Skip to main content
NCBI home page
Springer logoLink to Springer
. 2025 Sep 10;36(11):2237–2244. doi: 10.1007/s00198-025-07685-w

Bone mineral density assessment using radiofrequency echographic multispectrometry (REMS) in patients before and after total hip replacement

Maryan Bobelyak 1,2, Jan Vaculik 1,2, Jan J Stepan 3,4,✉
PMCID: PMC12628398  PMID: 40928527

Abstract

Summary

REMS-BMD by radiofrequency echographic multispectrometry is primarily determined by a patient’s BMI, age, and sex. Only about 2.8% of the changes in femoral neck REMS-BMD can be attributed to replacement of the total hip with metal implants.

Purpose

This study aimed to evaluate how total hip replacement affects femoral neck REMS-BMD and to examine the contributions of a patient’s sex, age, and BMI to the REMS-BMD assessment.

Methods

REMS-BMD was measured at the proximal femur using EchoStation (Echolight Spa, Lecce, Italy) in 27 women and 23 men with advanced hip osteoarthritis before surgery and again on the third day following total hip joint arthroplasty. For seven men, REMS-BMD was measured at both the proximal femur and lumbar spine, taking into account their actual as well as varying age, height, and weight.

Results

The patients’ ages ranged from 49 to 84 years, with the REMS-BMD T-score values for the femoral neck varying from + 1.8 to − 3.2. There was a significant correlation between the femoral neck REMS-BMD values measured before and after total hip arthroplasty. Post-surgery, the femoral neck REMS-BMD values showed a consistent decrease of − 0.021 g/cm2 (approximately 2.8%, p < 0.05) compared to pre-operative measurements. A model incorporating age, sex, and BMI accurately predicted femoral neck REMS-BMD in both pre- and postoperative settings, with no evidence of systematic bias. Variations in REMS-BMD were observed in both the femoral neck and lumbar spine when measurements were taken in the same patients under different conditions of age, height, and weight.

Conclusion

Approximately 90% of the variability in REMS-BMD of the femoral neck can be attributed to the patient’s age, sex, and BMI. Further research is needed to determine the extent to which REMS-BMD reflects areal or volumetric BMD, and how backscattered radiofrequency ultrasound signals influence the REMS-BMD.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00198-025-07685-w.

Keywords: Bone mineral density, Osteoporosis, Radiofrequency echographic multispectrometry

Introduction

Osteoporosis is a systemic skeletal disease characterized by low bone mass and microarchitectural deterioration of bone tissue, leading to a higher susceptibility to fractures [1]. The most commonly used method for diagnosing osteoporosis—and a strong predictor of osteoporotic fractures—is the measurement of bone mineral density (BMD) using dual X-ray absorptiometry (DXA) [2]. The diagnostic sensitivity and interpretation of DXA-BMD measurements in the lumbar spine and proximal femur may be influenced by artifacts such as degenerative changes, fractures, hip replacements, or surgical spinal implants [2]. Some studies suggest that the impact of these internal structural artifacts can be mitigated through ultrasound examination of the bone [3]. Ultrasound methods are appealing in clinical settings due to their non-invasive nature, portability, safety (as they do not use ionizing radiation), and lower cost. Ultrasonic parametric imaging, based on backscatter processing, has been studied ex vivo on samples of calcaneal and trabecular human bones [4–7].

Radiofrequency Echographic Multispectrometry (REMS) is a clinically available method for non-ionizing measurement of REMS-BMD in the lumbar spine and femur [8–11]. The REMS echographic device captures ultrasonic spectral profiles generated from interactions with bone structures. An independent Osteoporosis Score is calculated by comparing individual ultrasonic profiles to those collected from patients evaluated simultaneously with the EchoStation device and DXA-BMD from the Hologic bone densitometer. The Osteoporosis Score enables the classification of bone status as either healthy, osteopenic, or osteoporotic. The patient’s Osteoporosis Score enables the prediction of the patient’s REMS-BMD (g/cm2) using a linear equation with coefficients reflecting patient age (in 5-year intervals), sex, and BMI. The reference spectra and coefficients are stored in the device’s proprietary database. For clinical diagnostics, REMS-BMD values are presented as T-scores relative to the NHANES reference data [12, 13]. Currently, available published data do not clarify the contribution of the Osteoporosis Score to the prediction of REMS-BMD values. One possible approach to address this issue is to compare femoral neck REMS-BMD in patients with advanced stages of hip osteoarthritis, both before and after bone replacement with metal. This study aimed to evaluate changes in REMS-BMD in the proximal femur, comparing measurements taken before and after total hip arthroplasty.

Methods

The study was conducted in the Orthopedic Department of Bulovka Hospital in Prague, Czech Republic, as part of the Fracture Liaison Service research program. It aimed to evaluate the clinical value of a portable non-ionizing bone densitometry device in orthopedic practice. The participant group included 50 patients with advanced stages of hip osteoarthritis who had no history of low-energy fractures and were indicated for total hip joint arthroplasty. To illustrate the impact of anthropometric data on REMS-BMD values in the proximal femur and lumbar spine, the effects of changes in age and BMI were examined in seven male institutional staff members, whose REMS-BMD was measured using either their actual or fictitious age and BMI. The study protocol was approved by the Ethics Review Board of Bulovka Hospital, and all patients provided informed consent.

We measured the REMS-BMD (Radiofrequency Echographic Multi Spectrometry Bone Mineral Density) using an echographic device (EchoStudio: SN 002–230910, v.2.1.1–0, EchoStation; Echolight Spa, Lecce, Italy). The measurements were conducted by a single operator of the device, who received proper training from the device distributor. The calibration status of the REMS device was regularly verified. In patients with hip osteoarthritis, the measurements were taken at the same hip site both before total hip arthroplasty and again on the third postoperative day. For echoing the proximal femur, the ultrasound transducer was positioned parallel to the axis of the neck of the femur or the neck of the endoprosthesis. To visualize the lumbar spine, the transducer was first placed under the sternum to locate the L1 lumbar vertebra, and then it was moved downward to examine the L4 vertebra. In our patient group, we calculated the short-term intra-operator precision from two consecutive REMS acquisitions on 30 patients performed by the same operator [14]. This yielded a least significant change in femoral neck REMS-BMD of 0.014 g/cm2 at a 95% confidence level, with a precision error of 1.86%.

We checked the normality of the data using the Kolmogorov–Smirnov test. The results are presented as means with standard deviation (S.D.) for continuous variables. We performed between-group comparisons using the independent Student’s t-test for continuous variables. A Linear regression method was employed to analyze the relationship between two variables, assessing the statistical significance of the correlations using Pearson coefficients. The agreement between femoral neck REMS-BMD values obtained by echographic device measurements for 50 patients, both before and after total hip arthroplasty, was evaluated using Pearson’s correlation coefficient and Bland–Altman analysis, with 95% limits of agreement calculated to evaluate systematic bias. Model performance was evaluated using the coefficient of determination (Rsqr), Adjusted Rsqr, and p-values for overall model fit.

Furthermore, multiple linear regression was used to assess the independent contributions of age, sex, and BMI to femoral neck REMS-BMD, separately for pre- and postoperative measurements. The agreement between measured and calculated femoral neck REMS-BMD values was assessed using Pearson’s correlation coefficient and Bland–Altman analysis. A p-value of less than 0.05 was considered statistically significant. Data analysis was conducted using SigmaPlot for Windows version 15 (Grafiti LLC, USA). Data from the 50 patients, both before and after hip arthroplasty, are presented in Supplemental Tables S1-S5.

Results

The characteristics of 50 patients before total hip arthroplasty for hip osteoarthritis and after surgery are summarized in Table 1. Osteoporosis (REMS-BMD ≤  − 2.5 T-score) was evident in 6 women. Secondary osteoporosis was not suspected in any of the patients (Supplemental Tables S1-S4). Table 2 presents the correlations between REMS-BMD values measured at three proximal femoral sites in both women and men before surgery.

Table 1.

Characteristics of the study population before and after surgery

Before After
Number of subjects 50 50
Age (years) 68.8 ± 9.8
Women / Men 27/23
Menopause in women (years) 49.4 ± 5.0
Years since menopause 22.2 ± 12.6
BMI (kg/m2) 28.9 ± 5.6
Neck REMS-BMD (g/cm2) 0.742 ± 0.129 0.721 ± 0.133 a
Neck REMS-BMD T-score  − 1.21 ± 1.01  − 1.68 ± 2.44 b
Neck REMS-BMD Z-score 0.36 ± 0.77 0.14 ± 0.80 a
Total REMS-BMD (g/cm2) 0.884 ± 0.139 0.875 ± 0.148
Total REMS-BMD T-score  − 0.78 ± 0.96  − 1.08 ± 1.99
Total REMS-BMD Z-score 0.35 ± 0.79 0.32 ± 0.90
Trochanter REMS-BMD (g/cm2) 0.850 ± 0.122 0.841 ± 0.128
Trochanter REMS-BMD T-score  − 0.80 ± 0.88  − 0.86 ± 0.90
Trochanter REMS-BMD Z-score 0.05 ± 0.99 0.01 ± 0.99
Open in a new tab

ap < 0.001; bp < 0.005; paired T-test

Table 2.

Pearson correlations of clinical characteristics and REMS-BMD in 23 men and 27 women before surgery

Men BMI Neck REMS-BMD Total hip REMS-BMD Trochanter REMS-BMD
Age r  − 0.164  − 0.423  − 0.382  − 0.405
p 0.453 0.044 0.072 0.068
BMI r 0.810 0.895 0.895
p  < 0.001  < 0.001  < 0.001
Neck REMS-BMD r 0.963 0.963
p  < 0.001  < 0.001
Total hip REMS-BMD r 1
p  < 0.001
Women BMI Neck REMS-BMD Total hip REMS-BMD Trochanter REMS-BMD Yrs since menopause
Age r  − 0.283  − 0.661  − 0.667  − 0.667 0.931
p 0.152  < 0.001  < 0.001  < 0.001  < 0.001
BMI r 0.867 0.875 0.875  − 0.332
p  < 0.001  < 0.001  < 0.001 0.091
Neck REMS-BMD r 0.991 0.991  − 0.690
p  < 0.001  < 0.001  < 0.001
Total hip REMS-BMD r 1  − 0.682
p  < 0.001  < 0.001
Trochanter REMS-BMD r  − 0.682
p  < 0.001
Open in a new tab

The echographic device enabled the measurement of REMS-BMD in all fifty patients after total hip arthroplasty, where the proximal femur had been replaced with metal (Fig. 1). The device provided a femoral acquisition sample for verifying focus and depth in all patients, both before and after surgery. There were no significant differences in REMS-BMD values for the total hip or trochanter. REMS-BMD values for the femoral neck, measured before and after total hip arthroplasty, showed a significant difference (Table 1).

Fig. 1.

Fig. 1

Open in a new tab

Proximal femur X-ray before (left) and after (right) total arthroplasty of the left hip, the REMS scan in the same patient before (A) and after (B) surgery, and the femoral acquisition sample for verification of focus and depth before (C) and after (D) surgery. The data of this patient are highlighted with an asterisk in Supplemental Tables 1 and 2

The values of REMS-BMD of the femoral neck before total hip arthroplasty showed a significant correlation with the REMS-BMD values measured after the surgery (Adj Rsqr = 0.926) (Fig. 2). The Bland–Altman analysis indicated that the femoral neck REMS-BMD values obtained post-surgery were systematically lower by 0.021 g/cm2 (95% CI: –0.0312 to –0.0107) compared to those measured in the femoral neck before surgery. The Limits of Agreement ranged from − 0.0914 to 0.0496, with a 95% CI for the lower limit of − 0.1091 to − 0.0737 and an upper Limit of 0.0319 to 0.0673. This bias was statistically significant.

Fig. 2.

Fig. 2

Open in a new tab

Bland–Altman plots showing femoral neck REMS-BMD values by echographic device measurements for 50 patients before and after total hip arthroplasty. Left: Solid Line indicates mean difference; dashed Lines represent the 95% limits of agreement. The line of equality is also shown. The mean bias was significantly different from zero, and limits of agreement indicated significant systematic bias

A multiple Linear regression analysis indicated that age, sex, and BMI were independent predictors of femoral neck REMS-BMD and REMS-BMD T-score both before and after surgery. The pre-surgery model accounted for 90.4% of the variance in REMS-BMD represented by the following equation: REMS-femoral neck BMD = 0.733 − (0.00465 * age) − (0.0722 * sex) + (0.0153 * BMI). The model had an R-squared value of 0.904 and an adjusted R-squared value of 0.898 (p < 0.001) (see Supplemental Table S6). This equation was used to calculate the femoral neck REMS-BMD. BMD values calculated using this model showed a strong correlation with the measured femoral neck REMS-BMD values (R = 0.951, Rsqr = 0.904, Adj Rsqr = 0.902). The Bland–Altman analysis indicated a mean difference close to zero, suggesting no evidence of systematic bias (Fig. 3). Calculated REMS-BMD T-score correlated significantly with the measured one (R = 0.938, Rsqr = 0.880, Adj Rsqr = 0.877) (see Supplemental Fig. 1).

Fig. 3.

Fig. 3

Open in a new tab

Bland–Altman plots showing agreement between measured and calculated femoral neck REMS-BMD values before surgery. Left: Solid Line indicates mean difference; dashed Lines represent the 95% limits of agreement. The line of equality is also shown. The mean bias was close to zero, and limits of agreement indicated no significant systematic bias

The REMS-BMD values for the total proximal femur and trochanter each account for 35% of the total sum of the REMS-BMD values from all three femoral sites, and do not provide information independent of REMS-BMD of the femoral neck measured by the EchoStation. This relationship is documented by the concurrent correlations between REMS-BMD values in the total hip joint and trochanter in both females and males (see Table 2 and Fig. 4). Thus, we report only the femoral neck REMS-BMD values.

Fig. 4.

Fig. 4

Open in a new tab

The relationship between REMS-BMD values at the total hip and trochanter in women (●) and men (o) before surgery

The REMS-BMD values in both the femoral neck and lumbar spine varied when measured in the same patients under varying age, height, and weight (Fig. 5).

Fig. 5.

Fig. 5

Open in a new tab

Femoral neck and lumbar spine REMS-BMD measured in 7 men with an average age of 69.7 ± 12.2 years and a BMI of 28.0 ± 3.2 kg/m2 (real), alongside data from the same subjects under different ages and BMIs. REMS-BMD values were also significantly modified by sex (are not shown). The BMI of 13.9 in this figure is not physiologically plausible

Discussion

In all fifty of our patients, the device yielded REMS-BMD values at the femoral neck before total hip arthroplasty, as well as values of REMS-BMD measured in the metal material of the endoprosthesis. The postoperative “femoral neck” REMS-BMD values were significantly lower compared with the preoperative values. The Bland–Altman’s analysis indicated that the REMS-BMD values measured in the “femoral neck” after surgery were consistently and statistically significantly lower than the REMS-BMD values measured before surgery. The coefficient of determination in multiple linear regression analysis of the REMS-BMD measurements from the femoral neck was higher before total hip arthroplasty compared to REMS-BMD measurements taken after the surgery. For interpretation of the results, it is essential to note that the REMS-BMD values of the patient are derived from models stored in the proprietary device’s database, where the Osteoporosis Score is categorized by 5-year age intervals, sex, and BMI. The algorithm contains a spectral validation step. In this step, the spectra not similar enough to one of the reference models should be rejected [12]. Arguably, this should lead to rejection of metallic implant scans. Our findings suggest that the unique quality of the material being measured was not rejected and may hinder the accurate assessment of the Osteoporosis Score. If the metallic materials were classified as outside the models, the REMS-BMD would likely be calculated based solely on the patient’s age, sex, and BMI. Consequently, the 2.1% differences in REMS-BMD values before and after hip replacement, as determined by Bland–Altman’s analysis, could be explained by the dominant contributions of sex, age, and BMI to the determination of REMS-BMD.

Across previously published studies, demographic and body composition indices typically explained between 20 and 40% of BMD variance [15, 16], while some studies explained up to 60–80% based on the specific variables included in the models, measurement sites, and population characteristics, making direct comparisons between studies challenging [17–19]. In this study, pre-surgery age, sex, and BMI explained about 89–90% of the variability in REMS-BMD of the femoral neck (Adj Rsqr = 0.898). This substantial impact of age, sex, and BMI persisted in postoperative measurements, even with metal implants occupying most of the former bone volume (Adj Rsqr = 0.888). There was strong agreement between calculated and measured femoral neck REMS-BMD values (Adj Rsqr = 0.902), and Bland–Altman analysis showed no systematic bias, indicating that a simple model incorporating age, sex, and BMI can accurately predict femoral neck REMS-BMD and T-scores in both pre- and postoperative settings, with excellent agreement to measured values and no evidence of systematic bias.

We were unable to compare the REMS-BMD and DXA-BMD values in our patients with osteoarthritis affecting the proximal femur. In key echographic studies, correlations between DXA-BMD (measuring bone mineral) and REMS-BMD values (predicted based on Osteoporosis Score, sex, age, and BMI) have shown both sensitivity and specificity above 90% for both lumbar spine and femoral neck [12, 13, 20, 21]. However, the European multicenter study involving 4307 Caucasian women aged 30 to 90 years showed a larger variation in BMD measurements obtained through the REMS method compared to DXA [22]. In some studies, the REMS-BMD method classified more women as “osteoporotic” compared to the DXA-BMD. In patients with type 2 diabetes mellitus, DXA-BMD values were significantly higher than those in the control group, while REMS-BMD values were lower. Consequently, the proportion of women classified by REMS as “osteoporotic” was 47.0%, compared to 28.0% as measured by DXA [23]. Additionally, a study involving postmenopausal women with radiological osteoarthritis of the lumbar spine found that the REMS-BMD T-score was significantly lower than the DXA-BMD T-score for both the lumbar spine and all femoral subregions [24, 25]. Furthermore, in patients with disuse-related osteoporosis, the femoral neck REMS-BMD was significantly lower than the DXA-BMD value [26]. The discrepancies observed between the results of REMS-BMD and DXA-BMD measurements in some studies could be attributed to REMS’s capability to overcome common artifacts, or to the quality of bone other than in osteoporosis [27]. Additional research is needed to evaluate the reliability of REMS in assessing REMS-BMD in patients whose DXA-BMD values deviate from the expected correlation between REMS-BMD and DXA-BMD.

A strength of this study is the high predictive accuracy of the regression models, supported by strong statistical performance and agreement analyses, using readily available clinical variables (age, sex, BMI). However, we conducted a single-center observational study involving a well-defined and homogeneous group of patients with hip osteoarthritis. This focused approach allowed for a targeted evaluation of REMS in a specific surgical population. The real-world performance of REMS in a more heterogeneous population cannot be inferred from our cohort, and external validation in broader cohorts is required to confirm generalizability. Additionally, as the device does not provide the Osteoporosis Score data, we were unable to determine how the material of the endoprosthesis influences the spectral changes in the radiofrequency ultrasound signal or the subsequent determination of the Osteoporosis Score. We also lack a corresponding set of DXA values in our patients to illustrate the artificial increases in BMD post-hip replacement by DXA, in contrast to evaluable REMS-derived BMD.

The significant differences observed in the REMS-BMD results when the operator inputted either their actual or fictitious age and BMI (see Fig. 5) are attributed to the algorithm used for calculating REMS-BMD. When an incorrect age or BMI is input, the algorithm automatically aligns the measured spectral deformation with inappropriate reference models.

Conclusion

Radiofrequency echographic multispectrometry allows for the measurement of REMS-BMD in the femoral neck even after total hip arthroplasty. Post-surgery, the REMS-BMD values recorded in the femoral neck were consistently and statistically significantly lower by 2.8% than those measured in the femoral neck before surgery. A simple model incorporating age, sex, and BMI predicted femoral neck REMS-BMD in both pre- and postoperative settings, with excellent agreement to measured values and no evidence of systematic bias. Furthermore, the influence of sex, age, and BMI on predicting REMS-BMD was supported by variations in REMS-BMD values when different anthropometric indicators were applied. Additional research is necessary to understand better how back-reflected radiofrequency ultrasonic signals contribute to predictions of bone mineral density.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 184 KB) (184.5KB, docx)

Funding

Open access publishing supported by the institutions participating in the CzechELib Transformative Agreement. JS is supported by the RVO 00023728 (Institute of Rheumatology) and Charles University Project SVV 260 523.

Declarations

Conflict of interest

Maryan Bobelyak, Jan Vaculik, and Jan J. Stepan declare that there are no relationships that could be considered a conflict of interest. The device used in the study (EchoStudio: SN 002–230910, v.2.1.1–0, EchoStation; Echolight Spa, Lecce, Italy) was provided by the distributor, PROMEDICA PRAHA GROUP a.s. This arrangement followed a teleconference with the manufacturer, during which we expressed our intention to test the device in an orthopedic practice for patients with hip fractures and those undergoing hip replacement. We received regular updates on the calibration status of the REMS device. On May 2, 2025, the distributor requested the immediate return of the device. We returned the device along with all measured data on May 7, 2025. On June 24, 2025, the distributor asked us to sign a declaration stating that the borrowed device was intended solely for demonstration purposes and that the manufacturer had prohibited its use for research or clinical applications. We refused to sign the declaration retrospectively.

Footnotes

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

  • 1.Klibanski A, Adams-Campbell L, Bassford T, Blair SN, Boden SD (2001) Osteoporosis prevention, diagnosis, and therapy. JAMA 285(6):785–795. 10.1001/jama.285.6.785 [DOI] [PubMed] [Google Scholar]
  • 2.Slart R, Punda M, Ali DS, Bazzocchi A, Bock O, Camacho P et al (2025) Updated practice guideline for dual-energy X-ray absorptiometry (DXA). Eur J Nucl Med Mol Imaging 52(2):539–563. 10.1007/s00259-024-06912-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Ishizu H, Shimizu T, Sakamoto Y, Toyama F, Kitahara K, Takayama H et al (2024) Radiofrequency echographic multispectrometry (REMS) can overcome the effects of structural internal artifacts and evaluate bone fragility accurately. Calcif Tissue Int 114(3):246–254. 10.1007/s00223-023-01167-z [DOI] [PubMed] [Google Scholar]
  • 4.Li Y, Li B, Li Y, Liu C, Xu F, Zhang R et al (2019) The ability of ultrasonic backscatter parametric imaging to characterize bovine trabecular bone. Ultrason Imaging 41(5):271–289. 10.1177/0161734619862190 [DOI] [PubMed] [Google Scholar]
  • 5.Jiang YQ, Liu CC, Li RY, Wang WP, Ding H, Qi Q et al (2014) Analysis of apparent integrated backscatter coefficient and backscattered spectral centroid shift in Calcaneus in vivo for the ultrasonic evaluation of osteoporosis. Ultrasound Med Biol 40(6):1307–1317. 10.1016/j.ultrasmedbio.2013.12.024 [DOI] [PubMed] [Google Scholar]
  • 6.Liu C, Xu F, Ta D, Tang T, Jiang Y, Dong J et al (2016) Measurement of the human calcaneus in vivo using ultrasonic backscatter spectral centroid shift. J Ultrasound Med 35(10):2197–2208. 10.7863/ultra.15.03030 [DOI] [PubMed] [Google Scholar]
  • 7.Wear KA (2020) Mechanisms of interaction of ultrasound with cancellous bone: a review. IEEE Trans Ultrason Ferroelectr Freq Control 67(3):454–482. 10.1109/TUFFC.2019.2947755 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Diez-Perez A, Brandi ML, Al-Daghri N, Branco JC, Bruyere O, Cavalli L et al (2019) Radiofrequency echographic multi-spectrometry for the in-vivo assessment of bone strength: state of the art-outcomes of an expert consensus meeting organized by the European Society for Clinical and Economic Aspects of Osteoporosis, Osteoarthritis and Musculoskeletal Diseases (ESCEO). Aging Clin Exp Res 31(10):1375–1389. 10.1007/s40520-019-01294-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Fuggle NR, Beaudart C, Bruyère O, Abrahamsen B, Al-Daghri N, Burlet N et al (2024) Evidence-based guideline for the management of osteoporosis in men. Nat Rev Rheumatol 20(4):241–251. 10.1038/s41584-024-01094-9 [DOI] [PubMed] [Google Scholar]
  • 10.Borsoi L, Armeni P, Brandi ML (2023) Cost-minimization analysis to support the HTA of radiofrequency echographic multi spectrometry (REMS) in the diagnosis of osteoporosis. Global & Regional Health Technology Assessment 10:1–11. 10.33393/grhta.2023.2492 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Reginster JY, Silverman SL, Alokail M, Al-Daghri N, Hiligsmann M (2025) Cost-effectiveness of radiofrequency echographic multi-spectrometry for the diagnosis of osteoporosis in the United States. JBMR Plus 9(1):ziae138. 10.1093/jbmrpl/ziae138 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Casciaro S, Peccarisi M, Pisani P, Franchini R, Greco A, De Marco T et al (2016) An advanced quantitative echosound methodology for femoral neck densitometry. Ultrasound Med Biol 42(6):1337–1356. 10.1016/j.ultrasmedbio.2016.01.024 [DOI] [PubMed] [Google Scholar]
  • 13.Di Paola M, Gatti D, Viapiana O, Cianferotti L, Cavalli L, Caffarelli C et al (2019) Radiofrequency echographic multispectrometry compared with dual X-ray absorptiometry for osteoporosis diagnosis on lumbar spine and femoral neck. Osteoporos Int 30(2):391–402. 10.1007/s00198-018-4686-3 [DOI] [PubMed] [Google Scholar]
  • 14.Jankowski LG, Warner S, Gaither K, Lenchik L, Fan B, Lu Y et al (2019) Cross-calibration, least significant change and quality assurance in multiple dual-energy X-ray absorptiometry scanner environments: 2019 ISCD official position. J Clin Densitom 22(4):472–483. 10.1016/j.jocd.2019.09.001 [DOI] [PubMed] [Google Scholar]
  • 15.Ho-Pham LT, Nguyen ND, Lai TQ, Nguyen TV (2010) Contributions of lean mass and fat mass to bone mineral density: a study in postmenopausal women. BMC Musculoskelet Disord 11:59. 10.1186/1471-2474-11-59 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Xiao Z, Tan Z, Shang J, Cheng Y, Tang Y, Guo B et al (2020) Sex-specific and age-specific characteristics of body composition and its effect on bone mineral density in adults in southern China: a cross-sectional study. BMJ Open 10(4):e032268. 10.1136/bmjopen-2019-032268 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Reid IR, Ames R, Evans MC, Sharpe S, Gamble G, France JT et al (1992) Determinants of total body and regional bone mineral density in normal postmenopausal women--a key role for fat mass. J Clin Endocrinol Metab 75(1):45–51 [DOI] [PubMed] [Google Scholar]
  • 18.Shioji M, Yamamoto T, Ibata T, Tsuda T, Adachi K, Yoshimura N (2017) Artificial neural networks to predict future bone mineral density and bone loss rate in Japanese postmenopausal women. BMC Res Notes 10(1):590. 10.1186/s13104-017-2910-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Aflatooni J, Martin S, Edilbi A, Gadangi P, Singer W, Loving R et al (2023) A novel non-invasive method for predicting bone mineral density and fracture risk using demographic and anthropometric measures. Sports Med Health Sci 5(4):308–313. 10.1016/j.smhs.2023.09.003 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Conversano F, Franchini R, Greco A, Soloperto G, Chiriaco F, Casciaro E et al (2015) A novel ultrasound methodology for estimating spine mineral density. Ultrasound Med Biol 41(1):281–300. 10.1016/j.ultrasmedbio.2014.08.017 [DOI] [PubMed] [Google Scholar]
  • 21.Sergio RO, Nayelli RGE (2022) Evaluation of the bone mineral density in the Mexican female population using the radiofrequency echographic multi spectrometry (REMS) technology. Arch Osteoporos 17(1):43. 10.1007/s11657-022-01080-2 [DOI] [PubMed] [Google Scholar]
  • 22.Cortet B, Dennison E, Diez-Perez A, Locquet M, Muratore M, Nogues X et al (2021) Radiofrequency echographic multi spectrometry (REMS) for the diagnosis of osteoporosis in a European multicenter clinical context. Bone 143:115786. 10.1016/j.bone.2020.115786 [DOI] [PubMed] [Google Scholar]
  • 23.Caffarelli C, Tomai Pitinca MD, Al Refaie A, Ceccarelli E, Gonnelli S (2022) Ability of radiofrequency echographic multispectrometry to identify osteoporosis status in elderly women with type 2 diabetes. Aging Clin Exp Res 34(1):121–127. 10.1007/s40520-021-01889-w [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Caffarelli C, Tomai Pitinca MD, Al Refaie A, De Vita M, Catapano S, Gonnelli S (2022) Could radiofrequency echographic multispectrometry (REMS) overcome the overestimation in BMD by dual-energy X-ray absorptiometry (DXA) at the lumbar spine? BMC Musculoskelet Disord 23(1):469. 10.1186/s12891-022-05430-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Caffarelli C, Al Refaie A, Mondillo C, Manasse G, Versienti A, Tomai Pitinca MD et al (2024) The advantages of radiofrequency echographic multispectrometry in the evaluation of bone mineral density in a population with osteoarthritis at the lumbar spine. Diagnostics 14(5):523. 10.3390/diagnostics14050523 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Lalli P, Mautino C, Busso C, Bardesono F, Di Monaco M, Lippi L et al (2022) Reproducibility and accuracy of the radiofrequency echographic multi-spectrometry for femoral mineral density estimation and discriminative power of the femoral fragility score in patients with primary and disuse-related osteoporosis. J Clin Med. 10.3390/jcm11133761 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Siregar MFG, Jabbar F, Effendi IH, Alhair T, Prabudi MO, Faradina D (2024) Correlation between serum vitamin D levels and bone mass density evaluated by radiofrequency echographic multi-spectrometry technology (REMS) in menopausal women. Narra J 4(1):e452. 10.52225/narra.v4i1.452 [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary file1 (DOCX 184 KB) (184.5KB, docx)

Articles from Osteoporosis International are provided here courtesy of Springer

RESOURCES