Biophotonics methods for functional monitoring of complications of diabetes mellitus

Machine Learning Analysis of Human Skin by Optoacoustic Mesoscopy for Automated Extraction of Psoriasis and Aging Biomarkers

Ultra-wideband raster-scan optoacoustic mesoscopy (RSOM) is a novel modality that has demonstrated unprecedented ability to visualize epidermal and dermal structures in-vivo. However, an automatic and quantitative analysis of three-dimensional RSOM datasets remains unexplored. In this work we present our framework: Deep Learning RSOM Analysis Pipeline (DeepRAP), to analyze and quantify morphological skin features recorded by RSOM and extract imaging biomarkers for disease characterization. DeepRAP uses a multi-network segmentation strategy based on convolutional neural networks with transfer learning. This strategy enabled the automatic recognition of skin layers and subsequent segmentation of dermal microvasculature with an accuracy equivalent to human assessment. DeepRAP was validated against manual segmentation on 25 psoriasis patients under treatment and our biomarker extraction was shown to characterize disease severity and progression well with a strong correlation to physician evaluation and histology. In a unique validation experiment, we applied DeepRAP in a time series sequence of occlusion-induced hyperemia from 10 healthy volunteers. We observe how the biomarkers decrease and recover during the occlusion and release process, demonstrating accurate performance and reproducibility of DeepRAP. Furthermore, we analyzed a cohort of 75 volunteers and defined a relationship between aging and microvascular features in-vivo. More precisely, this study revealed that fine microvascular features in the dermal layer have the strongest correlation to age. The ability of our newly developed framework to enable the rapid study of human skin morphology and microvasculature in-vivo promises to replace biopsy studies, increasing the translational potential of RSOM.

Incremental residual polarization caused by aging in human skin

Significance

The study of the effect of aging on the optical properties of biological tissues, in particular polarization, is important in the development of new diagnostic approaches.

Aim

This work aims to provide a comprehensive analysis of the factors and mechanisms that contribute to the alteration of skin polarization properties caused by aging, using polarization-sensitive hyperspectral imaging measurements and Monte Carlo simulation.

Approach

Our investigation involved both experimental studies of in vivo human skin of volunteers of different ages and computational modeling that accounted for changes in the absorption and scattering properties of the skin model. Specifically, we analyzed alterations in the degree of linear polarization (DOLP) to better understand the impact of aging on skin polarization properties.

Results

A statistically significant increase in the DOLP was found for the elderly group. At the same time, there was no correlation between changes in polarization and the calculated blood volume fraction parameter for different ages. According to the simulation results, it was also found that a change in the scattering properties of biological tissues has a more significant effect on the change in polarizing light compared to the change in absorption.

Conclusions

The results of the work prove that the sensitivity of polarization imaging to age- or pathological-related skin changes may be primarily due to changes in scattering, which in turn is associated with changes in the collagen fibers of the dermis. The proposed technique shows promise for in vivo non-invasive real-time assessment of age-associated skin changes and can also be extended to monitor changes associated with the development of age-related pathologies.

Automatic detection of ischemic necrotic sites in small intestinal tissue using hyperspectral imaging and transfer learning

Acquiring large amounts of hyperspectral data of small intestinal tissue with real labels in the clinic is difficult, and the data shows inter-patient variability. Building an automatic identification model using a small dataset presents a crucial challenge in obtaining a strong generalization of the model. This study aimed to explore the performance of hyperspectral imaging and transfer learning techniques in the automatic identification of normal and ischemic necrotic sites in small intestinal tissue. Hyperspectral data of small intestinal tissues were collected from eight white rabbit samples. The transfer component analysis (TCA) method was performed to transfer learning on hyperspectral data between different samples and the variability of data distribution between samples was reduced. The results showed that the TCA transfer learning method improved the accuracy of the classification model with less training data. This study provided a reliable method for single-sample modelling to detect necrotic sites in small intestinal tissue .

Incoherent optical fluctuation flowmetry for detecting limbs with hemodynamically significant stenoses in patients with type 2 diabetes

INTRODUCTION

The development of new highly accurate, inexpensive and accessible methods for the detection of lower-extremity peripheral artery disease (LE-PAD) in diabetic patients is required. The aim of this study was to evaluate the accuracy of a new incoherent optical fluctuation flowmetry (IOFF) method in detecting legs with hemodynamically significant stenoses compared to ankle brachial index (ABI) and transcutaneous oximetry (TcPO2) in patients with diabetes mellitus (DM).

MATERIALS AND METHODS

Patients were recruited into 2 groups. Group 1 included patients with DM without LE-PAD and/or diabetic foot syndrome; Group 2 included patients with DM and LE-PAD. All patients underwent the following measurements: ultrasound (reference method), ABI, TcPO2, and the new IOFF method.

RESULTS

The new IOFF method showed a sensitivity of 79.5% and a specificity of 89.8% in detecting limbs with hemodynamically significant stenosis (AUC 0.890, CI 0.822-0.957). TcpO2 allows the diagnosis of LE-PAD with 69.2% sensitivity and 86.2% specificity (AUC 0.817, CI 0.723-0.911). Using a standard ABI cut-off of less than 0.9, the sensitivity and specificity for this parameter were 34.5% and 89.7%, respectively. Increasing the diagnostic cut-off of the ABI on the study group to 0.99 improved sensitivity to 84.6% and specificity to 78% (AUC,0.824 CI 0.732-0.915).

CONCLUSIONS

The new IOFF technique has demonstrated high sensitivity and specificity in the detection of LE-PAD in patients with DM. The high accuracy, rapid measurement, and potential availability suggest that the new IOFF method has a high potential for clinical application in the detection of PAD.

Non-invasive detection of early microvascular changes in juveniles with type 1 diabetes

AIMS/HYPOTHESIS

The study aimed to assess the usefulness of capillaroscopy and photoplethysmography in the search for early vascular anomalies in children with type 1 diabetes.

METHODS

One hundred sixty children and adolescents aged 6-18, 125 patients with type 1 diabetes, and 35 healthy volunteers were enrolled in the study. We performed a detailed clinical evaluation, anthropometric measurements, nailfold capillaroscopy, and photoplethysmography.

RESULTS

Patients with diabetes had more often abnormal morphology in capillaroscopy (68.60%, p = 0.019), enlarged capillaries (32.6%, p = 0.006), and more often more over five meandering capillaries (20.90%, p = 0.026) compared to healthy controls. Meandering capillaries correlated with higher parameters of nutritional status. In a photoplethysmography, patients with diagnosed neuropathy had a higher percentage of flow disturbance curves (p < 0.001) with a reduced frequency of normal curves (p = 0.050).

CONCLUSIONS

Capillaroscopic and photoplethysmographic examinations are non-invasive, painless, fast, and inexpensive. They are devoid of side effects, and there are no limitations in the frequency of their use and repetition. The usefulness of capillaroscopy and photoplethysmography in the study of microcirculation in diabetic patients indicates the vast application possibilities of these methods in clinical practice.

Lesion Detection and Analysis Using Optical Imaging

The biomedical application of optical spectroscopy and imaging is currently an active, developing area of research, supported by recent technical progress in the development of light sources and detectors [...].

Assessment of Blood Microcirculation Changes after COVID-19 Using Wearable Laser Doppler Flowmetry

The present work is focused on the study of changes in microcirculation parameters in patients who have undergone COVID-19 by means of wearable laser Doppler flowmetry (LDF) devices. The microcirculatory system is known to play a key role in the pathogenesis of COVID-19, and its disorders manifest themselves long after the patient has recovered. In the present work, microcirculatory changes were studied in dynamics on one patient for 10 days before his disease and 26 days after his recovery, and data from the group of patients undergoing rehabilitation after COVID-19 were compared with the data from a control group. A system consisting of several wearable laser Doppler flowmetry analysers was used for the studies. The patients were found to have reduced cutaneous perfusion and changes in the amplitude-frequency pattern of the LDF signal. The obtained data confirm that microcirculatory bed dysfunction is present in patients for a long period after the recovery from COVID-19.

A Review of Medicinal Plants with Renoprotective Activity in Diabetic Nephropathy Animal Models

Diabetic nephropathy (DN), also recognized as diabetic kidney disease, is a kidney malfunction caused by diabetes mellitus. A possible contributing factor to the onset of DN is hyperglycemia. Poorly regulated hyperglycemia can damage blood vessel clusters in the kidneys, leading to kidney damage. Its treatment is difficult and expensive because its causes are extremely complex and poorly understood. Extracts from medicinal plants can be an alternative treatment for DN. The bioactive content in medicinal plants inhibits the progression of DN. This work explores the renoprotective activity and possible mechanisms of various medicinal plant extracts administered to diabetic animal models. Research articles published from 2011 to 2022 were gathered from several databases including PubMed, Scopus, ProQuest, and ScienceDirect to ensure up-to-date findings. Results showed that medicinal plant extracts ameliorated the progression of DN via the reduction in oxidative stress and suppression of inflammation, advanced glycation end-product formation, cell apoptosis, and tissue injury-related protein expression.

Band selection for mapping chromophores of skin tissue

A numerical approach has been proposed to identify bands for optimally estimating the concentration of three types of viable chromophores within biological tissue. The bands are determined according to the condition number of absorption matrix associated with the attenuation coefficients of chromophores. The effectiveness of different sets of selected band combination was verified by using the spectral reflectance images of skin tissue acquired from standard forearm vascular occlusion tests via a spectroradiometer. Experimental results demonstrated that the concentration of chromophores within skin tissue could be estimated correctly and robustly only when the bands were deliberately selected.

Assessment of diabetic small-fiber neuropathy by using short-wave infrared hyperspectral imaging

Among patients with type 2 diabetes mellitus (T2DM), the association between hyperspectral imaging (HSI) examination and diabetic neuropathy (DN) is ascertained using HSI of the feet using four types of spectral difference measurements. DN was evaluated by traditional Michigan Neuropathy Screening Instrument (MNSI), evaluation of painful neuropathy (ID-Pain, DN4) and sudomotor function by measuring electrochemical skin conductance (ESC). Of the 120 T2DM patients and 20 healthy adults enrolled, T2DM patients are categorized into normal sudomotor (ESC >60 μS) and sudomotor dysfunction (ESC ≤ 60 μS) groups. Spectral difference analyses reveal significant intergroup differences, whereas traditional examinations cannot distinguish between the two groups. HSI waveform reflectance gradually increases with disease severity, at 1400 to 1600 nm. The area under the curve (AUC) of receiver operating characteristic (ROC) analysis for abnormal ESC is ≥0.8 for all four HSI methods. HSI could be an objective, sensitive, rapid, noninvasive and remote approach to identify early small-fiber DN.

Impairments of cerebral blood flow microcirculation in rats brought on by cardiac cessation and respiratory arrest

The impairments of cerebral blood flow microcirculation brought on by cardiac and respiratory arrest were assessed with multi-modal diagnostic facilities, utilising laser speckle contrast imaging, fluorescence spectroscopy and diffuse reflectance spectroscopy. The results of laser speckle contrast imaging show a notable reduction of cerebral blood flow in small and medium size vessels during a few minutes of respiratory arrest, while the same effect was observed in large sinuses and their branches during the circulatory cessation. Concurrently, the redox ratio assessed with fluorescence spectroscopy indicates progressing hypoxia, NADH accumulation and increase of FAD consumption. The results of diffuse reflectance spectra measurements display a more rapid grow of the perfusion of deoxygenated blood in case of circulatory impairment. In addition, consequent histopathological analysis performed by using new tissue staining procedure developed in-house. It shows notably higher reduction of size of the neurons due to their wrinkling within brain tissues influenced by circulation impair. Whereas, the brain tissues altered with the respiratory arrest demonstrate focal perivascular oedema and mild hypoxic changes of neuronal morphology. Thus, the study suggests that consequences of a cessation of cerebral blood flow become more dramatic and dangerous compare to respiratory arrest.

Biophotonics for diagnostic detection of extracellular vesicles

Extracellular Vesicles (EVs) are versatile carriers for biomarkers involved in the pathogenesis of multiple human disorders. Despite the increasing scientific and commercial interest in EV application in diagnostics, traditional biomolecular techniques usually require consistent sample amount, rely on operator-dependent and time- consuming procedures and cannot cope with the nano-size range of EVs, limiting both sensitivity and reproducibility of results. The application of biophotonics, i.e. light-based methods, for the diagnostic detection of EVs has brought to the development of innovative platforms with excellent sensitivity. In this review, we propose an overview of the most promising and emerging technologies used in the field of EV-related biomarker discovery. When tested on clinical samples, the reported biophotonic approaches in most cases have managed to discriminate between nanovesicles and contaminants, achieved much higher resolution compared to traditional procedures, and reached moderate to excellent diagnostic accuracy, thus demonstrating great potentialities for their clinical translation.

Skin Complications of Diabetes Mellitus Revealed by Polarized Hyperspectral Imaging and Machine Learning

Aging and diabetes lead to protein glycation and cause dysfunction of collagen-containing tissues. The accompanying structural and functional changes of collagen significantly contribute to the development of various pathological malformations affecting the skin, blood vessels, and nerves, causing a number of complications, increasing disability risks and threat to life. In fact, no methods of non-invasive assessment of glycation and associated metabolic processes in biotissues or prediction of possible skin complications, e.g., ulcers, currently exist for endocrinologists and clinical diagnosis. In this publication, utilizing emerging photonics-based technology, innovative solutions in machine learning, and definitive physiological characteristics, we introduce a diagnostic approach capable of evaluating the skin complications of diabetes mellitus at the very earlier stage. The results of the feasibility studies, as well as the actual tests on patients with diabetes and healthy volunteers, clearly show the ability of the approach to differentiate diabetic and control groups. Furthermore, the developed in-house polarization-based hyperspectral imaging technique accomplished with the implementation of the artificial neural network provides new horizons in the study and diagnosis of age-related diseases.

An Adaptive Federated Machine Learning-Based Intelligent System for Skin Disease Detection: A Step toward an Intelligent Dermoscopy Device

The prevalence of skin diseases has increased dramatically in recent decades, and they are now considered major chronic diseases globally. People suffer from a broad spectrum of skin diseases, whereas skin tumors are potentially aggressive and life-threatening. However, the severity of skin tumors can be managed (by treatment) if diagnosed early. Health practitioners usually apply manual or computer vision-based tools for skin tumor diagnosis, which may cause misinterpretation of the disease and lead to a longer analysis time. However, cutting-edge technologies such as deep learning using the federated machine learning approach have enabled health practitioners (dermatologists) in diagnosing the type and severity level of skin diseases. Therefore, this study proposes an adaptive federated machine learning-based skin disease model (using an adaptive ensemble convolutional neural network as the core classifier) in a step toward an intelligent dermoscopy device for dermatologists. The proposed federated machine learning-based architecture consists of intelligent local edges (dermoscopy) and a global point (server). The proposed architecture can diagnose the type of disease and continuously improve its accuracy. Experiments were carried out in a simulated environment using the International Skin Imaging Collaboration (ISIC) 2019 dataset (dermoscopy images) to test and validate the model’s classification accuracy and adaptability. In the future, this study may lead to the development of a federated machine learning-based (hardware) dermoscopy device to assist dermatologists in skin tumor diagnosis.

A personalised screening strategy for diabetic retinopathy: a cost-effectiveness perspective

AIMS/HYPOTHESIS

In this study we examined the cost-effectiveness of three different screening strategies for diabetic retinopathy: using a personalised adaptive model, annual screening (fixed intervals), and the current Dutch guideline (stratified based on previous retinopathy grade).

METHODS

For each individual, optimal diabetic retinopathy screening intervals were determined, using a validated risk prediction model. Observational data (1998-2017) from the Hoorn Diabetes Care System cohort of people with type 2 diabetes were used (n = 5514). The missing values of retinopathy grades were imputed using two scenarios of slow and fast sight-threatening retinopathy (STR) progression. By comparing the model-based screening intervals to observed time to develop STR, the number of delayed STR diagnoses was determined. Costs were calculated using the healthcare perspective and the societal perspective. Finally, outcomes and costs were compared for the different screening strategies.

RESULTS

For the fast STR progression scenario, personalised screening resulted in 11.6% more delayed STR diagnoses and €11.4 less costs per patient compared to annual screening from a healthcare perspective. The personalised screening model performed better in terms of timely diagnosis of STR (8.8% less delayed STR diagnosis) but it was slightly more expensive (€1.8 per patient from a healthcare perspective) than the Dutch guideline strategy.

CONCLUSIONS/INTERPRETATION

The personalised diabetic retinopathy screening model is more cost-effective than the Dutch guideline screening strategy. Although the personalised screening strategy was less effective, in terms of timely diagnosis of STR patients, than annual screening, the number of delayed STR diagnoses is low and the cost saving is considerable. With around one million people with type 2 diabetes in the Netherlands, implementing this personalised model could save €11.4 million per year compared with annual screening, at the cost of 658 delayed STR diagnoses with a maximum delayed time to diagnosis of 48 months.