Introduction:
Machine learning is always coordinated with artificial intelligence, which uses computer algorithms to make various predictions and classifications based on the acquired data. In recent years, machine learning and artificial intelligence have been rapidly used in orthopedic research. Machine learning is used for various domains in orthopedic surgeries as a radiographic diagnostic tool, for identification of the implants, for analyzing gait, and for predicting patient outcomes. As it has more advantages in orthopedic surgeries, it also has many limitations to use in clinical practice in daily life. Performing future works can overcome the limitations and enable the machine learning technique as a useful tool in orthopedic surgeries.
What Is Machine Learning?
Machine learning is a subdivision of artificial intelligence. It is considered the most tangible manifestation of artificial intelligence. Machine learning in orthopedics helps in fracture detection, detecting loose hip implants, grading osteoarthritis, and diagnosing bone tumors. The purpose of machine learning and artificial intelligence has been expanding as time goes on. Machine learning mainly focuses on forming automated computer systems that help in predicting outcomes through data and mathematics. Various machine learning algorithms extract information from tabulated data. Some of them include random forests, linear regression, nearest neighbor, decision trees, k-means clustering, and support vector machine (SVM). In addition to this, two data are recently developed to extract information from imaging data. They may be DL algorithms and artificial neural networks (ANN). Machine learning can be achieved by three methods: reinforcement, supervised, and unsupervised learning.
What Are the Types of Machine Learning?
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The main common type of machine learning is supervised machine learning. It is also called inductive learning. It happens when the data is fixed to tell the system exactly what pattern it should look for. To say that machine learning is involved in detecting arthritis of the knee using radiographs, the features of arthritis should be entered manually along with the features of normal knees. It is more commonly used as it takes less time to learn.
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Another common type of machine learning is unsupervised machine learning. It is also called analytic learning or deductive learning. It happens without labeling the data, and the machine needs to look for the patterns.
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The last type is reinforcement learning. It is involved in getting feedback from the system after the task has been completed.
What Are the Programmer Used in Machine Learning?
Machine learning uses various algorithms in orthopedic surgeries. The mainly used two algorithms are
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DL algorithms.
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ANN (artificial neural network).
Along with this, two familiar models of deep learning accompanied by ANN are convolutional neural networks (CNN) and recurrent neural networks. The major functions of convolutional neural networks are to extract apt features from imaging and classify it.
What Is the Risk Assessment of Machine Learning in Orthopedics?
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Machine learning has been used in the early years for safe drug prescriptions. In recent years, machine learning and DL algorithms have focused on decision-making in clinical practices for assessing the risks and other complications.
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It is studied that machine learning models are used in predicting mortality rate, cardiac complications, wound complications, posterior lumbar fusion, and in venous thromboembolism.
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It is also studied that machine learning was used in predicting thirty-day morbidity and mortality after a total joint arthroplasty.
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One of the disadvantages is that machine learning lacks in predicting deep infections and re-operation.
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In another study, the researchers used supervised learning to predict the postsurgical outcomes after total shoulder arthroplasty. It also illustrates that machine learning has performed in predicting transfusion, surgical site, infection, and readmission.
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Along with DL algorithms, it is studied that machine learning was used to predict the likelihood of amputation under 155 combat-related open calcaneal fractures.
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It was also studied that machine learning is involved in identifying safe persons for undergoing certain orthopedic surgeries like discectomy and cervical fusion.
What Is the Role of Machine Learning in Imaging Applications?
The different imaging techniques that are used in orthopedic surgeries include MRI (magnetic resonance imaging), conventional radiographs, and CT (computed tomography). Machine learning and artificial intelligence majorly work with the use of imaging tests. Machine learning using imaging results is mainly used in spine, arthritis, oncology, and trauma.
Spinal Cord
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Research has shown that CT scans use machine learning using neural networks and k- means to predict lumbar wedge fractures. Machine learning is used to predict issues in the various parts of the spinal cord.
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Machine learning can also be used to detect the accurate vertebral level for percutaneous spinal needle injections.
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It can also be used in detecting degenerative changes of the spinal cords, like central canal stenosis and spondylolisthesis. It is also used to identify disc spaces.
Arthritis
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The common type of arthritis is osteoarthritis. It is caused due to degeneration of articular cartilage.
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Studies have shown that machine learning may accurately diagnose osteoarthritis of the hip by using deep learning and CNN.
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Machine learning is used in optimally diagnosing osteoarthritis of the knees and hip.
Trauma
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For detecting fractures, machine learning tools can be used in imaging techniques.
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Studies show that the use of machine learning tools in imaging techniques may predict hip fractures using X-ray absorptiometry.
Oncology
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Machine learning is used to manage metastatic bone diseases, primarily focusing on bone fractures.
What Are the Basic Medical Applications of Machine Learning?
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In the early days, machine learning was used to predict the chemical properties of proteins and drugs and vaccine immunogenicity.
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In current years, machine learning and artificial intelligence have been applied to basic concepts like gait analysis, kinetics, implant design, and wearable technology.
Conclusion:
Machine learning has been evolving and expanding in orthopedics research. It is effectively used in the detection of spine pathologies, gait classifications, osteoarthritis detection, prosthesis control, and fracture detection. As machine learning has diagnostic and prognostic uses, it can be continuously used in orthopedics treatments.
