Quantitative Capillary Refill Time with image-based finger force estimation.

Skin color observation provides a simple and non-invasive method to estimate the health status of patients. Capillary Refill Time (CRT) is widely used as an indicator of pathophysiological conditions, especially in emergency patients. While the measurement of CRT is easy to perform, its evaluation is highly subjective. This study proposes a method to aid quantified CRT measurement using an RGB camera. The procedure consists in applying finger compression to the forearm, and the CRT is calculated based on the skin color change after the pressure release. We estimate compression applied by a finger from its fingernail color change during compression. Our study shows a step towards camera-based quantitative CRT for untrained individuals.

Skin-color-independent robust assessment of capillary refill time.

Capillary Refill Time (CRT) assesses peripheral perfusion in resource-limited settings. However, the repeatability and reproducibility of CRT measurements are limited for individuals with darker skin. This paper presents quantitative CRT measurements demonstrating good performance and repeatability across all Fitzpatrick skin phototypes. The study involved 22 volunteers and utilized controlled compression at 7 kPa, an RGB video camera, and cocircular polarized white LED light. CRT was determined by calculating the time constant of an exponential regression applied to the mean pixel intensity of the green (G) channel. An adaptive algorithm identifies the optimal regression region for noise reduction, and flags inappropriate readings. The results indicate that 80% of the CRT readings fell within a 20% range of the expected CRT value. The repetition standard deviation was 17%. These findings suggest the potential for developing reliable and reproducible quantitative CRT methods for robust measurements in patient triage, monitoring, and telehealth applications.

Arterial pulsation modulates the optical attenuation coefficient of skin.

Photoplethysmographic (PPG) signals arise from the modulation of light reflectivity on the skin due to changes of physiological origin. Imaging plethysmography (iPPG) is a video-based PPG method that can remotely monitor vital signs in a non-invasive manner. iPPG signals result from skin reflectivity modulation. The origin of such reflectivity modulation is still a subject of debate. Here, we have used optical coherence tomography (OCT) imaging to find whether iPPG signals may result from skin optical properties being directly or indirectly modulated by arterial transmural pressure propagation. The light intensity across the tissue was modeled through a simple exponential decay (Beer-Lambert law) to analyze in vivo the modulation of the optical attenuation coefficient of the skin by arterial pulsation. The OCT transversal images were acquired from a forearm of three subjects in a pilot study. The results show that the optical attenuation coefficient of skin changes at the same frequency as the arterial pulsation due to transmural pressure propagation (local ballistographic effect), but we cannot discard the contribution of global ballistographic effects.

Optical attenuation coefficient of skin under low compression.

In various biomedical optics therapies, knowledge of how light is absorbed or scattered by tissues is crucial. Currently, it is suspected that a low compression applied to the skin surface may improve light delivery into tissue. However, the minimum pressure needed to be applied to significantly increase the light penetration into the skin has not been determined. In this study, we used optical coherence tomography (OCT) to measure the optical attenuation coefficient of the human forearm dermis in a low compression regime (<8k P a). Our results show low pressures such as 4 kPa to 8 kPa are sufficient to significantly increase light penetration by decreasing the attenuation coefficient by at least 1.0m m -1.

Blood pressure estimation by spatial pulse-wave dynamics in a facial video.

We propose a remote method to estimate continuous blood pressure (BP) based on spatial information of a pulse-wave as a function of time. By setting regions of interest to cover a face in a mutually exclusive and collectively exhaustive manner, RGB facial video is converted into a spatial pulse-wave signal. The spatial pulse-wave signal is converted into spatial signals of contours of each segmented pulse beat and relationships of each segmented pulse beat. The spatial signal is represented as a time-continuous value based on a representation of a pulse contour in a time axis and a phase axis and an interpolation along with the time axis. A relationship between the spatial signals and BP is modeled by a convolutional neural network. A dataset was built to demonstrate the effectiveness of the proposed method. The dataset consists of continuous BP and facial RGB videos of ten healthy volunteers. The results show an adequate estimation of the performance of the proposed method when compared to the ground truth in mean BP, in both the correlation coefficient (0.85) and mean absolute error (5.4 mmHg). For comparison, the dataset was processed using conventional pulse features, and the estimation error produced by our method was significantly lower. To visualize the root source of the BP signals used by our method, we have visualized spatial-wise and channel-wise contributions to the estimation by the deep learning model. The result suggests the spatial-wise contribution pattern depends on the blood pressure, while the pattern of pulse contour-wise contribution pattern reflects the relationship between percussion wave and dicrotic wave.

Tissue Characterization by Low-Frequency Acoustic Waves Generated by a Single High-Frequency Focused Ultrasound Beam.

The mechanical properties of biological tissues are fingerprints of certain pathologic processes. Ultrasound systems have been used as a non-invasive technique to both induce kilohertz-frequency mechanical vibrations and detect waves resulting from interactions with biological structures. However, existing methodologies to produce kilohertz-frequency mechanical vibrations using ultrasound require the use of variable-frequency, dual-frequency and high-power systems. Here, we propose and demonstrate the use of bursts of megahertz- frequency acoustic radiation to observe kilohertz-frequency mechanical responses in biological tissues. Femoral bones were obtained from 10 healthy mice and 10 mice in which osteoporosis had been induced. The bones' porosity, trabecular number, trabecular spacing, connectivity and connectivity density were determined using micro-computed tomography (µCT). The samples were irradiated with short, focused acoustic radiation pulses (f = 3.1 MHz, t = 15 µs), and the low-frequency acoustic response (1-100 kHz) was acquired using a dedicated hydrophone. A strong correlation between the spectral maps of the acquired signals and the µCT data was found. In a subsequent evaluation, soft tissue stiffness measurements were performed with a gel wax-based tissue-mimicking phantom containing three spherical inclusions of the same type of gel but different densities and Young's moduli, yet with approximately the same echogenicity. Conventional B-mode ultrasound was unable to image the inclusions, while the novel technique proposed here showed good image contrast.

Improved susceptible-infectious-susceptible epidemic equations based on uncertainties and autocorrelation functions.

Compartmental equations are primary tools in the study of disease spreading processes. They provide accurate predictions for large populations but poor results whenever the integer nature of the number of agents is evident. In the latter instance, uncertainties are relevant factors for pathogen transmission. Starting from the agent-based approach, we investigate the role of uncertainties and autocorrelation functions in the susceptible-infectious-susceptible (SIS) epidemic model, including their relationship with epidemiological variables. We find new differential equations that take uncertainties into account. The findings provide improved equations, offering new insights on disease spreading processes.

Efficient method for comprehensive computation of agent-level epidemic dissemination in networks.

Susceptible-infected (SI) and susceptible-infected-susceptible (SIS) are simple agent-based models often employed in epidemic studies. Both models describe the time evolution of infectious diseases in networks whose vertices are either susceptible (S) or infected (I) agents. Precise estimation for disease spreading is one of the major goals in epidemic studies but often restricted to heavy numerical simulations. Analytic methods using operatorial content are subject to the asymmetric eigenvalue problem, limiting the use of perturbative methods. Numerical methods are limited to small populations, since the vector space increases exponentially with population size N. Here, we propose the use of the squared norm of the probability vector to obtain an algebraic equation, which permits the evaluation of stationary states in Markov processes. The equation requires the eigenvalues of symmetrized time generators and takes full advantage of symmetries, reducing the time evolution to an O(N) sparse problem. The calculation of eigenvalues employs quantum many-body techniques, while the standard perturbation theory accounts for small modifications to the network topology.