Early diagnosis of frailty: Technological and non-intrusive devices for clinical detection.

This work analyses different concepts for frailty diagnosis based on affordable standard technology such as smartphones or wearable devices. The goal is to provide ideas that go beyond classical diagnostic tools such as magnetic resonance imaging or tomography, thus changing the paradigm; enabling the detection of frailty without expensive facilities, in an ecological way for both patients and medical staff and even with continuous monitoring. Fried's five-point phenotype model of frailty along with a model based on trials and several classical physical tests were used for device classification. This work provides a starting point for future researchers who will have to try to bridge the gap separating elderly people from technology and medical tests in order to provide feasible, accurate and affordable tools for frailty monitoring for a wide range of users.

Automatic Ankle Angle Detection by Integrated RGB and Depth Camera System.

Depth cameras are developing widely. One of their main virtues is that, based on their data and by applying machine learning algorithms and techniques, it is possible to perform body tracking and make an accurate three-dimensional representation of body movement. Specifically, this paper will use the Kinect v2 device, which incorporates a random forest algorithm for 25 joints detection in the human body. However, although Kinect v2 is a powerful tool, there are circumstances in which the device's design does not allow the extraction of such data or the accuracy of the data is low, as is usually the case with foot position. We propose a method of acquiring this data in circumstances where the Kinect v2 device does not recognize the body when only the lower limbs are visible, improving the ankle angle's precision employing projection lines. Using a region-based convolutional neural network (Mask RCNN) for body recognition, raw data extraction for automatic ankle angle measurement has been achieved. All angles have been evaluated by inertial measurement units (IMUs) as gold standard. For the six tests carried out at different fixed distances between 0.5 and 4 m to the Kinect, we have obtained (mean ± SD) a Pearson's coefficient, r = 0.89 ± 0.04, a Spearman's coefficient, ρ = 0.83 ± 0.09, a root mean square error, RMSE = 10.7 ± 2.6 deg and a mean absolute error, MAE = 7.5 ± 1.8 deg. For the walking test, or variable distance test, we have obtained a Pearson's coefficient, r = 0.74, a Spearman's coefficient, ρ = 0.72, an RMSE = 6.4 deg and an MAE = 4.7 deg.

Modeling and Synthesis of Breast Cancer Optical Property Signatures With Generative Models.

Is it possible to find deterministic relationships between optical measurements and pathophysiology in an unsupervised manner and based on data alone? Optical property quantification is a rapidly growing biomedical imaging technique for characterizing biological tissues that shows promise in a range of clinical applications, such as intraoperative breast-conserving surgery margin assessment. However, translating tissue optical properties to clinical pathology information is still a cumbersome problem due to, amongst other things, inter- and intrapatient variability, calibration, and ultimately the nonlinear behavior of light in turbid media. These challenges limit the ability of standard statistical methods to generate a simple model of pathology, requiring more advanced algorithms. We present a data-driven, nonlinear model of breast cancer pathology for real-time margin assessment of resected samples using optical properties derived from spatial frequency domain imaging data. A series of deep neural network models are employed to obtain sets of latent embeddings that relate optical data signatures to the underlying tissue pathology in a tractable manner. These self-explanatory models can translate absorption and scattering properties measured from pathology, while also being able to synthesize new data. The method was tested on a total of 70 resected breast tissue samples containing 137 regions of interest, achieving rapid optical property modeling with errors only limited by current semi-empirical models, allowing for mass sample synthesis and providing a systematic understanding of dataset properties, paving the way for deep automated margin assessment algorithms using structured light imaging or, in principle, any other optical imaging technique seeking modeling. Code is available.

A novel aerosolisation mitigation device for endoscopic sinus and skull base surgery in the COVID-19 era.

PURPOSE: To provide a novel solution to reduce aerosol exposure in the operating room during endoscopic sinus and skull base procedures in the COVID-19 era. METHODS: We have designed a 3D printable midfacial mask that partially seals the nose, while allowing instrumentation during endoscopic transnasal surgery. The mask when connected to a vacuum system creates a constant negative pressure inside it, sucking out aerosols and gases generated during surgical procedures. Its effectiveness was tested using vapour exhalations by a human volunteer and drilling bone in a head model. The physical barrier effect was measured using fluorescein atomization in a head model. RESULTS: The pressure and airflow measured remained negative inside it in all the different situations tested. The mask was capable of completely evacuating human adult exhalation, and was more effective than the hand suction instrument. However, it was as effective as hand suction instrument at preventing aerosol spread from bone drilling. The physical barrier effect achieved a 72% reduction in the splatter created from the fluorescein atomization. CONCLUSIONS: The mask effectively prevented the spread of aerosols and reduced droplet spread during simulated transnasal endoscopic skull base surgery in laboratory conditions. This device has potential benefits in protecting surgical personnel against airborne transmission of COVID-19 and could be useful in reducing chronic exposure to the hazard of surgical smoke.

Context-free hyperspectral image enhancement for wide-field optical biomarker visualization.

Many well-known algorithms for the color enhancement of hyperspectral measurements in biomedical imaging are based on statistical assumptions that vary greatly with respect to the proportions of different pixels that appear in a given image, and thus may thwart their application in a surgical environment. This article attempts to explain why this occurs with SVD-based enhancement methods, and proposes the separation of spectral enhancement from analysis. The resulting method, termed affinity-based color enhancement, or ACE for short, achieves multi- and hyperspectral image coloring and contrast based on current spectral affinity metrics that can physically relate spectral data to a particular biomarker. This produces tunable, real-time results which are analogous to the current state-of-the-art algorithms, without suffering any of their inherent context-dependent limitations. Two applications of this method are shown as application examples: vein contrast enhancement and high-precision chromophore concentration estimation.

Measuring the Water Content in Wood Using Step-Heating Thermography and Speckle Patterns-Preliminary Results.

The relationship between wood and its degree of humidity is one of the most important aspects of its use in construction and restoration. The wood presents a behavior similar to a sponge, therefore, moisture is related to its expansion and contraction. The nondestructive evaluation (NDE) of the amount of moisture in wood materials allows to define, e.g., the restoration procedures of buildings or artworks. In this work, an integrated study of two non-contact techniques is presented. Infrared thermography (IRT) was able to retrieve thermal parameters of the wood related to the amount of water added to the samples, while the interference pattern generated by speckles was used to quantify the expansion and contraction of wood that can be related to the amount of water. In twenty-seven wooded samples, a known quantity of water was added in a controlled manner. By applying advanced image processing to thermograms and specklegrams, it was possible to determine fundamental values controlling both the absorption of water and the main thermophysical parameters that link the samples. On the one hand, results here shown should be considered preliminary because the experimental values obtained by IRT need to be optimized for low water contents introduced into the samples. On the other hand, speckle interferometry by applying an innovative procedure provided robust results for both high and low water contents.

Custom Scanning Hyperspectral Imaging System for Biomedical Applications: Modeling, Benchmarking, and Specifications.

Prototyping hyperspectral imaging devices in current biomedical optics research requires taking into consideration various issues regarding optics, imaging, and instrumentation. In summary, an ideal imaging system should only be limited by exposure time, but there will be technological limitations (e.g., actuator delay and backlash, network delays, or embedded CPU speed) that should be considered, modeled, and optimized. This can be achieved by constructing a multiparametric model for the imaging system in question. The article describes a rotating-mirror scanning hyperspectral imaging device, its multiparametric model, as well as design and calibration protocols used to achieve its optimal performance. The main objective of the manuscript is to describe the device and review this imaging modality, while showcasing technical caveats, models and benchmarks, in an attempt to simplify and standardize specifications, as well as to incentivize prototyping similar future designs.

Virtual FBGs Using Saturable Absorbers for Sensing with Fiber Lasers.

The spectral narrowing of Fiber Bragg Gratings (FBGs) introduced by unpumped Er-doped fiber (EDF) was analyzed for fiber lasers (FL). Owing to spatial hole burning (SHB), the spectral response of a virtual FBG can be employed for narrowing the band pass filter employed to determine the lasing wavelength of laser cavities. A common FL was mounted to analyze the spectral stability of the method, and a FL sensor for strain and temperature measurements was experimentally characterized to determine the stability of the narrowing effect achieved by the unpumped EDF, which acts as a virtual FBG. The results exhibited remarkably good narrowing effects of the spectral response of uniform FBGs.

On the spectral signature of melanoma: a non-parametric classification framework for cancer detection in hyperspectral imaging of melanocytic lesions.

Early detection and diagnosis is a must in secondary prevention of melanoma and other cancerous lesions of the skin. In this work, we present an online, reservoir-based, non-parametric estimation and classification model that allows for this functionality on pigmented lesions, such that detection thresholding can be tuned to maximize accuracy and/or minimize overall false negative rates. This system has been tested in a dataset consisting of 116 patients and a total of 124 hyperspectral images of nevi, raised nevi and melanomas, detecting up to 100% of the suspicious lesions at the expense of some false positives.

Hessian analysis for the delineation of amorphous anomalies in optical coherence tomography images of the aortic wall.

The aortic aneurysm is a disease originated mainly in the media layer of the aortic wall due to the occurrence of degraded areas of altered biological composition. These anomalous regions affect the structure and strength of the aorta artery, being their occurrence and extension proportional to the arterial vessel health. Optical Coherence Tomography (OCT) is applied to obtain cross-sectional images of the artery wall. The backscattering mechanisms in tissue make aorta images difficult to analyze due to noise and strong attenuation with penetration. The morphology of anomalies in pathological specimens is also diverse with amorphous shapes and varied dimensions, being these factors strongly related with tissue degradation and the aorta physiological condition. Hessian analysis of OCT images from aortic walls is used to assess the accurate delineation of these anomalous regions. A specific metric of the Hessian determinant is used to delineate degraded regions under blurry conditions and noise. A multiscale approach, based on an anisotropic Gaussian kernel filter, is applied to highlight and aggregate all the heterogeneity present in the aortic wall. An accuracy estimator metric has been implemented to evaluate and optimize the delineation process avoiding subjectivity. Finally, a degradation quantification score has been developed to assess aorta wall condition by OCT with validation against common histology.

Identification of vessel wall degradation in ascending thoracic aortic aneurysms with OCT.

Degradation of the wall of human ascending thoracic aorta has been assessed through Optical Coherence Tomography (OCT). OCT images of the media layer of the aortic wall exhibit micro-structure degradation in case of diseased aortas from aneurysmal vessels. The OCT indicator of degradation depends on the dimension of areas of the media layer where backscattered reflectivity becomes smaller due to a disorder on the morphology of elastin, collagen and smooth muscle cells (SMCs). Efficient pre-processing of the OCT images is required to accurately extract the dimension of degraded areas after an optimized thresholding procedure. OCT results have been validated against conventional histological analysis. The OCT qualitative assessment has achieved a pair sensitivity-specificity of 100%-91.6% in low-high degradation discrimination when a threshold of 4965.88µm(2) is selected. This threshold suggests to have physiological meaning. The OCT quantitative evaluation of degradation achieves a correlation of 0.736 between the OCT indicator and the histological score. This in-vitro study can be transferred to the clinical scenario to provide an intraoperative assessment tool to guide cardiovascular surgeons in open repair interventions.

Optical coherence tomography assessment of vessel wall degradation in thoracic aortic aneurysms.

Optical coherence tomography images of human thoracic aorta from aneurysms reveal elastin disorders and smooth muscle cell alterations when visualizing the media layer of the aortic wall. These disorders can be employed as indicators for wall degradation and, therefore, become a hallmark for diagnosis of risk of aneurysm under intraoperative conditions. Two approaches are followed to evaluate this risk: the analysis of the reflectivity decay along the penetration depth and the textural analysis of a two-dimensional spatial distribution of the aortic wall backscattering. Both techniques require preprocessing stages for the identification of the air-sample interface and for the segmentation of the media layer. Results show that the alterations in the media layer of the aortic wall are better highlighted when the textural approach is considered and also agree with a semiquantitative histopathological grading that assesses the degree of wall degradation. The correlation of the co-occurrence matrix attains a sensitivity of 0.906 and specificity of 0.864 when aneurysm automatic diagnosis is evaluated with a receiver operating characteristic curve.

Direct identification of breast cancer pathologies using blind separation of label-free localized reflectance measurements.

Breast tumors are blindly identified using Principal (PCA) and Independent Component Analysis (ICA) of localized reflectance measurements. No assumption of a particular theoretical model for the reflectance needs to be made, while the resulting features are proven to have discriminative power of breast pathologies. Normal, benign and malignant breast tissue types in lumpectomy specimens were imaged ex vivo and a surgeon-guided calibration of the system is proposed to overcome the limitations of the blind analysis. A simple, fast and linear classifier has been proposed where no training information is required for the diagnosis. A set of 29 breast tissue specimens have been diagnosed with a sensitivity of 96% and specificity of 95% when discriminating benign from malignant pathologies. The proposed hybrid combination PCA-ICA enhanced diagnostic discrimination, providing tumor probability maps, and intermediate PCA parameters reflected tissue optical properties.

Efficient dynamic events discrimination technique for fiber distributed Brillouin sensors.

A technique to detect real time variations of temperature or strain in Brillouin based distributed fiber sensors is proposed and is investigated in this paper. The technique is based on anomaly detection methods such as the RX-algorithm. Detection and isolation of dynamic events from the static ones are demonstrated by a proper processing of the Brillouin gain values obtained by using a standard BOTDA system. Results also suggest that better signal to noise ratio, dynamic range and spatial resolution can be obtained. For a pump pulse of 5 ns the spatial resolution is enhanced, (from 0.541 m obtained by direct gain measurement, to 0.418 m obtained with the technique here exposed) since the analysis is concentrated in the variation of the Brillouin gain and not only on the averaging of the signal along the time.

Brillouin frequency shift of standard optical fibers set in water vapor medium.

The dependence of the Brillouin frequency shift (BFS) on UV-cured acrylate coating and uncoated fibers for media that have different water vapor concentrations is experimentally investigated. The BFS is proportional to the temperature within the fiber, but it also depends on the water vapor contained in the surroundings of the fiber. A hypothesis based on the efficiency of the heat transfer due to the different humidity concentration in the media is proposed, and the temperature difference that depends on the heat transfer is quantified in standard fibers. A shift of approximately 0.22 MHz for relative humidity change between 60% and 98% at 20 degrees C is measured.

Feasibility study of imaging spectroscopy to monitor the quality of online welding.

An online welding quality system based on the use of imaging spectroscopy is proposed and discussed. Plasma optical spectroscopy has already been successfully applied in this context by establishing a direct correlation between some spectroscopic parameters, e.g., the plasma electronic temperature and the resulting seam quality. Given that the use of the so-called hyperspectral devices provides both spatial and spectral information, we propose their use for the particular case of arc welding quality monitoring in an attempt to determine whether this technique would be suitable for this industrial situation. Experimental welding tests are presented, and the ability of the proposed solution to identify simulated defects is proved. Detailed spatial analyses suggest that this additional dimension can be used to improve the performance of the entire system.

Multi-Line Fit Model for the Detection of Methane at ν(2) + 2ν(3) Band using Hollow-Core Photonic Bandgap Fibres.

Hollow-core photonic bandgap fibres (HC-PBFs) have emerged as a novel technology in the field of gas sensing. The long interaction pathlengths achievable with these fibres are especially advantageous for the detection of weakly absorbing gases. In this work, we demonstrate the good performance of a HC-PBF in the detection of the ν(2) + 2ν(3) band of methane, at 1.3 µm. The Q-branch manifold, at 1331.55 nm, is targeted for concentration monitoring purposes. A computationally optimized multi-line model is used to fit the Q-branch. Using this model, a detection limit of 98 ppmv (parts per million by volume) is estimated.

Defect Detection in Arc-Welding Processes by Means of the Line-to-Continuum Method and Feature Selection.

Plasma optical spectroscopy is widely employed in on-line welding diagnostics. The determination of the plasma electron temperature, which is typically selected as the output monitoring parameter, implies the identification of the atomic emission lines. As a consequence, additional processing stages are required with a direct impact on the real time performance of the technique. The line-to-continuum method is a feasible alternative spectroscopic approach and it is particularly interesting in terms of its computational efficiency. However, the monitoring signal highly depends on the chosen emission line. In this paper, a feature selection methodology is proposed to solve the uncertainty regarding the selection of the optimum spectral band, which allows the employment of the line-to-continuum method for on-line welding diagnostics. Field test results have been conducted to demonstrate the feasibility of the solution.

Gas Sensor Based on Photonic Crystal Fibres in the 2ν(3) and ν(2) + 2ν(3) Vibrational Bands of Methane.

In this work, methane detection is performed on the 2ν(3) and ν(2) + 2ν(3) absorption bands in the Near-Infrared (NIR) wavelength region using an all-fibre optical sensor. Hollow-core photonic bandgap fibres (HC-PBFs) are employed as gas cells due to their compactness, good integrability in optical systems and feasibility of long interaction lengths with gases. Sensing in the 2ν(3) band of methane is demonstrated to achieve a detection limit one order of magnitude better than that of the ν(2) + 2ν(3) band. Finally, the filling time of a HC-PBF is demonstrated to be dependent on the fibre length and geometry.