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Hyperspectral imaging (HSI) has become a very compelling technique in different scientific areas; indeed, many researchers use it in the fields of remote sensing, agriculture, forensics, and medicine. In the latter, HSI plays a crucial role as a diagnostic support and for surgery guidance. However, the computational effort in elaborating hyperspectral data is not trivial. Furthermore, the demand for detecting diseases in a short time is undeniable. In this paper, we take up this challenge by parallelizing three machine-learning methods among those that are the most intensively used: Support Vector Machine (SVM), Random Forest (RF), and eXtreme Gradient Boosting (XGB) algorithms using the Compute Unified Device Architecture (CUDA) to accelerate the classification of hyperspectral skin cancer images. They all showed a good performance in HS image classification, in particular when the size of the dataset is limited, as demonstrated in the literature. We illustrate the parallelization techniques adopted for each approach, highlighting the suitability of Graphical Processing Units (GPUs) to this aim. Experimental results show that parallel SVM and XGB algorithms significantly improve the classification times in comparison with their serial counterparts. [ABSTRACT FROM AUTHOR]

Ambient Assisted Living is a concept that focuses on using technology to support and enhance the quality of life and well-being of frail or elderly individuals in both indoor and outdoor environments. It aims at empowering individuals to maintain their independence and autonomy while ensuring their safety and providing assistance when needed. Human Activity Recognition is widely regarded as the most popular methodology within the field of Ambient Assisted Living. Human Activity Recognition involves automatically detecting and classifying the activities performed by individuals using sensor-based systems. Researchers have employed various methodologies, utilizing wearable and/or non-wearable sensors, and employing algorithms ranging from simple threshold-based techniques to more advanced deep learning approaches. In this review, literature from the past decade is critically examined, specifically exploring the technological aspects of Human Activity Recognition in Ambient Assisted Living. An exhaustive analysis of the methodologies adopted, highlighting their strengths and weaknesses is provided. Finally, challenges encountered in the field of Human Activity Recognition for Ambient Assisted Living are thoroughly discussed. These challenges encompass issues related to data collection, model training, real-time performance, generalizability, and user acceptance. Miniaturization, unobtrusiveness, energy harvesting and communication efficiency will be the crucial factors for new wearable solutions. [ABSTRACT FROM AUTHOR]

The SARS-CoV-2 pandemic challenged health systems worldwide, thus advocating for practical, quick and highly trustworthy diagnostic instruments to help medical personnel. It features a long incubation period and a high contagion rate, causing bilateral multi-focal interstitial pneumonia, generally growing into acute respiratory distress syndrome (ARDS), causing hundreds of thousands of casualties worldwide. Guidelines for first-line diagnosis of pneumonia suggest Chest X-rays (CXR) for patients exhibiting symptoms. Potential alternatives include Computed Tomography (CT) scans and Lung UltraSound (LUS). Deep learning (DL) has been helpful in diagnosis using CT scans, LUS, and CXR, whereby the former commonly yields more precise results. CXR and CT scans present several drawbacks, including high costs. Radiation-free LUS imaging requires high expertise, and physicians thus underutilise it. LUS demonstrated a strong correlation with CT scans and reliability in pneumonia detection, even in the early stages. Here, we present an LUS video-classification approach based on contemporary DL strategies in close collaboration with Fondazione IRCCS Policlinico San Matteo's Emergency Department (ED) of Pavia. This research addressed SARS-CoV-2 patterns detection, ranked according to three severity scales by operating a trustworthy dataset comprising ultrasounds from linear and convex probes in 5400 clips from 450 hospitalised subjects. The main contributions of this study are related to the adoption of a standardised severity ranking scale to evaluate pneumonia. This evaluation relies on video summarisation through key-frame selection algorithms. Then, we designed and developed a video-classification architecture which emerged as the most promising. In contrast, the literature primarily concentrates on frame-pattern recognition. By using advanced techniques such as transfer learning and data augmentation, we were able to achieve an F1-Score of over 89% across all classes. [ABSTRACT FROM AUTHOR]

Currently, one of the most common causes of death worldwide is cancer. The development of innovative methods to support the early and accurate detection of cancers is required to increase the recovery rate of patients. Several studies have shown that medical Hyperspectral Imaging (HSI) combined with artificial intelligence algorithms is a powerful tool for cancer detection. Various preprocessing methods are commonly applied to hyperspectral data to improve the performance of the algorithms. However, there is currently no standard for these methods, and no studies have compared them so far in the medical field. In this work, we evaluated different combinations of preprocessing steps, including spatial and spectral smoothing, Min-Max scaling, Standard Normal Variate normalization, and a median spatial smoothing technique, with the goal of improving tumor detection in three different HSI databases concerning colorectal, esophagogastric, and brain cancers. Two machine learning and deep learning models were used to perform the pixel-wise classification. The results showed that the choice of preprocessing method affects the performance of tumor identification. The method that showed slightly better results with respect to identifing colorectal tumors was Median Filter preprocessing (0.94 of area under the curve). On the other hand, esophagogastric and brain tumors were more accurately identified using Min-Max scaling preprocessing (0.93 and 0.92 of area under the curve, respectively). However, it is observed that the Median Filter method smooths sharp spectral features, resulting in high variability in the classification performance. Therefore, based on these results, obtained with different databases acquired by different HSI instrumentation, the most relevant preprocessing technique identified in this work is Min-Max scaling. [ABSTRACT FROM AUTHOR]

Featured Application: Machine Learning-based diagnostic tool to predict SARS-CoV-2 positivity and the need of hospitalized patients for oxygen therapy when managing constrained resources in emergency departments in contingency periods. The COVID-19 pandemic highlighted an urgent need for reliable diagnostic tools to minimize viral spreading. It is mandatory to avoid cross-contamination between patients and detect COVID-19 positive individuals to cluster people by prognosis and manage the emergency department's resources. Fondazione IRCCS Policlinico San Matteo Hospital's Emergency Department (ED) of Pavia let us evaluate the exploitation of machine learning algorithms on a clinical dataset gathered from laboratory-confirmed rRT-PCR test patients, collected from 1 March to 30 June 2020. Physicians examined routine blood tests, clinical history, symptoms, arterial blood gas (ABG) analysis, and lung ultrasound quantitative examination. We developed two diagnostic tools for COVID-19 detection and oxygen therapy prediction, namely, the need for ventilation support due to lung involvement. We obtained promising classification results with F1 score levels meeting 92%, and we also engineered a user-friendly interface for healthcare providers during daily screening operations. This research proved machine learning models as a potential screening methodology during contingency times. [ABSTRACT FROM AUTHOR]

Cancer originates from the uncontrolled growth of healthy cells into a mass. Chromophores, such as hemoglobin and melanin, characterize skin spectral properties, allowing the classification of lesions into different etiologies. Hyperspectral imaging systems gather skin-reflected and transmitted light into several wavelength ranges of the electromagnetic spectrum, enabling potential skin-lesion differentiation through machine learning algorithms. Challenged by data availability and tiny inter and intra-tumoral variability, here we introduce a pipeline based on deep neural networks to diagnose hyperspectral skin cancer images, targeting a handheld device equipped with a low-power graphical processing unit for routine clinical testing. Enhanced by data augmentation, transfer learning, and hyperparameter tuning, the proposed architectures aim to meet and improve the well-known dermatologist-level detection performances concerning both benign-malignant and multiclass classification tasks, being able to diagnose hyperspectral data considering real-time constraints. Experiments show 87% sensitivity and 88% specificity for benign-malignant classification and specificity above 80% for the multiclass scenario. AUC measurements suggest classification performance improvement above 90% with adequate thresholding. Concerning binary segmentation, we measured skin DICE and IOU higher than 90%. We estimated 1.21 s, at most, consuming 5 Watts to segment the epidermal lesions with the U-Net++ architecture, meeting the imposed time limit. Hence, we can diagnose hyperspectral epidermal data assuming real-time constraints. [ABSTRACT FROM AUTHOR]

The reproduction of the brain 'sactivity and its functionality is the main goal of modern neuroscience. To this aim, several models have been proposed to describe the activity of single neurons at different levels of detail. Then, single neurons are linked together to build a network, in order to reproduce complex behaviors. In the literature, different network-building rules and models have been described, targeting realistic distributions and connections of the neurons. In particular, the Granular layEr Simulator (GES) performs the granular layer network reconstruction considering biologically realistic rules to connect the neurons. Moreover, it simulates the network considering the Hodgkin–Huxley model. The work proposed in this paper adopts the network reconstruction model of GES and proposes a simulation module based on Leaky Integrate and Fire (LIF) model. This simulator targets the reproduction of the activity of large scale networks, exploiting the GPU technology to reduce the processing times. Experimental results show that a multi-GPU system reduces the simulation of a network with more than 1.8 million neurons from approximately 54 to 13 h. [ABSTRACT FROM AUTHOR]

In recent years, researchers designed several artificial intelligence solutions for healthcare applications, which usually evolved into functional solutions for clinical practice. Furthermore, deep learning (DL) methods are well-suited to process the broad amounts of data acquired by wearable devices, smartphones, and other sensors employed in different medical domains. Conceived to serve the role of diagnostic tool and surgical guidance, hyperspectral images emerged as a non-contact, non-ionizing, and label-free technology. However, the lack of large datasets to efficiently train the models limits DL applications in the medical field. Hence, its usage with hyperspectral images is still at an early stage. We propose a deep convolutional generative adversarial network to generate synthetic hyperspectral images of epidermal lesions, targeting skin cancer diagnosis, and overcome small-sized datasets challenges to train DL architectures. Experimental results show the effectiveness of the proposed framework, capable of generating synthetic data to train DL classifiers. [ABSTRACT FROM AUTHOR]

In modern computational modeling, neuroscientists need to reproduce long-lasting activity of large-scale networks, where neurons are described by highly complex mathematical models. These aspects strongly increase the computational load of the simulations, which can be efficiently performed by exploiting parallel systems to reduce the processing times. Graphics Processing Unit (GPU) devices meet this need providing on desktop High Performance Computing. In this work, authors describe a novel Granular layEr Simulator development implemented on a multi-GPU system capable of reconstructing the cerebellar granular layer in a 3D space and reproducing its neuronal activity. The reconstruction is characterized by a high level of novelty and realism considering axonal/dendritic field geometries, oriented in the 3D space, and following convergence/divergence rates provided in literature. Neurons are modeled using Hodgkin and Huxley representations. The network is validated by reproducing typical behaviors which are well-documented in the literature, such as the center-surround organization. The reconstruction of a network, whose volume is 600 × 150 × 1,200 μm3 with 432,000 granules, 972 Golgi cells, 32,399 glomeruli, and 4,051 mossy fibers, takes 235 s on an Intel i9 processor. The 10 s activity reproduction takes only 4.34 and 3.37 h exploiting a single and multi-GPU desktop system (with one or two NVIDIA RTX 2080 GPU, respectively). Moreover, the code takes only 3.52 and 2.44 h if run on one or two NVIDIA V100 GPU, respectively. The relevant speedups reached (up to ~38× in the single-GPU version, and ~55× in the multi-GPU) clearly demonstrate that the GPU technology is highly suitable for realistic large network simulations. [ABSTRACT FROM AUTHOR]

Featured Application: Cysts detection in the retina is of crucial importance for the correct evaluation of microvascular impairment in patients with macular edema on images acquired by optical coherence tomography angiography. We describe a fully automatic method for cysts detection able to compensate for the presence of motion artifacts. The correct detection of cysts in Optical Coherence Tomography Angiography images is of crucial importance for allowing reliable quantitative evaluation in patients with macular edema. However, this is a challenging task, since the commercially available software only allows manual cysts delineation. Moreover, even small eye movements can cause motion artifacts that are not always compensated by the commercial software. In this paper, we propose a novel algorithm based on the use of filters and morphological operators, to eliminate the motion artifacts and delineate the cysts contours/borders. The method has been validated on a dataset including 194 images from 30 patients, comparing the algorithm results with the ground truth produced by the medical doctors. The Jaccard index between the algorithmic and the manual detection is 98.97%, with an overall accuracy of 99.62%. [ABSTRACT FROM AUTHOR]

Accidental falls are the main cause of fatal and nonfatal injuries, which typically lead to hospital admissions among elderly people. A wearable system capable of detecting unintentional falls and sending remote notifications will clearly improve the quality of the life of such subjects and also helps to reduce public health costs. In this paper, we describe an edge computing wearable system based on deep learning techniques. In particular, we give special attention to the description of the classification and communication modules, which have been developed by keeping in mind the limits in terms of computational power, memory occupancy, and power consumption of the designed wearable device. The system thus developed is capable of classifying 3D-accelerometer signals in real-time and to issue remote alerts while keeping power consumption low and improving on the present state-of-the-art solutions in the literature. [ABSTRACT FROM AUTHOR]

Purpose: To investigate choriocapillaris (CC) perfusion, by evaluating flow voids (FV), in eyes with intermediate age‐related macular degeneration (iAMD) using swept‐source optical coherence tomography angiography (SS‐OCT‐A). Methods: Patients with bilateral or unilateral iAMD and normal controls underwent SS‐OCT and OCT‐A examination. Choriocapillaris (CC) FVs were quantitatively assessed on OCT‐A images using matlab (version 2017b; MathWorks, Natick, MA, USA), after a preprocessing aimed at compensating for CC attenuation artefacts. Three different thresholds [1 standard deviation (SD), 1.25 SD and 1.5 SD] were applied. Final FV percentage (FV%) was calculated as the ratio between area with absent flow and total scanned area. Results: Of 41 patients with iAMD and 16 normal subjects enrolled in the study, 39 eyes (39 patients) with iAMD and all 16 normal eyes (16 control subjects) were included in the final analysis. Mean FV% (1 SD) was 13.45 ± 0.66 in controls, 14.19 ± 1.23 in bilateral iAMD and 14.21 ± 0.99 in unilateral iAMD (p = 0.03, for difference between controls and bilateral iAMD). Mean FV% (1.25 SD) was 6.55 ± 0.65 in controls, 7.33 ± 1.4 in bilateral iAMD and 7.06 ± 1.4 in unilateral iAMD (p = 0.048, for difference between controls and bilateral iAMD). Mean FV% (1.5 SD) was 2.71 ± 0.82 in controls, 2.55 ± 1.12 in bilateral iAMD and 3.25 ± 1.17 in unilateral iAMD (p = 0.038, for difference between bilateral and unilateral iAMD). Conclusion: A significantly higher FV% was found in patients with iAMD versus controls. A higher trend in FV% was found in unilateral iAMD (with neovascular AMD in the fellow eye) versus bilateral iAMD, when applying the lowest threshold. Further, larger and longitudinal studies are needed to confirm this data. [ABSTRACT FROM AUTHOR]

Hyperspectral images are used in different applications in Earth and space science, and many of these applications exhibit real- or near real-time constraints. A problem when analyzing hyperspectral images is that their spatial resolution is generally not enough to separate different spectrally pure constituents (endmembers); as a result, several of them can be found in the same pixel. Spectral unmixing is an important technique for hyperspectral data exploitation, aimed at finding the spectral signatures of the endmembers and their associated abundance fractions. The development of techniques able to provide unmixing results in real-time is a long desired goal in the hyperspectral imaging community. In this paper, we describe a real-time hyperspectral unmixing chain based on three main steps: 1) estimation of the number of endmembers using the hyperspectral subspace identification with minimum error (HySime); 2) estimation of the spectral signatures of the endmembers using the vertex component analysis (VCA); and 3) unconstrained abundance estimation. We have developed new parallel implementations of the aforementioned algorithms and assembled them in a fully operative real-time unmixing chain using graphics processing units (GPUs), exploiting NVIDIA’s compute unified device architecture (CUDA) and its basic linear algebra subroutines (CuBLAS) library, as well as OpenMP and BLAS for multicore parallelization. As a result, our real-time chain exploits both CPU (multicore) and GPU paradigms in the optimization. Our experiments reveal that this hybrid GPU–CPU parallel implementation fully meets real-time constraints in hyperspectral imaging applications. [ABSTRACT FROM PUBLISHER]

The identification of signal subspace is a crucial operation in hyperspectral imagery, enabling a correct dimensionality reduction that often yields gains in algorithm performance and efficiency. This paper presents new parallel implementations of a widely used hyperspectral subspace identification with minimum error (HySime) algorithm on different types of high-performance computing architectures, including general purpose multicore CPUs, graphics processing units (GPUs), and digital signal processors (DSPs). We first developed an optimized serial version of the HySime algorithm using the C programming language, and then we developed three parallel versions: one for a multi-core Intel CPU using the OpenMP API and the ATLAS algebra library, another one using NVIDIA’s compute unified device architecture (CUDA) and its basic linear algebra subroutines library (CuBLAS), and another one using a Texas’ multicore DSP. Experimental results, based on the processing of simulated and real hyperspectral images of various sizes, show the effectiveness of our GPU and multicore CPU implementations, which satisfy the real-time constraints given by the data acquisition rate. The DSP implementation offers a good tradeoff between low power consumption and computational performance, but it is still penalized by the absence of double precision floating point accuracy and/or suitable mathematical libraries. [ABSTRACT FROM PUBLISHER]

The early detection of skin cancer is of crucial importance to plan an effective therapy to treat the lesion. In routine medical practice, the diagnosis is based on the visual inspection of the lesion and it relies on the dermatologists' expertise. After a first examination, the dermatologist may require a biopsy to confirm if the lesion is malignant or not. This methodology suffers from false positives and negatives issues, leading to unnecessary surgical procedures. Hyperspectral imaging is gaining relevance in this medical field since it is a non-invasive and non-ionizing technique, capable of providing higher accuracy than traditional imaging methods. Therefore, the development of an automatic classification system based on hyperspectral images could improve the medical practice to distinguish pigmented skin lesions from malignant, benign, and atypical lesions. Additionally, the system can assist general practitioners in first aid care to prevent noncritical lesions from reaching dermatologists, thereby alleviating the workload of medical specialists. In this paper is presented a parallel pipeline for skin cancer detection that exploits hyperspectral imaging. The computational times of the serial processing have been reduced by adopting multicore and many-core technologies, such as OpenMP and CUDA paradigms. Different parallel approaches have been combined, leading to the development of fifteen classification pipeline versions. Experimental results using in-vivo hyperspectral images show that a hybrid parallel approach is capable of classifying an image of 50 × 50 pixels with 125 bands in less than 1 s. [ABSTRACT FROM AUTHOR]

Currently, high-level synthesis (HLS) methods and tools are a highly relevant area in the strategy of several leading companies in the field of system-on-chips (SoCs) and field programmable gate arrays (FPGAs). HLS facilitates the work of system developers, who benefit from integrated and automated design workflows, considerably reducing the design time. Although many advances have been made in this research field, there are still some uncertainties about the quality and performance of the designs generated with the use of HLS methodologies. In this paper, we propose an optimization of the HLS methodology by code refactoring using Xilinx SDSoCTM (Software-Defined System-On-Chip). Several options were analyzed for each alternative through code refactoring of a multiclass support vector machine (SVM) classifier written in C, using two different Zynq®-7000 SoC devices from Xilinx, the ZC7020 (ZedBoard) and the ZC7045 (ZC706). The classifier was evaluated using a brain cancer database of hyperspectral images. The proposed methodology not only reduces the required resources using less than 20% of the FPGA, but also reduces the power consumption −23% compared to the full implementation. The speedup obtained of 2.86× (ZC7045) is the highest found in the literature for SVM hardware implementations. [ABSTRACT FROM AUTHOR]

Telemetry allows to monitor the behavior of a system and it is applied to many different and popular fields such as motorsport. In this case, a data-logger collects all the data coming from different automobile sensors providing a very reliable image of the car status and a better vehicle setup. This paper is focused on the development of a new data-logging system for motorsport application, which meets several process constraints, such as high throughput and low power consumption that, to the best of the authors' knowledge, the available devices on the market were not able to satisfy. The new data-logger consists of a fixed and a removable part, which exchanges data through a transceiver exploiting the visible light communication (VLC) technology. In this way, every physical contact between the two parts of the system is avoided. All the communication procedures are managed by a micro-controller mounted on each part of the system. The transceiver consists of the AFBR-1634Z and AFBR-2634Z (Broadcom Limited, San Jose, CA, USA) components, the optical fiber transmitter and the receiver, respectively, produced by Broadcom Inc. By keeping the distance short between them, they can assure a real wireless communication, even without using a high throughput technology like optical fiber. The entire system is powered by an inductive coupling system. In order to test the transceiver, it is connected to a micro-controller reaching a data rate of 8 Mbit/s. But even better performance is achieved by upgrading the micro-controller and changing the transmission technique, connecting the transceiver to the serial peripheral interface (SPI) port of the micro-controller: in this case, a data rate of 21 Mbit/s is reached, perfectly suitable with the application requirements and even further. [ABSTRACT FROM AUTHOR]

One of the most important tasks in hyperspectral imaging is the classification of the pixels in the scene in order to produce thematic maps. This problem can be typically solved through machine learning techniques. In particular, deep learning algorithms have emerged in recent years as a suitable methodology to classify hyperspectral data. Moreover, the high dimensionality of hyperspectral data, together with the increasing availability of unlabeled samples, makes deep learning an appealing approach to process and interpret those data. However, the limited number of labeled samples often complicates the exploitation of supervised techniques. Indeed, in order to guarantee a suitable precision, a large number of labeled samples is normally required. This hurdle can be overcome by resorting to unsupervised classification algorithms. In particular, autoencoders can be used to analyze a hyperspectral image using only unlabeled data. However, the high data dimensionality leads to prohibitive training times. In this regard, it is important to realize that the operations involved in autoencoders training are intrinsically parallel. Therefore, in this paper we present an approach that exploits multi-core and many-core devices in order to achieve efficient autoencoders training in hyperspectral imaging applications. Specifically, in this paper, we present new OpenMP and CUDA frameworks for autoencoder training. The obtained results show that the CUDA framework provides a speed-up of about two orders of magnitudes as compared to an optimized serial processing chain. [ABSTRACT FROM AUTHOR]

The precise delineation of brain cancer is a crucial task during surgery. There are several techniques employed during surgical procedures to guide neurosurgeons in the tumor resection. However, hyperspectral imaging (HSI) is a promising non-invasive and non-ionizing imaging technique that could improve and complement the currently used methods. The HypErspectraL Imaging Cancer Detection (HELICoiD) European project has addressed the development of a methodology for tumor tissue detection and delineation exploiting HSI techniques. In this approach, the K-means algorithm emerged in the delimitation of tumor borders, which is of crucial importance. The main drawback is the computational complexity of this algorithm. This paper describes the development of the K-means clustering algorithm on different parallel architectures, in order to provide real-time processing during surgical procedures. This algorithm will generate an unsupervised segmentation map that, combined with a supervised classification map, will offer guidance to the neurosurgeon during the tumor resection task. We present parallel K-means clustering based on OpenMP, CUDA and OpenCL paradigms. These algorithms have been validated through an in-vivo hyperspectral human brain image database. Experimental results show that the CUDA version can achieve a speed-up of ~ 150 × with respect to a sequential processing. The remarkable result obtained in this paper makes possible the development of a real-time classification system. [ABSTRACT FROM AUTHOR]

The use of hyperspectral imaging (HSI) in the medical field is an emerging approach to assist physicians in diagnostic or surgical guidance tasks. However, HSI data processing involves very high computational requirements due to the huge amount of information captured by the sensors. One of the stages with higher computational load is the K-Nearest Neighbors (KNN) filtering algorithm. The main goal of this study is to optimize and parallelize the KNN algorithm by exploiting the GPU technology to obtain real-time processing during brain cancer surgical procedures. This parallel version of the KNN performs the neighbor filtering of a classification map (obtained from a supervised classifier), evaluating the different classes simultaneously. The undertaken optimizations and the computational capabilities of the GPU device throw a speedup up to 66.18× when compared to a sequential implementation. [ABSTRACT FROM AUTHOR]

Introduction: This study aimed to evaluate the association between macular optical coherence tomography angiography (OCT-A) metrics, characteristics of ultrawide field (UWF) imaging, and cerebrovascular disease in patients with diabetes mellitus (DM) with different stages of diabetic retinopathy (DR). Methods: 516 eyes of 258 DM patients were enrolled in two centers (Milan and Belfast). UWF color fundus photos (CFPs) were obtained with Optos California (Optos, PLC) and graded for both DR severity and predominantly peripheral lesions presence (>50% of CFP lesions) by two independent graders. OCT-A (3 × 3 mm), available in 252 eyes of 136 patients, was used to determine perimeter, area, and circularity index of the foveal avascular zone and vessel density (VD); perfusion density (PD); fractal dimension on superficial, intermediate (ICP), and deep capillary plexuses; flow voids (FVs) in the choriocapillaris. Results: Out of 516 eyes, 108 eyes (20.9%) had no DR, and 6 eyes were not gradable. The remaining 402 eyes were as follows: 10.3% (53) had mild nonproliferative DR (NPDR), 38.2% (197) had moderate NPDR, 11.8% (61) had severe NPDR, and 17.6% (91) had proliferative DR. A worse DR stage was associated with a history of stroke (p = 0.044). Logistic regression analysis after taking into account sex, type of DM, age, DM duration, and OCT-A variables found that PD and VD on ICP were significantly associated with presence of stroke and DR severity. Conclusion: OCT-A metrics show an association with the presence of cerebrovascular complications, providing potentially useful parameters to estimate vascular risk in patients with DM. [ABSTRACT FROM AUTHOR]

Ambient Assisted Living is a concept that focuses on using technology to support and enhance the quality of life and well-being of frail or elderly individuals in both indoor and outdoor environments. It aims at empowering individuals to maintain their independence and autonomy while ensuring their safety and providing assistance when needed. Human Activity Recognition is widely regarded as the most popular methodology within the field of Ambient Assisted Living. Human Activity Recognition involves automatically detecting and classifying the activities performed by individuals using sensor-based systems. Researchers have employed various methodologies, utilizing wearable and/or non-wearable sensors, and employing algorithms ranging from simple threshold-based techniques to more advanced deep learning approaches. In this review, literature from the past decade is critically examined, specifically exploring the technological aspects of Human Activity Recognition in Ambient Assisted Living. An exhaustive analysis of the methodologies adopted, highlighting their strengths and weaknesses is provided. Finally, challenges encountered in the field of Human Activity Recognition for Ambient Assisted Living are thoroughly discussed. These challenges encompass issues related to data collection, model training, real-time performance, generalizability, and user acceptance. Miniaturization, unobtrusiveness, energy harvesting and communication efficiency will be the crucial factors for new wearable solutions., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2023 Guerra, Torti, Marenzi, Schmid, Ramat, Leporati and Danese.)

The Covid-19 European outbreak in February 2020 has challenged the world's health systems, eliciting an urgent need for effective and highly reliable diagnostic instruments to help medical personnel. Deep learning (DL) has been demonstrated to be useful for diagnosis using both computed tomography (CT) scans and chest X-rays (CXR), whereby the former typically yields more accurate results. However, the pivoting function of a CT scan during the pandemic presents several drawbacks, including high cost and cross-contamination problems. Radiation-free lung ultrasound (LUS) imaging, which requires high expertise and is thus being underutilised, has demonstrated a strong correlation with CT scan results and a high reliability in pneumonia detection even in the early stages. In this study, we developed a system based on modern DL methodologies in close collaboration with Fondazione IRCCS Policlinico San Matteo's Emergency Department (ED) of Pavia. Using a reliable dataset comprising ultrasound clips originating from linear and convex probes in 2908 frames from 450 hospitalised patients, we conducted an investigation into detecting Covid-19 patterns and ranking them considering two severity scales. This study differs from other research projects by its novel approach involving four and seven classes. Patients admitted to the ED underwent 12 LUS examinations in different chest parts, each evaluated according to standardised severity scales. We adopted residual convolutional neural networks (CNNs), transfer learning, and data augmentation techniques. Hence, employing methodological hyperparameter tuning, we produced state-of-the-art results meeting F1 score levels, averaged over the number of classes considered, exceeding 98%, and thereby manifesting stable measurements over precision and recall., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Purpose: The aim of this study was to evaluate 1-year quantitative changes in specific inflammatory parameters on optical coherence tomography (OCT) / optical coherence tomography angiography (OCTA) in diabetic macular edema (DME) treated with subthreshold micropulse laser (SMPL)., Methods: Thirty-seven patients / eyes with previously treatment-naïve DME treated with SMPL were prospectively evaluated at 3, 6, and 12 months. Fifteen fellow eyes with only microaneurysms (MAS) not eligible for treatment were controls. Evaluated OCT / OCTA parameters included: central macular thickness (CMT); hyper-reflective retinal spots (HRS); disorganization of inner retinal layers (DRILs); MA in the superficial / deep capillary plexuses (SCP/DCP); cysts in the area at the SCP / DCP; and macular perfusion parameters (MATLAB, version 2017b)., Results: In the treated group, mean best corrected visual acuity (BCVA) progressively increased from 69.4 ± 12.0 to 76.0 ± 9.1 Early Treatment Diabetic Retinopathy Study (ETDRS) letters ( P < 0.001) at 12 months; HRS decreased from baseline (80.75 ± 20.41) at 3 (73.81 ± 17.1, P = 0.002), 6 (69.16 ± 16.48, P < 0.0001), and 12 months (66.29 ± 18.53, P < 0.0001). MA decreased at 3 months in the DCP ( P = 0.015), at 6 and 12 months in both plexuses ( P ≤ 0.0007). BCVA, HRS, and MA remained stable in the controls during all follow-ups. DRIL was present in 18 of 37 patients at baseline and progressively decreased from 557.0 ± 238.7 to 387.1 ± 282.1 μm ( P = 0.01). The area of cyst decreased both in the SCP ( P = 0.03) and the DCP ( P = 0.02). CMT and perfusion parameters did not change., Conclusions: SMPL reduced the number of HRS (sign of activated microglia cells in the retina), MA, DRIL extension, and the area of cysts. Further studies are needed to confirm these preliminary data on the anti-inflammatory effect of SMPL, and to explore the mechanism of action., Translational Relevance: The follow-up of OCT/OCTA noninvasive biomarkers offers a unique insight in the mechanism of laser action, suggesting an anti-inflammatory effect of SMPL., Competing Interests: Disclosure: S. Vujosevic, None; C. Toma, None; E. Villani, None; M. Brambilla, None; E. Torti, None; F. Leporati, None; A. Muraca, None; P. Nucci, None; S. De Cilla, None, (Copyright 2020 The Authors.)

Breast microcalcifications are a common mammographic finding. Microcalcifications are considered suspicious signs of breast cancer and a breast biopsy is required, however, cancer is diagnosed in only a few patients. Reducing unnecessary biopsies and rapid characterization of breast microcalcifications are unmet clinical needs. In this study, 473 microcalcifications detected on breast biopsy specimens from 56 patients were characterized entirely by Raman mapping and confirmed by X-ray scattering. Microcalcifications from malignant samples were generally more homogeneous, more crystalline, and characterized by a less substituted crystal lattice compared with benign samples. There were significant differences in Raman features corresponding to the phosphate and carbonate bands between the benign and malignant groups. In addition to the heterogeneous composition, the presence of whitlockite specifically emerged as marker of benignity in benign microcalcifications. The whole Raman signature of each microcalcification was then used to build a classification model that distinguishes microcalcifications according to their overall biochemical composition. After validation, microcalcifications found in benign and malignant samples were correctly recognized with 93.5% sensitivity and 80.6% specificity. Finally, microcalcifications identified in malignant biopsies, but located outside the lesion, reported malignant features in 65% of in situ and 98% of invasive cancer cases, respectively, suggesting that the local microenvironment influences microcalcification features. This study confirms that the composition and structural features of microcalcifications correlate with breast pathology and indicates new diagnostic potentialities based on microcalcifications assessment. SIGNIFICANCE: Raman spectroscopy could be a quick and accurate diagnostic tool to precisely characterize and distinguish benign from malignant breast microcalcifications detected on mammography., (©2020 American Association for Cancer Research.)