Machine learning algorithms for diagnosis of hip bone osteoporosis: a systematic review and meta-analysis study

doi:10.1186/s12938-023-01132-9

	Machine learning algorithms for diagnosis of hip bone osteoporosis: a systematic review and meta-analysis study
	Rahim, Fakher 1; Zadeh, Amin Zaki 2; Javanmardi, Pouya 3; Komolafe, Temitope Emmanuel4 ; Khalafi, Mohammad 5; Arjomandi, Ali 3; Ghofrani, Haniye Alsadat 3; Shirbandi, Kiarash 6
	2023-12
发表期刊	BIOMEDICAL ENGINEERING ONLINE
EISSN	1475-925X
卷号	22 期号:1
发表状态	已发表
DOI	10.1186/s12938-023-01132-9
摘要	Background: Osteoporosis is a significant health problem in the skeletal system, associated with bone tissue changes and its strength. Machine Learning (ML), on the other hand, has been accompanied by improvements in recent years and has been in the spotlight. This study is designed to investigate the Diagnostic Test Accuracy (DTA) of ML to detect osteoporosis through the hip dual-energy X-ray absorptiometry (DXA) images. Methods: The ISI Web of Science, PubMed, Scopus, Cochrane Library, IEEE Xplore Digital Library, CINAHL, Science Direct, PROSPERO, and EMBASE were systematically searched until June 2023 for studies that tested the diagnostic precision of ML model-assisted for predicting an osteoporosis diagnosis. Results: The pooled sensitivity of univariate analysis of seven studies was 0.844 (95% CI 0.791 to 0.885, I 2 = 94% for 7 studies). The pooled specificity of univariate analysis was 0.781 (95% CI 0.732 to 0.824, I 2 = 98% for 7 studies). The pooled diagnostic odds ratio (DOR) was 18.91 (95% CI 14.22 to 25.14, I 2 = 93% for 7 studies). The pooled mean positive likelihood ratio (LR+) and the negative likelihood ratio (LR−) were 3.7 and 0.22, respectively. Also, the summary receiver operating characteristics (sROC) of the bivariate model has an AUC of 0.878. Conclusion: Osteoporosis can be diagnosed by ML with acceptable accuracy, and hip fracture prediction was improved via training in an Architecture Learning Network (ALN). © 2023, The Author(s).
关键词	Bone Diagnosis Digital libraries Diseases Learning algorithms Bone disease Hip Likelihood ratios Lower extremity Machine learning algorithms Machine-learning Meta-analysis Metabolic Osteoporosis Univariate analysis
URL	查看原文
收录类别	EI ; SCI
语种	英语
WOS研究方向	Engineering
WOS类目	Engineering, Biomedical
WOS记录号	WOS:001025840100002
出版者	BioMed Central Ltd
EI入藏号	20232814376311
EI主题词	Machine learning
EI分类号	461.2 Biological Materials and Tissue Engineering ; 461.6 Medicine and Pharmacology ; 723.4 Artificial Intelligence ; 723.4.2 Machine Learning ; 723.5 Computer Applications ; 903.4.1 Libraries
原始文献类型	Journal article (JA)
引用统计
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/316857
专题	生物医学工程学院生物医学工程学院_PI研究组_沈定刚组
通讯作者	Khalafi, Mohammad; Shirbandi, Kiarash
作者单位	1.Cihan Univ Sulaimaniya, Dept Anesthesia, Sulaymaniyah, Kurdistan Regio, Iraq 2.Ahvaz Jondishapour Univ Med Sci, Sch Med, Ahvaz, Iran 3.Ahvaz Jundishapur Univ Med Sci, Fac Paramed, Dept Radiol Technol, Ahvaz, Iran 4.ShanghaiTech Univ, Sch Biomed Engn, Shanghai 201210, Peoples R China 5.Tabriz Univ Med Sci, Sch Med, Tabriz, Iran 6.Univ Tehran Med Sci, Res Ctr Mol & Cellular Imaging, Tehran, Iran
推荐引用方式 GB/T 7714	Rahim, Fakher,Zadeh, Amin Zaki,Javanmardi, Pouya,et al. Machine learning algorithms for diagnosis of hip bone osteoporosis: a systematic review and meta-analysis study[J]. BIOMEDICAL ENGINEERING ONLINE,2023,22(1).
APA	Rahim, Fakher.,Zadeh, Amin Zaki.,Javanmardi, Pouya.,Komolafe, Temitope Emmanuel.,Khalafi, Mohammad.,...&Shirbandi, Kiarash.(2023).Machine learning algorithms for diagnosis of hip bone osteoporosis: a systematic review and meta-analysis study.BIOMEDICAL ENGINEERING ONLINE,22(1).
MLA	Rahim, Fakher,et al."Machine learning algorithms for diagnosis of hip bone osteoporosis: a systematic review and meta-analysis study".BIOMEDICAL ENGINEERING ONLINE 22.1(2023).