Cupping therapy is one form of alternative medicine that is used widely across the world. Although the applications of cupping therapy including pain relief have a 1000‐year history, the therapeutic effect of cupping is still questionable due to a lack of scientific evidence. Therefore, in the present study, we embedded a near‐infrared spectroscopic sensor into a suction cup to monitor the hemodynamic changes on the treated site while the hemodynamics at the surrounding tissue of the cup was also simultaneously monitored by another near‐infrared spectroscopic sensor. The results from 10 healthy male subjects show a dramatic increase of the oxy‐hemoglobin (OHb) and deoxy‐hemoglobin (RHb) concentrations at the treatment site while the OHb and RHb levels were decreased at the surrounding tissue. Moreover, after the treatment, we observed that the OHb concentrations were maintained at a higher level than before treatment at both sites, which may demonstrate how cupping therapy works for treatment. In summary, the results showed that cupping therapy increases blood volume and tissue oxygenation at the treatment site while those were slightly decreased at the surrounding tissue. This study showed that the embedding of near‐infrared spectroscopy in a cupping system could offer a better understanding of the mechanism of cupping therapy.
Significance: Melanin and hemoglobin have been measured as important diagnostic indicators of facial skin conditions for aesthetic and diagnostic purposes. Commercial clinical equipment provides reliable analysis results, but it has several drawbacks: exclusive to the acquisition system, expensive, and computationally intensive.Aim: We propose an approach to alleviate those drawbacks using a deep learning model trained to solve the forward problem of light-tissue interactions. The model is structurally extensible for various light sources and cameras and maintains the input image resolution for medical applications.Approach: A facial image is divided into multiple patches and decomposed into melanin, hemoglobin, shading, and specular maps. The outputs are reconstructed into a facial image by solving the forward problem over skin areas. As learning progresses, the difference between the reconstructed image and input image is reduced, resulting in the melanin and hemoglobin maps becoming closer to their distribution of the input image. Results:The proposed approach was evaluated on 30 subjects using the professional clinical system, VISIA VAESTRO. The correlation coefficients for melanin and hemoglobin were found to be 0.932 and 0.857, respectively. Additionally, this approach was applied to simulated images with varying amounts of melanin and hemoglobin. Conclusion:The proposed approach showed high correlation with the clinical system for analyzing melanin and hemoglobin distribution, indicating its potential for accurate diagnosis. Further calibration studies using clinical equipment can enhance its diagnostic ability. The structurally extensible model makes it a promising tool for various image acquisition conditions.
The integral imaging system has received considerable research attention because it can be applied to real-time three-dimensional image displays with a continuous view angle without supplementary devices. Most previous approaches place a physical micro-lens array in front of the image, where each lens looks different depending on the viewing angle. A computational integral imaging system with a virtual micro-lens arrays has been proposed in order to provide flexibility for users to change micro-lens arrays and focal length while reducing distortions due to physical mismatches with the lens arrays. However, computational integral imaging methods only represent part of the whole image because the size of virtual lens arrays is much smaller than the given large-scale images when dealing with large-scale images. As a result, the previous approaches produce sub-aperture images with a small field of view and need additional devices for depth information to apply to integral imaging pickup systems. In this paper, we present a single image-based computational RGB-D integral imaging pickup system for a large field of view in real time. The proposed system comprises three steps: deep learning-based automatic depth map estimation from an RGB input image without the help of an additional device, a hierarchical integral imaging system for a large field of view in real time, and post-processing for optimized visualization of the failed pickup area using an inpainting method. Quantitative and qualitative experimental results verify the proposed approach’s robustness.
Skin elasticity has been regarded as one of the main indicators of skin condition. Current measurement devices for skin elasticity are mostly expensive for home‐use and should contact the skin surface. As a first step to develop improved methods, we focus on the relation between skin elasticity and the entropy of skin images. Reduced skin elasticity causes wrinkles. It spreads frequency components and increases their randomness in the frequency domain. The randomness is quantified as entropy, which is a measure of the disorder of a system in physics. Therefore, skin elasticity is expected to have a negative relation with entropy. This tendency can be improved by applying penetration depth characteristics according to the wavelength of light. From cheeks and forehead of 12 Korean adults, skin images are acquired with three different light sources (470 nm, 870 nm and broadband light) and skin elasticity is measured. The root mean square error between the measured data and the fitted model is “0.27” (870 nm), “0.49” (broadband light) and “1.42” (470 nm). Furthermore, the results are analyzed by classifying by sex, age and measurement area. This study demonstrates the possibility of developing noncontact home‐use devices to measure skin elasticity in the future.
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