◽  
◽  
D. P. Yadav ◽  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Bone Fractures ◽  
Healing Process ◽  
Medical Science ◽  
Texture Features ◽  
Long Bone ◽  
Fracture Detection ◽  
X Ray Imaging ◽  
Automated Technique

The field of medical science is going to take advantage of Machine learning. It has increased dramatically over the last decade. Nowadays, you can see other innovations used in medical sciences, such as machine learning and deep learning. They can help to diagnose the illness or cause. It can also aid in the healing process by keeping notes. At a similar pace, an upper hand has been provided to the physicians for image processing by incorporating computers. Bone fractures are normal these days, and the identification of fractures is a critical part of orthopedic X-ray imaging. The automated technique lets the doctor quickly begin medical treatment. Using Machine Learning and CNN (Convolutional Neural Network), we suggest a new deep learning model perform bone diagnosis by eliminating discontinuity followed by segmentation of the image in a system that detects bone fractures. It overcomes the shortcomings of the previous approach that operates only on examination of the texture features. The proposed deep learning modified ResNeXt model performs much better than the state-of arts.

scholarly journals A Novel Capacitive Measurement Device for Longitudinal Monitoring of Bone Fracture Healing

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6694
Author(s):  
Angela Sorriento ◽  
Marcello Chiurazzi ◽  
Luca Fabbri ◽  
Michelangelo Scaglione ◽  
Paolo Dario ◽  
...  
Keyword(s):  
Bone Fractures ◽  
Healing Process ◽  
Long Bone ◽  
Sensor Technology ◽  
External Fixators ◽  
X Rays ◽  
Implant Stability ◽  
Measurement Device ◽  
3D Space ◽  
Bone Callus

The healing process of surgically-stabilised long bone fractures depends on two main factors: (a) the assessment of implant stability, and (b) the knowledge of bone callus stiffness. Currently, X-rays are the main diagnostic tool used for the assessment of bone fractures. However, they are considered unsafe, and the interpretation of the clinical results is highly subjective, depending on the clinician’s experience. Hence, there is the need for objective, non-invasive and repeatable methods to allow a longitudinal assessment of implant stability and bone callus stiffness. In this work, we propose a compact and scalable system, based on capacitive sensor technology, able to measure, quantitatively, the relative pins displacements in bone fractures treated with external fixators. The measurement device proved to be easily integrable with the external fixator pins. Smart arrangements of the sensor units were exploited to discriminate relative movements of the external pins in the 3D space with a resolution of 0.5 mm and 0.5°. The proposed capacitive technology was able to detect all of the expected movements of the external pins in the 3D space, providing information on implant stability and bone callus stiffness.

scholarly journals Correlating mRUST scoring with fracture healing in the mouse model

2020 ◽  
Vol 3 ◽  
Author(s):  
Anthony McGuire ◽  
Adam Knox ◽  
Caio de Andrade Staut ◽  
Melissa Kacena ◽  
Roman Natoli ◽  
...  
Keyword(s):  
Mouse Model ◽  
Fracture Healing ◽  
Bone Fractures ◽  
Healing Process ◽  
Fracture Repair ◽  
Long Bone ◽  
Post Surgery ◽  
Method Of Assessment ◽  
Non Destructive

Background/Objective: Long bone fractures are an expensive and frequent cause of disability in humans. Research seeking to accelerate and improve the healing process is more essential than ever. Animal models, mice especially, provide an inexpensive and reproducible model of in vivo fracture healing. However, many measures of murine fracture healing outcomes are either expensive or destructive, limiting their ability to be translated to clinical studies. We seek to determine how these measures such as biomechanics, µCT, and histology correlate to the relatively new, inexpensive, and non-destructive method of mRUST scoring in a mouse model.  Methods: One hundred and thirty-five, 12-week old male C57BL6/J mice were divided into nine groups of 15 mice. Mice underwent a surgically created, femoral fracture. At biweekly timepoints, anteroposterior and lateral radiographs were taken, and 15 mice were sacrificed at each time point (7, 10, 14, 17, 21, 24, 28, 35, and 42 days post-surgery) for biomechanical, µCT, and histological analyses. The modified Radiographic Union Scale for Tibial fractures (mRUST scoring) provides a score based on the visualization of a callus and fracture line in four cortices on the radiographs. Data analysis will be performed to determine the degree of correlation between mRUST scoring and other fracture healing outcomes.  Results/Conclusion: Data collection in this experiment is still forthcoming. Upon successful completion of this project, we will have established numerical correlations between mRUST scoring and other fracture healing outcomes, such as biomechanics, µCT microarchitecture, and histology. These correlations will provide a powerful tool in future mouse fracture healing studies, as data on the state and strength of fracture repair could be determined by simple radiograph.  Scientific/Clinical Policy Impact and Implications: This study will both provide future murine fracture studies with an inexpensive and non-destructive method of assessment that is more directly translatable to human fracture studies. 

scholarly journals An Introduction to IoT Based Posture Detection and Underlying Sensor Technology

2020 ◽  
Author(s):  
Jahnvi Gupta ◽  
Nitin Gupta ◽  
Mukesh Kumar ◽  
Ritwik Duggal
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Deep Learning ◽  
Patient Monitoring ◽  
Elderly Care ◽  
Medical Science ◽  
Sensor Technology ◽  
Human Posture ◽  
Posture Recognition ◽  
Selection For

Analysis of human posture has many applications in the field of sports and medical science including patient monitoring, lifestyle analysis, elderly care etc. Many of the works in this area have been based on computer vision techniques. These are limited in providing real-time solution. Thus, Internet of Things (IoT) based solution are being planned and used for the human posture recognition and detection. The data collected from sensors is then passed to machine learning or deep learning algorithms to find different patterns. In this chapter an introduction to IoT based posture detection is provided with an introduction to underlying sensor technology, which can help in selection for appropriate sensors for the posture detection.<br>

Effect of xenogenic cortical bone on fracture healing in a canine fracture model

2013 ◽  
Vol 61 (2) ◽  
pp. 149-159
Author(s):  
Suyoung Heo ◽  
Kyoungmin So ◽  
Sehoon Kim ◽  
Minsu Kim ◽  
Haebeom Lee ◽  
...  
Keyword(s):  
Cortical Bone ◽  
Bone Healing ◽  
Bone Fractures ◽  
Healing Process ◽  
Fracture Repair ◽  
Host Bone ◽  
Long Bone ◽  
Micro Ct ◽  
Group A ◽  
Group B

The purpose of this study was to investigate the effect of xenogenic cortical bone (XCB) on fracture repair in the canine ulna. The entire group of animals (n = 12) had a transverse resection of 5 mm length at the middle part of the right ulnar diaphysis. In Group A (eight beagles), the fracture was treated with XCB and metal bone screw. In Group B (four beagles), the fracture was treated with metal bone plate and screw. Radiological, micro-computed tomography (micro-CT), histological examination and mechanical testing were employed to evaluate bone healing and reaction of XCB in the host bone. In Group A, bone union was noticed in 6 out of 8 dogs (75%), starting from the 4th week onwards. Micro-CT and histological examinations showed that the XCB was absorbed and incorporated into the host bone. Incorporation of XCB was observed in 7 cases (88%); it started from the 10th week onwards and continued to week 32 after surgery. Biomechanical strength of the bone fracture site was higher in Group A than in Group B, and was similar to that of normal bone. XCB enhances the bone healing process and can be used as absorbable internal fixation for the management of long bone fractures in dogs.

scholarly journals Design of a Fracture Detection System based on Deep Program in a Convolutional Neural Network

Webology ◽  
2021 ◽  
Vol 18 (2) ◽  
pp. 509-518
Author(s):  
Payman Hussein Hussan ◽  
Syefy Mohammed Mangj Al-Razoky ◽  
Hasanain Mohammed Manji Al-Rzoky
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Bone Fractures ◽  
Detection System ◽  
Training Data ◽  
Learning Models ◽  
Fracture Detection ◽  
Data Set ◽  
Final Fracture

This paper presents an efficient method for finding fractures in bones. For this purpose, the pre-processing set includes increasing the quality of images, removing additional objects, removing noise and rotating images. The input images then enter the machine learning phase to detect the final fracture. At this stage, a Convolutional Neural Networks is created by Genetic Programming (GP). In this way, learning models are implemented in the form of GP programs. And evolve during the evolution of this program. Then finally the best program for classifying incoming images is selected. The data set in this work is divided into training and test friends who have nothing in common. The ratio of training data to test is equal to 80 to 20. Finally, experimental results show good results for the proposed method for bone fractures.

scholarly journals An Introduction to IoT Based Posture Detection and Underlying Sensor Technology

2020 ◽  
Author(s):  
Jahnvi Gupta ◽  
Nitin Gupta ◽  
Mukesh Kumar ◽  
Ritwik Duggal
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Deep Learning ◽  
Patient Monitoring ◽  
Elderly Care ◽  
Medical Science ◽  
Sensor Technology ◽  
Human Posture ◽  
Posture Recognition ◽  
Selection For

Analysis of human posture has many applications in the field of sports and medical science including patient monitoring, lifestyle analysis, elderly care etc. Many of the works in this area have been based on computer vision techniques. These are limited in providing real-time solution. Thus, Internet of Things (IoT) based solution are being planned and used for the human posture recognition and detection. The data collected from sensors is then passed to machine learning or deep learning algorithms to find different patterns. In this chapter an introduction to IoT based posture detection is provided with an introduction to underlying sensor technology, which can help in selection for appropriate sensors for the posture detection.<br>

FEA Analysis of a Portable Knee Rehabilitation Device Using Mechanical Loading

2012 ◽  
Author(s):  
Daric Fitzwater ◽  
Andrew Rophie ◽  
Benjamin Schroeder ◽  
Andrew Dole ◽  
Juan Solano ◽  
...  
Keyword(s):  
Bone Fractures ◽  
Healing Process ◽  
Long Bone ◽  
The Finite Element Method ◽  
Engineering Research ◽  
Knee Rehabilitation ◽  
Research Initiative ◽  
Knee Loading ◽  
Fea Analysis ◽  
Ansys Workbench

In this paper, a solid model has been created with CAD software and analyzed with FEA software to obtain the deformed geometry, stress distribution, modal frequencies, temperature distribution, and life expectancy of a knee loading device that will be used in a combined biomedical and mechanical engineering research initiative. The purpose of this device is to mechanically load the end of the long bone of the human leg, causing movement of the fluids within the bone that can stimulate increased growth of bone tissues. This could potentially be used to speed the healing process of bone fractures. The CAD model of the device was constructed in Pro/ENGINEER and then exported to ANSYS Workbench where it was then meshed and solved using the finite element method.

scholarly journals Classification of Watermelon Seeds Using Morphological Patterns of X-ray Imaging: A Comparison of Conventional Machine Learning and Deep Learning

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6753
Author(s):  
Mohammed Raju Ahmed ◽  
Jannat Yasmin ◽  
Eunsung Park ◽  
Geonwoo Kim ◽  
Moon S. Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Discriminant Analysis ◽  
Transfer Learning ◽  
Image Features ◽  
Linear Discriminant ◽  
X Ray ◽  
X Ray Imaging ◽  
Conventional Machine ◽  
Viable Seeds

In this study, conventional machine learning and deep leaning approaches were evaluated using X-ray imaging techniques for investigating the internal parameters (endosperm and air space) of three cultivars of watermelon seed. In the conventional machine learning, six types of image features were extracted after applying different types of image preprocessing, such as image intensity and contrast enhancement, and noise reduction. The sequential forward selection (SFS) method and Fisher objective function were used as the search strategy and feature optimization. Three classifiers were tested (linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and k-nearest neighbors algorithm (KNN)) to find the best performer. On the other hand, in the transfer learning (deep learning) approaches, simple ConvNet, AlexNet, VGG-19, ResNet-50, and ResNet-101 were used to train the dataset and class prediction of the seed. For the supervised model development (both conventional machine learning and deep learning), the germination test results of the samples were used where the seeds were divided into two classes: (1) normal viable seeds and (2) nonviable and abnormal viable seeds. In the conventional classification, 83.6% accuracy was obtained by LDA using 48 features. ResNet-50 performed better than other transfer learning architectures, with an 87.3% accuracy which was the highest accuracy in all classification models. The findings of this study manifested that transfer learning is a constructive strategy for classifying seeds by analyzing their morphology, where X-ray imaging can be adopted as a potential imaging technique.

scholarly journals Using machine learning methods in problems with large amounts of data

2021 ◽  
Vol 116 ◽  
pp. 00080
Author(s):  
Olga Kuimova ◽  
Vladislav Kukartsev ◽  
Artem Stupin ◽  
Ekaterina Markevich ◽  
Stanislav Apanasenko
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Medical Science ◽  
Modern Medicine ◽  
Clustering Methods ◽  
Learning Mechanisms ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Manual Processing ◽  
Classification Of Images

This article explores the use of artificial intelligence in medicine, in particular in radiology, pathology, drug development. The usefulness of robotic assistants in the medical field is revealed, including machine learning in medical science, as well as routing in hospitals. It also discusses such machine learning methods as classification methods, regression restoration methods, clustering methods. As a result, based on what is considered in this article, it is concluded that manual processing becomes more complicated and impossible with a large amount of data. There is a need for automatic processing that can transform modern medicine. And also, conclusions were made about how accurately the deep learning mechanisms can provide a more accurate result in the processing and classification of images compared to the results obtained at the human level. It became clear that deep learning not only aids in the selection and extraction of characteristics, but also has the potential to measure predictive target audiences and provide proactive predictions to help clinicians go a long way.

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