Using Artificial Intelligence Algorithms to Recognize Osteoporosis: A Review
DOI:
https://doi.org/10.26555/jiteki.v10i4.30352Keywords:
Osteoporosis, Machine Learning, Artificial Neural Network, Deep Learning, Convolutional neural network, Density of Bone Minerals (BMD), Bone Density Test (DEXA)Abstract
Osteoporosis is a silent disease that usually occurs due to bone mineral deficiency (BMD), which leads to increased bone porosity, thus weakening the bones and increasing their porosity, which increases the risk of fractures in those with this disease. Bone porosity is defined as an increase in internal spaces in the bone structure, which reduces its density and strength and makes it more susceptible to fractures. Many parts of the skeleton are exposed to osteoporosis, such as the hip, thigh, jaw, knee, forearm, spine, and others. The incidence of osteoporosis increases in the elderly, and women are more affected by it than men. There are also other factors such as genetic predisposition and lifestyle. The use of artificial intelligence-based technical programs has received wide attention in the medical field to diagnose and classify various medical images, such as images of cancerous tumors, arthritis, osteoporosis, and others, as artificial intelligence provides accurate and rapid tools for the early detection of osteoporosis through the analysis of medical images, outperforming traditional methods, which improves treatment opportunities and reduces diagnostic costs. However, these techniques face challenges such as algorithmic bias and the need for diverse databases to ensure a balanced assessment of different cases.In addition, despite the advances in computer technologies for the early detection of osteoporosis, the disease remains a challenge in healthcare due to the absence of clear symptoms until fractures occur, the difficulty of early detection, the variability in disease progression, and the need for personalized treatment plans, which leads to increased mortality. The paper presents a review of studies that have addressed osteoporosis in skeletal parts such as the knee, spine, hip, and teeth. It also reviews the techniques and methods used in diagnosis, with a focus on the role of artificial intelligence in improving accuracy and speed of detection. The review shows how deep learning algorithms, especially convolutional neural networks (CNNs),have been effectively used to classify osteoporosis through the results and achieve high accuracy rates in different studies.
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