Deep learning in the radiologic diagnosis of osteoporosis: a literature review

被引:3
作者
He, Yu [1 ]
Lin, Jiaxi [2 ]
Zhu, Shiqi [2 ]
Zhu, Jinzhou [2 ]
Xu, Zhonghua [3 ]
机构
[1] Soochow Univ, Suzhou Med Coll, Suzhou, Jiangsu, Peoples R China
[2] Soochow Univ, Affiliated Hosp 1, Dept Gastroenterol, Suzhou, Jiangsu, Peoples R China
[3] Jiangsu Univ, Jintan Affiliated Hosp, Dept Orthoped, Changzhou 213200, Peoples R China
关键词
Osteoporosis; screening; deep learning; bone mineral density; literature review; convolutional neural network; BONE-MINERAL DENSITY; FRACTURE RISK-ASSESSMENT; X-RAY; NEURAL-NETWORK; RADIOGRAPHS; PREDICTION; IDENTIFICATION; OSTEOPENIA; MODEL;
D O I
10.1177/03000605241244754
中图分类号
R-3 [医学研究方法]; R3 [基础医学];
学科分类号
1001 ;
摘要
Objective 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.Methods 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.Results 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).Conclusions Deep learning has demonstrated a remarkable capacity for osteoporosis screening. However, clinical commercialization of a diagnostic model for osteoporosis remains a challenge.
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页数:18
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