The influence of antenna gain and beamwidth used in OSSEUS in the screening process for osteoporosis

Pinheiro, Bruno de Melo; Campos, Antônio Luiz P. S.; Carvalho, Dionísio Dias Aires de; Cruz, Agnaldo S.; Valentim, Ricardo Alexsandro de Medeiros; Veras, Nícolas Vinícius Rodrigues; Santos, João Paulo Queiroz dos

doi:10.1038/s41598-021-98204-4

Cited by 7 publications

(5 citation statements)

References 14 publications

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“…the measurement is precise to two decimal points regarding the voltage value measured at the output of the power detector. Concerning the Osseus shielding, it is capable of attenuating external signals by more than 40 dB 29 . At the end of each collection, the result of the DXA exam (GE Lunar DPX Pro) was noted on the form.…”

Section: Methodsmentioning

confidence: 99%

Osteoporosis screening using machine learning and electromagnetic waves

Albuquerque

Carvalho

Cruz

et al. 2023

Sci Rep

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Osteoporosis is a disease characterized by impairment of bone microarchitecture that causes high socioeconomic impacts in the world because of fractures and hospitalizations. Although dual-energy X-ray absorptiometry (DXA) is the gold standard for diagnosing the disease, access to DXA in developing countries is still limited due to its high cost, being present only in specialized hospitals. In this paper, we analyze the performance of Osseus, a low-cost portable device based on electromagnetic waves that measures the attenuation of the signal that crosses the medial phalanx of a patient’s middle finger and was developed for osteoporosis screening. The analysis is carried out by predicting changes in bone mineral density using Osseus measurements and additional common risk factors used as input features to a set of supervised classification models, while the results from DXA are taken as target (real) values during the training of the machine learning algorithms. The dataset consisted of 505 patients who underwent osteoporosis screening with both devices (DXA and Osseus), of whom 21.8% were healthy and 78.2% had low bone mineral density or osteoporosis. A cross-validation with k-fold = 5 was considered in model training, while 20% of the whole dataset was used for testing. The obtained performance of the best model (Random Forest) presented a sensitivity of 0.853, a specificity of 0.879, and an F1 of 0.859. Since the Random Forest (RF) algorithm allows some interpretability of its results (through the impurity check), we were able to identify the most important variables in the classification of osteoporosis. The results showed that the most important variables were age, body mass index, and the signal attenuation provided by Osseus. The RF model, when used together with Osseus measurements, is effective in screening patients and facilitates the early diagnosis of osteoporosis. The main advantages of such early screening are the reduction of costs associated with exams, surgeries, treatments, and hospitalizations, as well as improved quality of life for patients.

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Section: Methodsmentioning

confidence: 99%

Osteoporosis screening using machine learning and electromagnetic waves

Albuquerque

Carvalho

Cruz

et al. 2023

Sci Rep

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show abstract

“…Due to the conductive nature of human tissue layers, especially skin and muscle, where higher water content results in higher conductivity, microwave power is attenuated as it penetrates the tissue, thus high energy concentration and penetration are needed for precise detection. Penetration depth and behavior of microwaves in the vicinity and inside the body tissues have been studied in the literature [1][2][3]. In [1], a split ring resonator (SRR) probe is employed to investigate the penetration depth of microwaves in the thigh and distal femur bones.…”

Section: Introductionmentioning

confidence: 99%

“…Microstrip patch sensors are also used in biomedical applications. In [3], the performance of a microstrip patch antenna array as an osteoporosis screening sensor is discussed, in which miniaturization and improvement of radiation parameters in addition to RF signal reception are evaluated. Raster scanning of microstrip patch antennas for bone fracture detection is discussed in [4,5].…”

Section: Introductionmentioning

confidence: 99%

Design of a Novel Dual-Polarized Microwave Sensor for Human Bone Fracture Detection Using Reactive Impedance Surfaces

Moqadam

Kazemi

2023

Preprint

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In this paper, a novel miniaturized dual-polarized transceiver sensor using a reactive impedance surface (RIS) is presented for detecting fractures in human bone tissues. A RIS structure comprised of elegantly arranged rectangular patch elements are placed at the back of the radiating square patch antenna. The RIS reduces the overall dimensions of the sensor by 30% compared to a conventional patch resonator. Reducing the size results in better detection of bone fractures. Furthermore, a novel dielectric plano-concave lens is introduced between the patch antenna and the body tissue as a matching medium to concentrate and penetrate higher levels of EM power in the tissues as well as conform the curvy shape of the body. A few via holes, filled with a lossy dielectric material similar to human fat tissue, are embedded in the lens to increase the insertion loss in the presence of the cracked bone for easier crack detection. Because of the higher conductivity of blood filling the crack, insertion loss increases when scanning the damaged area. Without the use of the proposed lens, this increase in loss compared to normal bone is negligible and the crack is undetectable. Hence, by utilizing the proposed lens, it is possible to sense the difference between cracked and normal bones, and as a result, the resolution of the images in the raster scan is improved. In the proposed configuration, two sensors as a transmitter and a receiver are placed opposite each other on the tissue and move along the tissue simultaneously. The amount of EM power collected by the receiver can be determined by measuring the S-parameters; these parameters are used for construction of the images of the fractured bones. Several cases of bone fractures with horizontal and vertical orientations and width of at least 1 mm are evaluated through full-wave simulations. Narrow cracks in the millimeter range are precisely detected and localized with the help of the proposed sensor. The experimental measurements are carried out on a semi-solid modified human mimicking phantom. Scattering parameters are evaluated at each step of the raster scan to sense the difference between the presence or absence of cracks in the bone tissue. Bone fractures are clearly observed in the images constructed from the phases of the insertion losses between two transceiver sensors and from the contrast between the crack and the surrounding tissue. Orientation of the fracture is also determined by switching between two orthogonal polarizations. Great agreement between the experimental and simulation results is observed, which validates the reliability of the proposed system. Stable performance in the vicinity of different human bodies and higher concentration and penetration of power to increase detection accuracy are the prominent features of the proposed sensor.

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“…Therefore, precise detection requires high energy concentration and penetration. The literature has extensively studied the penetration depth and behavior of microwaves in the vicinity and inside the body tissues 1 – 3 . For instance, 1 employed a split ring resonator (SRR) probe to investigate the penetration depth of microwaves in the thigh and distal femur bones while evaluating the recovery of extremity trauma patients.…”

Section: Introductionmentioning

confidence: 99%

“…Microstrip patch sensors have also found applications in the field of biomedical research. In 3 , a microstrip patch antenna array was evaluated as a screening sensor for osteoporosis, with a focus on miniaturization, and improvement of radiation characteristics and RF signal reception. Other studies 4 , 5 have explored the use of raster scanning with microstrip patch antennas for bone fracture detection.…”

Section: Introductionmentioning

confidence: 99%

Design of a novel dual-polarized microwave sensor for human bone fracture detection using reactive impedance surfaces

Moqadam

Kazemi

2023

Sci Rep

View full text Add to dashboard Cite

This paper presents a novel miniaturized dual-polarized transceiver sensor system for detecting fractures in human bone tissues. The system features a patch antenna and a Reactive Impedance Surface (RIS) layer that reduces its size by 30% compared to conventional designs, resulting in enhanced fracture detection accuracy. Additionally, the system includes a dielectric plano-concave lens that adapts to the human body and improves impedance matching for optimal performance. The lens contains via holes filled with a lossy dielectric material similar to human fat tissue, which concentrates electromagnetic (EM) power and increases penetration depth for more effective crack detection. To detect fractures, two identical sensors are placed opposite each other on the tissue and moved simultaneously. The amount of EM power collected by the receiver sensor is measured using S-parameters; the transmission coefficient (S21) phases and contrast between the crack and surrounding tissue are used to construct images of fractured bones. Full-wave simulations and experimental measurements on a semi-solid human arm mimicking phantom demonstrate the proposed dual-polarized sensor's ability to detect the location and orientation of narrow cracks in the millimeter range. The system exhibits reliable performance across different human bodies.

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The influence of antenna gain and beamwidth used in OSSEUS in the screening process for osteoporosis

Cited by 7 publications

References 14 publications

Osteoporosis screening using machine learning and electromagnetic waves

Osteoporosis screening using machine learning and electromagnetic waves

Design of a Novel Dual-Polarized Microwave Sensor for Human Bone Fracture Detection Using Reactive Impedance Surfaces

Design of a novel dual-polarized microwave sensor for human bone fracture detection using reactive impedance surfaces

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