Comparison of concordance between chuna manual therapy diagnosis methods (palpation, X-ray, artificial intelligence program) in lumbar spine

Abstract Objectives Boys with Duchenne Muscular Dystrophy (DMD) are at increased risk for compromised bone health, manifesting as low-impact trauma long bone fractures and vertebral compression fractures. Methods In a prospective observational study, we studied bone health parameters in North Indian boys with DMD. We consecutively enrolled ambulatory boys with DMD on glucocorticoid therapy. Bone health was evaluated with X-ray spine, Dual-energy X-ray absorptiometry (DXA), serum calcium, vitamin D3 (25[OH]D), 1,25-dihyroxyvitamin D3 (1,25[OH]2D3), serum osteocalcin, osteopontin, and N terminal telopeptide of type 1 collagen (Ntx) levels. Results A total of 76 boys with DMD were enrolled. The median age was 8.5 (interquartile range [IQR] 7.04–10.77) years. Among these, seven (9.2%) boys had long bone fractures, and four (5.3%) had vertebral compression fractures. Fifty-four (71%) boys underwent DXA scan, and among these 31 (57%) had low bone mineral density (BMD, ≤−2 z-score) at the lumbar spine. The mean BMD z-score at the lumbar spine was −2.3 (95% confidence interval [CI] = −1.8, −2.8), and at the femoral neck was −2.5 (95% CI = −2, −2.9). 25(OH)D levels were deficient in 68 (89.5%, n=76) boys, and 1,25(OH)2D3 levels were deficient in all. Mean serum osteocalcin levels were 0.68 ± 0.38 ng/mL (n=54), serum osteopontin levels were 8.6 ± 4.6 pg/mL (n=54) and serum Ntx levels were 891 ± 476 nmol/L (n=54). Boys with low BMD received glucocorticoids for longer duration, in comparison to those with normal BMD (median, IQR [16.9 (6–34) months vs. 7.8 (4.8–13.4) months]; p=0.04). Conclusions Bone health is compromised in North Indian boys with DMD. BMD at the lumbar spine is reduced in more than half of boys with DMD and nearly all had vitamin D deficiency on regular vitamin D supplements. Longer duration of glucocorticoid therapy is a risk factor for low BMD in our cohort.

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Training Artificial Intelligence Neural Networks to Detect Devices on a Chest X-ray, a Pathway to COVID-19 Testing

Journal of the National Medical Association ◽

10.1016/j.jnma.2020.09.140 ◽

2020 ◽

Vol 112 (5) ◽

pp. S50

Author(s):

Zachary Eller ◽

Michelle Chen ◽

Jermaine Heath ◽

Uzma Hussain ◽

Thomas Obisean ◽

...

Keyword(s):

Artificial Intelligence ◽

Neural Networks ◽

X Ray ◽

Chest X Ray

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POS0045 LEAST SIGNIFICANT CHANGE IN BONE DENSITOMETRY IN PATIENTS WITH OBESITY

Annals of the Rheumatic Diseases ◽

10.1136/annrheumdis-2021-eular.3819 ◽

2021 ◽

Vol 80 (Suppl 1) ◽

pp. 227.2-228

Author(s):

D. Claire ◽

M. Geoffroy ◽

L. Kanagaratnam ◽

C. Isabelle ◽

A. Hittinger ◽

...

Keyword(s):

Bone Mineral Density ◽

Lumbar Spine ◽

General Population ◽

Bone Mineral ◽

Bone Densitometry ◽

Dual Energy ◽

Mineral Density ◽

Least Significant Change ◽

X Ray ◽

Significant Change

Background:Dual energy X-ray absoprtiometry is the reference method to mesure bone mineral density (1). Loss of bone mineral density is significant if it exceeds the least significant change. The threshold value used in general population is 0,03 g/cm2 (2). Patients with obesity are known for having a higher bone mineral density due to metabolism and physiopathology characteristics (3,4).Objectives:The aim of our study was to determine the least significant change in bone densitometry in patients with obesity.Methods:We conducted an interventionnal study in 120 patients with obesity who performed a bone densitometry. We measured twice the bone mineral density at the lumbar spine, the femoral neck and the total hip in the same time (5,6). We determined the least significant change in bone densitometry from each pair of measurements, using the Bland and Altman method. We also determined the least significant change in bone densitometry according to each stage of obesity.Results:The least significant change in bone densitometry in patients with obesity is 0,046g/cm2 at the lumbar spine, 0.069 g/cm2 at the femoral neck and 0.06 g/cm2 at the total hip.Conclusion:The least significant change in bone densitometry in patients with obesity is higher than in general population. These results may improve DXA interpretation in this specific population, and may personnalize their medical care.References:[1]Lees B, Stevenson JC. An evaluation of dual-energy X-ray absorptiometry and comparison with dual-photon absorptiometry. Osteoporos Int. mai 1992;2(3):146-52.[2]Briot K, Roux C, Thomas T, Blain H, Buchon D, Chapurlat R, et al. Actualisation 2018 des recommandations françaises du traitement de l’ostéoporose post-ménopausique. Rev Rhum. oct 2018;85(5):428-40.[3]Shapses SA, Pop LC, Wang Y. Obesity is a concern for bone health with aging. Nutr Res N Y N. mars 2017;39:1-13.[4]Savvidis C, Tournis S, Dede AD. Obesity and bone metabolism. Hormones. juin 2018;17(2):205-17.[5]Roux C, Garnero P, Thomas T, Sabatier J-P, Orcel P, Audran M, et al. Recommendations for monitoring antiresorptive therapies in postmenopausal osteoporosis. Jt Bone Spine Rev Rhum. janv 2005;72(1):26-31.[6]Ravaud P, Reny JL, Giraudeau B, Porcher R, Dougados M, Roux C. Individual smallest detectable difference in bone mineral density measurements. J Bone Miner Res. août 1999;14(8):1449-56.Disclosure of Interests:None declared.

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Deep learning for visualization and novelty detection in large X-ray diffraction datasets

npj Computational Materials ◽

10.1038/s41524-021-00575-9 ◽

2021 ◽

Vol 7 (1) ◽

Author(s):

Lars Banko ◽

Phillip M. Maffettone ◽

Dennis Naujoks ◽

Daniel Olds ◽

Alfred Ludwig

Keyword(s):

Thin Film ◽

Crystal Structure ◽

Artificial Intelligence ◽

Deep Learning ◽

Novelty Detection ◽

Structural Similarity ◽

X Ray Diffraction ◽

X Ray ◽

Diffraction Patterns ◽

Post Hoc

AbstractWe apply variational autoencoders (VAE) to X-ray diffraction (XRD) data analysis on both simulated and experimental thin-film data. We show that crystal structure representations learned by a VAE reveal latent information, such as the structural similarity of textured diffraction patterns. While other artificial intelligence (AI) agents are effective at classifying XRD data into known phases, a similarly conditioned VAE is uniquely effective at knowing what it doesn’t know: it can rapidly identify data outside the distribution it was trained on, such as novel phases and mixtures. These capabilities demonstrate that a VAE is a valuable AI agent for aiding materials discovery and understanding XRD measurements both ‘on-the-fly’ and during post hoc analysis.

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Ability of radiofrequency echographic multispectrometry to identify osteoporosis status in elderly women with type 2 diabetes

Aging Clinical and Experimental Research ◽

10.1007/s40520-021-01889-w ◽

2021 ◽

Author(s):

Carla Caffarelli ◽

Maria Dea Tomai Pitinca ◽

Antonella Al Refaie ◽

Elena Ceccarelli ◽

Stefano Gonnelli

Keyword(s):

Type 2 Diabetes ◽

Lumbar Spine ◽

Elderly Women ◽

Fragility Fractures ◽

Dual Energy ◽

Healthy Controls ◽

Diagnosis Of Osteoporosis ◽

X Ray ◽

The Difference

Abstract Background Patients with type 2 diabetes (T2DM) have an increased or normal BMD; however fragility fractures represent one of the most important complications of T2DM. Aims This study aimed to evaluate whether the use of the Radiofrequency Echographic multi spectrometry (REMS) technique may improve the identification of osteoporosis in T2DM patients. Methods In a cohort of 90 consecutive postmenopausal elderly (70.5 ± 7.6 years) women with T2DM and in 90 healthy controls we measured BMD at the lumbar spine (LS-BMD), at femoral neck (FN-BMD) and total hip (TH-BMD) using a dual-energy X-ray absorptiometry device; moreover, REMS scans were also carried out at the same axial sites. Results DXA measurements were all higher in T2DM than in non-T2DM women; instead, all REMS measurements were lower in T2DM than in non T2DM women. Moreover, the percentage of T2DM women classified as “osteoporotic”, on the basis of BMD by REMS was markedly higher with respect to those classified by DXA (47.0% vs 28.0%, respectively). On the contrary, the percentage of T2DM women classified as osteopenic or normal by DXA was higher with respect to that by REMS (48.8% and 23.2% vs 38.6% and 14.5%, respectively). T2DM women with fragility fractures presented lower values of both BMD-LS by DXA and BMD-LS by REMS with respect to those without fractures; however, the difference was significant only for BMD-LS by REMS (p < 0.05). Conclusions Our data suggest that REMS technology may represent a useful approach to enhance the diagnosis of osteoporosis in patients with T2DM.

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