Abstract
Osteolysis is one of the most prominent reasons of revision surgeries in total joint arthroplasty. This biological phenomenon is induced by wear particles and corrosion products that stimulate inflammatory biological response of surrounding tissues. The eventual responses of osteolysis are the activation of macrophages leading to bone resorption and prosthesis failure. Various factors are involved in the initiation of osteolysis from biological issues, design, material specifications, and model of the prosthesis to the health condition of the patient. Nevertheless, the factors leading to osteolysis are sometimes preventable. Changes in implant design and polyethylene manufacturing are striving to improve overall wear. Osteolysis is clinically asymptomatic and can be diagnosed and analyzed during follow-up sessions through various imaging modalities and methods, such as serial radiographic, CT scan, MRI, and image processing-based methods, especially with the use of artificial neural network algorithms. Deep learning algorithms with a variety of neural network structures such as CNN, U-Net, and Seg-UNet have proved to be efficient algorithms for medical image processing specifically in the field of orthopedics for the detection and segmentation of tumors. These deep learning algorithms can effectively detect and analyze osteolytic lesions well in advance during follow-up sessions in order to administer proper treatments before reaching a critical point. Osteolysis can be treated surgically or nonsurgically with medications. However, revision surgeries are the only solution for the progressive osteolysis. In this literature review, the underlying causes, mechanisms, and treatments of osteolysis are discussed with the main focus on the possible computer-based methods and algorithms that can be effectively employed for the detection of osteolysis.
Highlights
Osteolysis is a biological process initiated by induced particles at the interface of bone metal or bone cement of prosthetic implants which is radiographically manifested as linear endosteal radiolucencies or scalloped focal. is phenomenon in long-term results in bone loss, periprostatic fractures, and loosening of implants
The output of the network predicts finegrained label whose classification accuracy is considered by two structure branches simultaneously. Employing this artificial neural network model could effectively be used for the classification of osteolytic lesions, as the same obstacles are involved in the detection and classification of bone tumors, and the appearance of osteolytic lesions in CT images is very similar to that of bone tumors
Osteolysis is a progressive and biological reaction to particulate wear debris. It is the most common indication for revision surgeries after total joint arthroplasty in long-term reviews. e biological mechanisms leading to osteolytic lesions are only beginning to be understood
Summary
Osteolysis is a biological process initiated by induced particles at the interface of bone metal or bone cement of prosthetic implants which is radiographically manifested as linear endosteal radiolucencies or scalloped focal. is phenomenon in long-term results in bone loss, periprostatic fractures, and loosening of implants. Regardless of imaging techniques, detection and analysis of osteolysis are difficult tasks to perform as osteolytic lesions are not easy to be distinguished especially at the first years after implantation. E advantageous feature of CNNs is that they can learn directly useful image features and other structured data, whereas the task of feature extraction has been carried out by machine learning models or by hand before CNNs. ese types of neural network architecture proved to be powerful deep learning models in the field of image analysis, judging from the existence of specific features in their structure [20,21,22]. To give a guideline for the main factors and issues involved in this medical phenomenon and to introduce computer-based methods and algorithms especially in the field of artificial neural networks used successfully for similar purposes
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