He Yu, Lin Jiaxi, Zhu Shiqi, Zhu Jinzhou, Xu Zhonghua
Suzhou Medical College, Soochow University, Suzhou, Jiangsu, China.
Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China.
J Int Med Res. 2024 Apr;52(4):3000605241244754. doi: 10.1177/03000605241244754.
Osteoporosis is a systemic bone disease characterized by low bone mass, damaged bone microstructure, increased bone fragility, and susceptibility to fractures. With the rapid development of artificial intelligence, a series of studies have reported deep learning applications in the screening and diagnosis of osteoporosis. The aim of this review was to summary the application of deep learning methods in the radiologic diagnosis of osteoporosis.
We conducted a two-step literature search using the PubMed and Web of Science databases. In this review, we focused on routine radiologic methods, such as X-ray, computed tomography, and magnetic resonance imaging, used to opportunistically screen for osteoporosis.
A total of 40 studies were included in this review. These studies were divided into three categories: osteoporosis screening (n = 20), bone mineral density prediction (n = 13), and osteoporotic fracture risk prediction and detection (n = 7).
Deep learning has demonstrated a remarkable capacity for osteoporosis screening. However, clinical commercialization of a diagnostic model for osteoporosis remains a challenge.
骨质疏松症是一种全身性骨病，其特征为骨量低、骨微结构受损、骨脆性增加以及易发生骨折。随着人工智能的快速发展，一系列研究报道了深度学习在骨质疏松症筛查和诊断中的应用。本综述的目的是总结深度学习方法在骨质疏松症放射学诊断中的应用。
我们使用PubMed和Web of Science数据库进行了两步文献检索。在本综述中，我们重点关注用于机会性筛查骨质疏松症的常规放射学方法，如X线、计算机断层扫描和磁共振成像。
本综述共纳入40项研究。这些研究分为三类：骨质疏松症筛查（n = 20）、骨密度预测（n = 13）以及骨质疏松性骨折风险预测与检测（n = 7）。
深度学习在骨质疏松症筛查方面已展现出显著能力。然而，骨质疏松症诊断模型的临床商业化仍然是一项挑战。
