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

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).

A single-sided magnet for deep-depth fat quantification.

Early detection of fatty-liver disease is important before further aggravations of the disease, such as cirrhosis, can develop. In this study, we developed a low-cost, movable single-sided magnet for in vivo liver fat quantification. A gradient field of 73.5 G/cm and a field strength of 0.0725 T were obtained by structurally optimizing the concave U-shaped magnet, on which the region of interest (ROI) was a curved shape about 0.4 mm thick, 8 cm above the surface of the radiofrequency (RF) coil. We constructed a prototype nuclear magnetic-resonance (NMR) relaxometry system based on this optimized magnet. Subsequent phantom experiments demonstrated the effectiveness of the single-sided magnet in evaluating different proton density fat fraction (PDFF) phantoms. As expected, the results of the six phantoms showed good positive correlation between PDFF and the fitted fat amplitude, which suggested that single-sided NMR relaxometry could be used to quantify liver fat in vivo.