Evaluation of fragility fracture risk using deep learning based on ultrasound radio frequency signal.

BACKGROUND: It was essential to identify individuals at high risk of fragility fracture and prevented them due to the significant morbidity, mortality, and economic burden associated with fragility fracture. The quantitative ultrasound (QUS) showed promise in assessing bone structure characteristics and determining the risk of fragility fracture. AIMS: To evaluate the performance of a multi-channel residual network (MResNet) based on ultrasonic radiofrequency (RF) signal to discriminate fragility fractures retrospectively in postmenopausal women, and compared it with the traditional parameter of QUS, speed of sound (SOS), and bone mineral density (BMD) acquired with dual X-ray absorptiometry (DXA). METHODS: Using QUS, RF signal and SOS were acquired for 246 postmenopausal women. An MResNet was utilized, based on the RF signal, to categorize individuals with an elevated risk of fragility fracture. DXA was employed to obtain BMD at the lumbar, hip, and femoral neck. The fracture history of all adult subjects was gathered. Analyzing the odds ratios (OR) and the area under the receiver operator characteristic curves (AUC) was done to evaluate the effectiveness of various methods in discriminating fragility fracture. RESULTS: Among the 246 postmenopausal women, 170 belonged to the non-fracture group, 50 to the vertebral group, and 26 to the non-vertebral fracture group. MResNet was competent to discriminate any fragility fracture (OR = 2.64; AUC = 0.74), Vertebral fracture (OR = 3.02; AUC = 0.77), and non-vertebral fracture (OR = 2.01; AUC = 0.69). After being modified by clinical covariates, the efficiency of MResNet was further improved to OR = 3.31-4.08, AUC = 0.81-0.83 among all fracture groups, which significantly surpassed QUS-SOS (OR = 1.32-1.36; AUC = 0.60) and DXA-BMD (OR = 1.23-2.94; AUC = 0.63-0.76). CONCLUSIONS: This pilot cross-sectional study demonstrates that the MResNet model based on the ultrasonic RF signal shows promising performance in discriminating fragility fractures in postmenopausal women. When incorporating clinical covariates, the efficiency of the modified MResNet is further enhanced, surpassing the performance of QUS-SOS and DXA-BMD in terms of OR and AUC. These findings highlight the potential of the MResNet as a promising approach for fracture risk assessment. Future research should focus on larger and more diverse populations to validate these results and explore its clinical applications.

NOD2 attenuates osteoarthritis via reprogramming the activation of synovial macrophages.

OBJECTIVE: Synovial inflammation, which precedes other pathological changes in osteoarthritis (OA), is primarily initiated by activation and M1 polarization of macrophages. While macrophages play a pivotal role in the inflammatory process of OA, the mechanisms underlying their activation and polarization remain incompletely elucidated. This study aims to investigate the role of NOD2 as a reciprocal modulator of HMGB1/TLR4 signaling in macrophage activation and polarization during OA pathogenesis. DESIGN: We examined NOD2 expression in the synovium and determined the impact of NOD2 on macrophage activation and polarization by knockdown and overexpression models in vitro. Paracrine effect of macrophages on fibroblast-like synoviocytes (FLS) and chondrocytes was evaluated under conditions of NOD2 overexpression. Additionally, the in vivo effect of NOD2 was assessed using collagenase VII induced OA model in mice. RESULTS: Expression of NOD2 was elevated in osteoarthritic synovium. In vitro experiments demonstrated that NOD2 serves as a negative regulator of HMGB1/TLR4 signaling pathway. Furthermore, NOD2 overexpression hampered the inflammatory paracrine effect of macrophages on FLS and chondrocytes. In vivo experiments revealed that NOD2 overexpression mitigated OA in mice. CONCLUSIONS: Supported by convincing evidence on the inhibitory role of NOD2 in modulating the activation and M1 polarization of synovial macrophages, this study provided novel insights into the involvement of innate immunity in OA pathogenesis and highlighted NOD2 as a potential target for the prevention and treatment of OA.

Osteoporosis Diagnosis Based on Ultrasound Radio Frequency Signal via Multi-channel Convolutional Neural Network.

Osteoporosis is a metabolic osteopathy syndrome, and the incidence of osteoporosis increases significantly with age. Currently, bone quantitative ultrasound (QUS) has been considered as a potential method for screening and diagnosing osteoporosis. However, its diagnostic accuracy is quite low. By contrast, deep learning based methods have shown the great power for extracting the most discriminative features from complex data. To improve the osteoporosis diagnostic accuracy and take advantages of QUS, we devise a deep learning method based on ultrasound radio frequency (RF) signal. Specifically, we construct a multi-channel convolutional neural network (MCNN) combined with a sliding window scheme, which can enhance the number of data as well. By using speed of sound (SOS), the quantitative experimental results of our preliminary study indicate that our proposed osteoporosis diagnosis method outperforms the conventional ultrasound methods, which may assist the clinician for osteoporosis screening.