Artificial Intelligence Algorithms for Bone Mineral Density Prediction Based on DEXA-DICOM and CT-Images: A Review Approaches

Abstract

Bone Mineral Density (BMD) is a key indicator in the diagnosis of osteoporosis and assessment of potential fractures, which are important for maintaining public health. Medical imaging techniques such as Dual-Energy X-ray Absorptiometry (DEXA) and Computed Tomography (CT) are utilized for the evaluation of BMD. The integration of Artificial Intelligence (AI) with DEXA and CT techniques has significantly improved their accuracy, efficiency, and automation. This study concentrates on AI algorithms such as machine learning (ML) and deep learning (DL) in the analysis of DEXA-DICOM and CT images to predict BMD. Particular emphasis is placed on the characteristics of AI in enhancing decision-making, optimizing resource utilization, and addressing challenges within healthcare systems. DL models, especially convolutional neural networks (CNNs), have demonstrated promising performance in predicting BMD. This review analyzes the literature dealing with the use of AI for osteoporosis diagnosis, discusses the issues of a lack of uniformity, data collection, and the ethical dimensions of the application of AI in healthcare. Furthermore, determines as well as the efforts still necessary to make the use of AI in healthcare automated, reliable, and ethically justifiable.

Ähnliche Arbeiten