In response to Bobelyak et al. (2025), Pocock and Chan (2025) and Chan et al. (2026)

Osteoporosis International
https://doi.org/10.1007/s00198-026-08179-z

LETTER TO THE EDITOR

In response to Bobelyak et al. (2025), Pocock and Chan (2025) and Chan et al. (2026)

N. Birch, D. Tognarini, P. McCloud, P. Button

Received: 27 April 2026 / Accepted: 29 July 2026
© The Author(s), under exclusive licence to the International Osteoporosis Foundation and the Bone Health and Osteoporosis Foundation 2026

Dear Editors,

We read with interest the article “Bone mineral density assessment using radiofrequency echographic multispectrometry (REMS) in patients before and after total hip replacement” by Bobelyak et al., the accompanying Editorial by Pocock and Chan and the recently published paper from Chan et al. “Demographic determinants of REMS-derived BMD and fragility score” [1-3].

The Bobelyak paper [1] purported to show that only about 10% of the variability of the radiofrequency echographic multi-spectrometry (REMS) bone mineral density (BMD) could be attributed to radiofrequency (RF) backscatter data, the rest being attributed to age, gender and body mass index (BMI).

Pocock and Chan [2] noted the small sample size of the main Bobelyak study (n = 50) and sub-study (n = 7) but flagged the findings as being “provocative”, suggesting that the Bobelyak data and their own data (unpublished) showed REMS does not function as a genuine imaging-based diagnostic tool.

Chan et al. [3] recently published on the influence of age and weight on REMS-BMD and Fragility Score calculation with 209 participants in their main study and five in their sub-study. They, like Bobelyak et al., concluded that there is an excessive degree of algorithmic dependence within the calculations performed by the REMS software of both BMD and the Fragility Score.

Despite these findings, it is possible the sample sizes in both studies are too small to detect variance. The methodology underpinning REMS-BMD measurement [4, 5] utilised reference database populations in 5-year age segments [6] and was further divided into three categories of BMI: normal/underweight, overweight and obese [6, 7]. Therefore, the full REMS reference database consists of 42 segments, each built using 100 results and each of which had to include cases classed as “normal” and “osteoporotic”. We postulate that any analyses of REMS results, focussing on age and weight data should have sample sizes that at least equal the reference database sets.

We have analysed data (on file) from 5137 pre-and post-menopausal female patients, from two established REMS clinical services, in Australia and the United Kingdom (UK), including 6389 REMS scans of the lumbar spine and 8744 REMS scans of the hips.

The analysis replicates the reference age and BMI segmentation model used by the REMS software [8]. Substantial variability in both BMD values (g/cm2) and T-scores was observed within individual age-BMI segments. In segments containing > 50 scans, T-score ranges exceeded 2.0 SD. In most of these segments, the range also spanned diagnostic T-score thresholds from normal (≥ -1.0) to osteoporosis (≤ -2.5). Using the lumbar spine 56-60-year age segment as an example, the degree of variation of BMD values is evident across the three BMI segments (Fig. 1). From this data, an important trend has been observed. The smaller the sample size, such as in the Obese BMI category, the less variation of data point spread is evident. We have observed a similar trend in the results at both hips.

This trend suggests that to detect the true extent of BMD range within a segment, close to 100 data points may be needed. Only in this way can the direct influence of the acquired RF backscatter spectral data in determining final BMD outcome be adequately demonstrated. Conversano et al., from an analysis of the REMS scan reference database, have recently demonstrated similar findings [8].

Fig. 1 SAS (v 9.4) statistical software-generated Box and Whisker plots demonstrating the spread of data in the 56- to 60-year age group according to BMI classification. The box shows the middle 50% of the data (quartiles Q2 and Q3) and the whiskers extend to the most extreme values that are still considered not outliers. The lower limit = Q1 minus (1.5× the interquartile range (IQR)) and the upper limit = Q3 plus (1.5× IQR). The outlier symbols are individual observations beyond those limits. A greater than 2 standard deviation (SD) spread is evident for all observations where there are > 50 data points. All three WHO diagnostic categories are represented in the underweight/normal and overweight BMI plots when the minimum data set for each is ≥ 44.

By comparison, the age range of the Bobelyak sample (n = 50) was 68.8 ± 9.8 years and the mean BMI was 28.9 ± 5.6; therefore, their sample ranged across up to 15 age/BMI segments. Similarly, the Chan et al. sample of 209 is spread across 13-age and three BMI segments. As a result, it is highly unlikely that the data from any of the segments in either study was sufficient to demonstrate the true distribution of BMD, T-scores or fragility scores within those segments.

Regards,

Dr. N. Birch, Dr. D. Tognarini, Dr. P. McCloud, Mr. P. Button

Data availability

The data that support the findings of this study are not openly available due to reasons of sensitivity and are available from the corresponding author upon reasonable request.

Declarations

Conflict of interest

Dr. N. Birch is a co-owner of and holds stock in Osteoscan UK Ltd. Dr. D. Tognarini is a co-owner of and holds stock in Bone Compass, Australia. Dr. P. McCloud and Mr. P. Button have no conflicts of interest.

References

1. Bobelyak M, Vaculik J, Stepan JJ (2025) Bone mineral density assessment using radiofrequency echographic multispectrometry (REMS) in patients before and after total hip replacement. Osteoporos Int. https://doi.org/10.1007/s00198-025-07685-w
2. Pocock N, Chan D (2025) Editorial: is REMS-BMD truly a measured parameter? A call for transparency and technical clarification. Osteoporos Int. https://doi.org/10.1007/s00198-025-07699-4
3. Chan D, Chen W, Yabsley E, Pocock N (2026) Demographic determinants of REMS-derived BMD and fragility score. Osteoporos Int. https://doi.org/10.1007/s00198-026-07960-4
4. 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 in Med & Biol 41(1):281-300
5. 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 in Medicine & Biology 42(6):1337-56. https://doi.org/10.1016/j.ultrasmedbio.2016.01.024
6. Villani V, Conversano F, Aventaggiato M, et al. (2014) Implementation of a model database for a novel ultrasonic approach to bone evaluation. 3rd Imeko TC13 Symposium on Measurements in Biology and Medicine “New Frontiers in Biomedical Measurements” April 17-18, 2014, Lecce, Italy. http://www.cnr.it/ontology/cnr/individuo/prodotto/ID325510
7. Echolight SpA. (2018) Technical sheet rev02_20180301_ENG. Echolight SpA, 73100 Lecce, Italy
8. Conversano F, Pisani P, Casciaro S (2026) Methodological clarification and analysis of demographic and anthropometric determinants in the calculation of REMS bone mineral density. Calcif Tissue Int 117:85. https://doi.org/10.1007/s00223-026-01547-1

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Corresponding author

N. Birch
nickbirch@doctors.org.uk

Affiliations

1. Osteoscan UK Ltd, Bragborough Health and Wellbeing Centre, Welton Road, Braunston, Daventry NN6 6NL, Northamptonshire, United Kingdom
2. Bone Compass, 85 Argus Street, Cheltenham 3192, VIC, Australia
3. McCloud Consulting Group, 17 Lincoln Avenue, Colonel Light Gardens 5041, SA, Australia

Published online: 11 August 2026