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

Abstract

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, I² = 94% for 7 studies). The pooled specificity of univariate analysis was 0.781 (95% CI 0.732 to 0.824, I² = 98% for 7 studies). The pooled diagnostic odds ratio (DOR) was 18.91 (95% CI 14.22 to 25.14, I² = 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).

Keywords: Artificial intelligence; Bone diseases; Hip; Lower extremity; Machine learning; Meta-analysis; Metabolic; Osteoporosis.

Fig. 1

Study flow diagram showing how to extract articles

Fig. 2

A Risk of bias and applicability concerns graph; review authors’ judgments about each domain presented as percentages across included studies. B Risk of bias and applicability concerns summary; review authors' judgments about each domain for each included study

Fig. 3

Funnel plot showing the low likelihood of publication bias in all included studies

Fig. 4

Univariate sub-group analysis of sensitivity with random model based on Network Architecture. G represents sub-group analysis of data, when g = 0 (ANN), g = 1 (SVM), g = 2 (RF), g = 3 (KNN), g = 4 (LR), and g = 5 (DT)

Fig. 5

Univariate sub-group analysis of specificity with random model based on Network Architecture. G represents sub-group analysis of data, when g = 0 (ANN), g = 1 (SVM), g = 2 (RF), g = 3 (KNN), g = 4 (LR), and g = 5 (DT)

Fig. 6

Univariate sub-group analysis of DOR with random model based on Network Architecture. G represents sub-group analysis of data, when g = 0 (ANN), g = 1 (SVM), g = 2 (RF), g = 3 (KNN), g = 4 (LR), and g = 5 (DT)