Near‐infrared bone densitometry: A feasibility study on distal radius measurement

Chung, Chun Eun; Chen, Yu Pin; Leu, Tsai Hsueh; Sun, Chia‐Liang

doi:10.1002/jbio.201700342

Cited by 8 publications

(9 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…7 seemingly contradict those reported by Chung et al. 14 Those authors demonstrated that the transmitted light intensity and aBMD are strongly correlated in the measurement of the ultradistal radius using an 850 nm wavelength light. However, in our results, the aBMD estimated only from transmitted light does not show a high coefficient of determination.…”

Section: Discussionmentioning

confidence: 70%

“…reported a correlation between near-infrared light transmittance and aBMD in the ultradistal radius, but they were concerned about the bias from soft tissue. 14 We previously reported that the slope of the intensity distribution formed by diffuse reflected light correlates with BMD but showed nonlinear variation with skin thickness and BMD. 13 …”

Section: Introductionmentioning

confidence: 95%

“…Motivated by the need to measure BMD in a simple and safe manner for the early detection of osteoporosis, optical bone densitometry has been studied. 13 , 14 …”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optical bone densitometry robust to variation of soft tissue using machine learning techniques: validation by Monte Carlo simulation

2022

View full text Add to dashboard Cite

. Significance: To achieve early detection of osteoporosis, a simple bone densitometry method using optics was proposed. However, individual differences in soft tissue structure and optical properties can cause errors in quantitative bone densitometry. Therefore, developing optical bone densitometry that is robust to soft tissue variations is important for the early detection of osteoporosis. Aim: The purpose of this study was to develop an optical bone densitometer that is insensitive to soft tissue, using Monte Carlo simulation and machine learning techniques, and to verify its feasibility. Approach: We propose a method to measure spatially resolved diffuse light from three directions of the biological tissue model and used machine learning techniques to predict bone density from these data. The three directions are backward, forward, and lateral to the direction of ballistic light irradiation. The method was validated using Monte Carlo simulations using synthetic biological tissue models with 1211 different random structural and optical properties. Results: The results were computed after a 10-fold cross-validation. From the simulated optical data, the machine learning model predicted bone density with a coefficient of determination of 0.760. Conclusions: The optical bone densitometry method proposed in this study was found to be robust against individual differences in soft tissue.

show abstract

Section: Discussionmentioning

confidence: 70%

Section: Introductionmentioning

confidence: 95%

See 1 more Smart Citation

Optical bone densitometry robust to variation of soft tissue using machine learning techniques: validation by Monte Carlo simulation

2022

View full text Add to dashboard Cite

show abstract

“…Scientists have proven that the morphological parameters of the biological material are extremely informative in the course of diagnostic research, in particular, to determine in the course of forensic examination the species, sex, time of death and burial in both animal and human medicine (Shanbhogue et al, 2015;Chung et al, 2018). The skeleton is the most informative object for these tasks, because changes in the bones persist for a long time, and soft tissues decay quickly.…”

Section: Discussionmentioning

confidence: 99%

X-ray densitometric indices of proximal phalanx, medial phalanx and ungular pelvic limb bones as criteria for age diagnosis of cattle in forensic veterinary expertise

Yatsenko

Tkachuk

Busol

et al. 2019

Regul. Mech. Biosyst.

View full text Add to dashboard Cite

Morphological parameters of biological material are extremely informative in diagnostic studies, in particular, to determine the species, sex, time of death, the term of burial. The most informative object for these tasks is the skeleton, because changes in the bones are stored for a long time, while soft tissue is subjected to rotting. Bone tissue is the most durable, but at the same time, it is very labile and reacts to all metabolic processes in the body. The object of the study was proximal phalanx, medial phalanx and ungular bone of the pelvic limb of cattle ranging in age from newborn to 12 years old. Radiography of the proximal phalanx, medial phalanx and ungular bones of the pelvic limb was performed on the Arman apparatus. The bones were subjected to X-ray in the lateromedial projection. The inner and outer sections of the tubular bone were determined. The mathematical modeling of the interaction of X-rays and the cortical layer of bones of fingers (proximal phalanx, medial phalanx and ungular) of cattle was carried out in this work. It is established that this process is described by Bouger's law. The physico-mathematical model of proximal phalanx, medial phalanx and ungular bones has been calculated, on the basis of which it was possible to calculate the X-ray densitometric indices of these bones of cattle. The age features of dynamics of X-ray densitometric indices of the proximal phalanx, medial phalanx and ungular bones were established and a method of determining the age of cattle according to this criterion was proposed. A mathematical model for the proximal phalanx, medial phalanx and ungular bones of the pelvic limbs of cattle that can be applied in X-ray densitometry uses: for the average third proximal phalanx – section of heterogeneous tubular structure modeled by a semicircle; for a medial phalanx bone – a section of a triangular shape; for the ungular bone – a heterogeneous structure, the plantar surface is inscribed in a rectangle. The process of interaction of X-rays with the bone structure of the examined pelvic limb bones can be described by Bouguer's law. The developed mathematical modeling of this interaction and the algorithm for its analysis is the basis for determining the age of cattle for X-ray densitometric indices of the proximal phalanx, medial phalanx and ungular bones of pelvic limbs. By X-ray densitometry of the proximal phalanx and medial phalanx bones of the pelvic limbs extremities one can diagnose the age of bovine animals from birth to 5 years, but according to ungular bones – from birth to 10 years. X-ray densitometry of medial phalanx and ungular bones of pelvic limbs can be used for diagnosing bovine cattle in a complex with other morphological, chemical and physical methods of investigation.

show abstract

“…The findings of their investigation revealed a robust correlation between the optical attenuation values and BMD measurements. This suggests that the NIR-based optical attenuation technique has the potential to serve as a valuable indicator of bone density, as assessed by DXA [19] .…”

Section: Introductionmentioning

confidence: 99%

A Study on Intelligent Optical Bone Densitometry

Meitei,

Chang,

Cheong

et al. 2024

IEEE J. Transl. Eng. Health Med.

Self Cite

View full text Add to dashboard Cite

Osteoporosis is a prevalent chronic disease worldwide, particularly affecting the aging population. The gold standard diagnostic tool for osteoporosis is Dual-energy X-ray Absorptiometry (DXA). However, the expensive cost of the DXA machine and the need for skilled professionals to operate it restrict its accessibility to the general public. This paper builds upon previous research and proposes a novel approach for rapidly screening bone density. The method involves utilizing near-infrared light to capture local body information within the human body. Deep learning techniques are employed to analyze the obtained data and extract meaningful insights related to bone density. Our initial prediction, utilizing multi-linear regression, demonstrated a strong correlation (r = 0.98, p-value = 0.003**) with the measured Bone Mineral Density (BMD) obtained from Dual-energy X-ray Absorptiometry (DXA). This indicates a highly significant relationship between the predicted values and the actual BMD measurements. A deep learning-based algorithm is applied to analyze the underlying information further to predict bone density at the wrist, hip, and spine. The prediction of bone densities in the hip and spine holds significant importance due to their status as gold-standard sites for assessing an individual’s bone density. Our prediction rate had an error margin below 10% for the wrist and below 20% for the hip and spine bone density.

show abstract

Near‐infrared bone densitometry: A feasibility study on distal radius measurement

Cited by 8 publications

References 21 publications

Optical bone densitometry robust to variation of soft tissue using machine learning techniques: validation by Monte Carlo simulation

Optical bone densitometry robust to variation of soft tissue using machine learning techniques: validation by Monte Carlo simulation

X-ray densitometric indices of proximal phalanx, medial phalanx and ungular pelvic limb bones as criteria for age diagnosis of cattle in forensic veterinary expertise

A Study on Intelligent Optical Bone Densitometry

Contact Info

Product

Resources

About