Deep learning in the radiologic diagnosis of osteoporosis: a literature review.
J Int Med Res
; 52(4): 3000605241244754, 2024 Apr.
Article
em En
| MEDLINE
| ID: mdl-38656208
ABSTRACT
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.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Osteoporose
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Imageamento por Ressonância Magnética
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Tomografia Computadorizada por Raios X
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Densidade Óssea
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Aprendizado Profundo
Limite:
Humans
Idioma:
En
Revista:
J Int Med Res
/
J. int. med. res
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Journal of international medical research
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
China