Hyperspectral imaging in wound care: A systematic review

Using Near-Infrared Spectroscopy and Education to Support Older Adults with Diabetic Foot Ulcers to Age-in-Place: A Case Series

ABSTRACT

The objective of this article is to demonstrate the added value of foot care provided by an RN with foot care training to older adults in their home by focusing on four older adults with diabetic foot ulcers. The RN used a mobile multispectral near-infrared spectroscopy device to enhance the assessment of the diabetic foot ulcers. The Mobile Seniors Wellness Network methodically engaged with English-speaking adults 55 years and older living within a 90-minute radius of the city's community health center. Older adults were referred to the research project through various sources. The participation group included 366 participants with varying holistic healthcare concerns and financial stressors that impacted their ability to age well in place. Over the course of visits by the RN and registered social worker, positive outcomes were facilitated through the collaboration of the participant and the multidisciplinary team, thus enhancing the individual's confidence to remain at home longer. In a time of ongoing provincial health crisis, it may be cost-effective to provide in-home support to those who want to age well in their communities by deploying a Mobile Seniors Wellness Network system throughout the province and enhancing the RN's assessment of feet with a portable and innovative technology tool.

Validation of multispectral imaging-based tissue oxygen saturation detecting system for wound healing recognition on open wounds

Significance

The multispectral imaging-based tissue oxygen saturation detecting (TOSD) system offers deeper penetration ( ∼ 2 to 3 mm) and comprehensive tissue oxygen saturation (StO 2 ) assessment and recognizes the wound healing phase at a low cost and computational requirement. The potential for miniaturization and integration of TOSD into telemedicine platforms could revolutionize wound care in the challenging pandemic era.

Aim

We aim to validate TOSD's application in detectingStO 2 by comparing it with wound closure rates and laser speckle contrast imaging (LSCI), demonstrating TOSD's ability to recognize the wound healing process.

Approach

Utilizing a murine model, we compared TOSD with digital photography and LSCI for comprehensive wound observation in five mice with 6-mm back wounds. Sequential biochemical analysis of wound discharge was investigated for the translational relevance of TOSD.

Results

TOSD demonstrated constant signals on unwounded skin with differential changes on open wounds. Compared with LSCI, TOSD provides indicative recognition of the proliferative phase during wound healing, with a higher correlation coefficient to wound closure rate (TOSD: 0.58; LSCI: 0.44).StO 2 detected by TOSD was further correlated with proliferative phase angiogenesis markers.

Conclusions

Our findings suggest TOSD's enhanced utility in wound management protocols, evaluating clinical staging and therapeutic outcomes. By offering a noncontact, convenient monitoring tool, TOSD can be applied to telemedicine, aiming to advance wound care and regeneration, potentially improving patient outcomes and reducing healthcare costs associated with chronic wounds.

Hemoglobin Concentration as an Indicator of Skin Radiation Damage During Radiation Therapy Treatments

PURPOSE

Acute radiation dermatitis (ARD) is the most common side effect reported by patients undergoing radiation therapy (RT). Currently, the assessment of the severity of the reaction is based on the visual assessment of the skin, which is a subjective method, depending on many factors. The main aim of this study was to investigate the usefulness of hyperspectral imaging (HSI) in the assessment of ARD and find physiological factors that could be correlated with ARD.

METHODS AND MATERIALS

In this clinical pilot trial, weekly acquisitions of hyperspectral camera images of irradiated skin were performed for 5 weeks of RT and at the posttreatment follow-up visit which took place 30 to 40 days after the last fraction of RT. At the same time, the severity of radiodermatitis was assessed based on the criteria of the National Cancer Institute Common Terminology Criteria for Adverse Events (CTCAE). The content and concentration of chromophores in irradiated skin were quantitatively determined using a hyperspectral camera.

RESULTS

The use of HSI supported by image analysis and processing methods allowed for the determination of the content and distribution of hemoglobin and melanin in the irradiated skin. It was found that the hemoglobin concentration is correlated with the subjective assessment made according to the CTCAE protocol.

CONCLUSIONS

HSI is a sensitive and specific method of analyzing the concentration of chromophores in the skin, including hemoglobin. A clear correlation was found between hemoglobin concentration and CTCAE v.5 scale because of which HSI can be considered as an objective method of skin assessment during RT.

Artificial neural networks trained on simulated multispectral data for real-time imaging of skin microcirculatory blood oxygen saturation

Significance

Imaging blood oxygen saturation (SO 2 ) in the skin can be of clinical value when studying ischemic tissue. Emerging multispectral snapshot cameras enable real-time imaging but are limited by slow analysis when using inverse Monte Carlo (MC), the gold standard for analyzing multispectral data. Using artificial neural networks (ANNs) facilitates a significantly faster analysis but requires a large amount of high-quality training data from a wide range of tissue types for a precise estimation ofSO 2 .

Aim

We aim to develop a framework for training ANNs that estimatesSO 2 in real time from multispectral data with a precision comparable to inverse MC.

Approach

ANNs are trained using synthetic data from a model that includes MC simulations of light propagation in tissue and hardware characteristics. The model includes physiologically relevant variations in optical properties, unique sensor characteristics, variations in illumination spectrum, and detector noise. This approach enables a rapid way of generating high-quality training data that covers different tissue types and skin pigmentation.

Results

The ANN implementation analyzes an image in 0.11 s, which is at least 10,000 times faster than inverse MC. The hardware modeling is significantly improved by an in-house calibration of the sensor spectral response. An in-vivo example shows that inverse MC and ANN give almost identicalSO 2 values with a mean absolute deviation of 1.3%-units.

Conclusions

ANN can replace inverse MC and enable real-time imaging of microcirculatorySO 2 in the skin if detailed and precise modeling of both tissue and hardware is used when generating training data.

Multispectral Imaging-Based System for Detecting Tissue Oxygen Saturation With Wound Segmentation for Monitoring Wound Healing

OBJECTIVE

Blood circulation is an important indicator of wound healing. In this study, a tissue oxygen saturation detecting (TOSD) system that is based on multispectral imaging (MSI) is proposed to quantify the degree of tissue oxygen saturation (StO2) in cutaneous tissues.

METHODS

A wound segmentation algorithm is used to segment automatically wound and skin areas, eliminating the need for manual labeling and applying adaptive tissue optics. Animal experiments were conducted on six mice in which they were observed seven times, once every two days. The TOSD system illuminated cutaneous tissues with two wavelengths of light - red ([Formula: see text] nm) and near-infrared ([Formula: see text] nm), and StO2 levels were calculated using images that were captured using a monochrome camera. The wound segmentation algorithm using ResNet34-based U-Net was integrated with computer vision techniques to improve its performance.

RESULTS

Animal experiments revealed that the wound segmentation algorithm achieved a Dice score of 93.49%. The StO2 levels that were determined using the TOSD system varied significantly among the phases of wound healing. Changes in StO2 levels were detected before laser speckle contrast imaging (LSCI) detected changes in blood flux. Moreover, statistical features that were extracted from the TOSD system and LSCI were utilized in principal component analysis (PCA) to visualize different wound healing phases. The average silhouette coefficients of the TOSD system with segmentation (ResNet34-based U-Net) and LSCI were 0.2890 and 0.0194, respectively.

CONCLUSION

By detecting the StO2 levels of cutaneous tissues using the TOSD system with segmentation, the phases of wound healing were accurately distinguished. This method can support medical personnel in conducting precise wound assessments. Clinical and Translational Impact Statement-This study supports efforts in monitoring StO2 levels, wound segmentation, and wound healing phase classification to improve the efficiency and accuracy of preclinical research in the field.

A novel visible and near-infrared hyperspectral imaging platform for automated breast-cancer detection

BACKGROUND

Breast cancer is a leading cause of cancer-related deaths among women worldwide. Early and accurate detection is crucial for improving patient outcomes. Our study utilizes Visible and Near-Infrared Hyperspectral Imaging (VIS-NIR HSI), a promising non-invasive technique, to detect cancerous regions in ex-vivo breast specimens based on their hyperspectral response.

METHODS

In this paper, we present a novel HSI platform integrated with fuzzy c-means clustering for automated breast cancer detection. We acquire hyperspectral data from breast tissue samples, and preprocess it to reduce noise and enhance hyperspectral features. Fuzzy c-means clustering is then applied to segment cancerous regions based on their spectral characteristics.

RESULTS

Our approach demonstrates promising results. We evaluated the quality of the clustering using metrics like Silhouette Index (SI), Davies-Bouldin Index (DBI), and Calinski-Harabasz Index (CHI). The clustering metrics results revealed an optimal number of 6 clusters for breast tissue classification, and the SI values ranged from 0.68 to 0.72, indicating well-separated clusters. Moreover, the CHI values showed that the clusters were well-defined, and the DBI values demonstrated low cluster dispersion. Additionally, the sensitivity, specificity, and accuracy of our system were evaluated on a dataset of breast tissue samples. We achieved an average sensitivity of 96.83%, specificity of 93.39%, and accuracy of 95.12%. These results indicate the effectiveness of our HSI-based approach in distinguishing cancerous and non-cancerous regions.

CONCLUSIONS

The paper introduces a robust hyperspectral imaging platform coupled with fuzzy c-means clustering for automated breast cancer detection. The clustering metrics results support the reliability of our approach in effectively segmenting breast tissue samples. In addition, the system shows high sensitivity and specificity, making it a valuable tool for early-stage breast cancer diagnosis. This innovative approach holds great potential for improving breast cancer screening and, thereby, enhancing our understanding of the disease and its detection patterns.

Multispectral near-infrared spectroscopy study evaluating the effect of razor design on shaving-induced erythema

BACKGROUND

While shaving-induced erythema is a common inflammatory skin issue, there is a lack of quantitative information on how well a shaving product performs in this regard. In this study, multispectral near-infrared spectroscopy (NIRS) imaging was used to quantitatively and qualitatively measure the extent of shaving-induced erythema. The research compares a safety razor and a cartridge razor to evaluate their impact on skin irritation.

MATERIALS AND METHODS

Fifty-nine healthy male volunteers without pre-existing skin conditions were enrolled. Basic demographics were recorded, and participants' faces or necks were imaged before shaving. Shaving was conducted on the right side of the face/neck with the safety razor and on the left side of the face/neck using the 3-blade cartridge razor. Images were captured immediately after shaving, at 5 and 10 min post-shaving.

RESULTS

Tissue oxygen saturation (StO2) measurements demonstrated that the safety razor induced significantly less erythema than the cartridge razor. Immediately after shaving, 40.3% of skin shaved with the safety razor had erythema compared to 57.6% for the cartridge razor. At 5 min post-shaving, 36.5% of skin shaved with the safety razor had erythema, compared to 53.8% of cartridge razor.

CONCLUSIONS

Multispectral NIRS revealed significant differences in shaving-induced erythema between safety and cartridge razors. Safety razors demonstrated a lower incidence of erythema, suggesting a potential advantage for individuals prone to skin irritation. This study contributes valuable insights into skin irritation and highlights the potential of multispectral NIRS in dermatology research.

Multispectral indices for real-time and non-invasive tissue ischemia monitoring using snapshot cameras

An adequate supply of oxygen-rich blood is vital to maintain cell homeostasis, cellular metabolism, and overall tissue health. While classical methods of measuring tissue ischemia are often invasive, localized and require skin contact or contrast agents, spectral imaging shows promise as a non-invasive, wide field, and contrast-free approach. We evaluate three novel reflectance-based spectral indices from the 460 - 840 nm spectral range. With the aim of enabling real time visualization of tissue ischemia, information is extracted from only 2-3 spectral bands. Video-rate spectral data was acquired from arm occlusion experiments in 27 healthy volunteers. The performance of the indices was evaluated against binary Support Vector Machine (SVM) classification of healthy versus ischemic skin tissue, two other indices from literature, and tissue oxygenation estimated using spectral unmixing. Robustness was tested by evaluating these under various lighting conditions and on both the dorsal and palmar sides of the hand. A novel index with real-time capabilities using reflectance information only from 547 nm and 556 nm achieves an average classification accuracy of 88.48, compared to 92.65 using an SVM trained on all available wavelengths. Furthermore, the index has a higher accuracy compared to reference methods and its time dynamics compare well against the expected clinical responses. This holds promise for robust real-time detection of tissue ischemia, possibly contributing to improved patient care and clinical outcomes.

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 .

Gas Plasma Exposure Alters Microcirculation and Inflammation during Wound Healing in a Diabetic Mouse Model

Diabetes can disrupt physiological wound healing, caused by decreased levels or impaired activity of angiogenic factors. This can contribute to chronic inflammation, poor formation of new blood vessels, and delayed re-epithelialization. The present study describes the preclinical application of medical gas plasma to treat a dermal, full-thickness ear wound in streptozotocin (STZ)-induced diabetic mice. Gas plasma-mediated effects occurred in both sexes but with gender-specific differences. Hyperspectral imaging demonstrated gas plasma therapy changing microcirculatory parameters, particularly oxygen saturation levels during wound healing, presumably due to the gas plasma's tissue delivery of reactive species and other bioactive components. In addition, gas plasma treatment significantly affected cell adhesion by regulating focal adhesion kinase and vinculin, which is important in maintaining skin barrier function by regulating syndecan expression and increasing re-epithelialization. An anticipated stimulation of blood vessel formation was detected via transcriptional and translational increase of angiogenic factors in gas plasma-exposed wound tissue. Moreover, gas plasma treatment significantly affected inflammation by modulating systemic growth factors and cytokine levels. The presented findings may help explain the mode of action of successful clinical plasma therapy of wounds of diabetic patients.

Dual-Scan Photoacoustic Tomography for the Imaging of Vascular Structure on Foot

Chronic leg ulcers are affecting approximately 6.5 million Americans, and they are associated with significant mortality, reduced quality of life, and high treatment costs. Since many chronic ulcers have underlying vascular insufficiency, accurate assessment of tissue perfusion is critical to treatment planning and monitoring. This study introduces a dual-scan photoacoustic (PA) tomography (PAT) system that can simultaneously image the dorsal and plantar sides of the foot to reduce imaging time. To account for the unique shape of the foot, the system employs height-adjustable and articulating baseball stages that can scan along the foot's contour. In vivo results from healthy volunteers demonstrate the system's ability to acquire clear images of foot vasculature, and results from patients indicate that the system can image patients with various ulcer conditions. We also investigated various PA features and examined their correlation with the foot condition. Our preliminary results indicate that vessel sharpness, occupancy, intensity, and density could all be used to assess tissue perfusion. This research demonstrated the potential of PAT for routine clinical tissue perfusion assessment.

Synergistic coupling between 3D bioprinting and vascularization strategies

Three-dimensional (3D) bioprinting offers promising solutions to the complex challenge of vascularization in biofabrication, thereby enhancing the prospects for clinical translation of engineered tissues and organs. While existing reviews have touched upon 3D bioprinting in vascularized tissue contexts, the current review offers a more holistic perspective, encompassing recent technical advancements and spanning the entire multistage bioprinting process, with a particular emphasis on vascularization. The synergy between 3D bioprinting and vascularization strategies is crucial, as 3D bioprinting can enable the creation of personalized, tissue-specific vascular network while the vascularization enhances tissue viability and function. The review starts by providing a comprehensive overview of the entire bioprinting process, spanning from pre-bioprinting stages to post-printing processing, including perfusion and maturation. Next, recent advancements in vascularization strategies that can be seamlessly integrated with bioprinting are discussed. Further, tissue-specific examples illustrating how these vascularization approaches are customized for diverse anatomical tissues towards enhancing clinical relevance are discussed. Finally, the underexplored intraoperative bioprinting (IOB) was highlighted, which enables the direct reconstruction of tissues within defect sites, stressing on the possible synergy shaped by combining IOB with vascularization strategies for improved regeneration.

Hyperspectral imaging benchmark based on machine learning for intraoperative brain tumour detection

Brain surgery is one of the most common and effective treatments for brain tumour. However, neurosurgeons face the challenge of determining the boundaries of the tumour to achieve maximum resection, while avoiding damage to normal tissue that may cause neurological sequelae to patients. Hyperspectral (HS) imaging (HSI) has shown remarkable results as a diagnostic tool for tumour detection in different medical applications. In this work, we demonstrate, with a robust k-fold cross-validation approach, that HSI combined with the proposed processing framework is a promising intraoperative tool for in-vivo identification and delineation of brain tumours, including both primary (high-grade and low-grade) and secondary tumours. Analysis of the in-vivo brain database, consisting of 61 HS images from 34 different patients, achieve a highest median macro F1-Score result of 70.2 ± 7.9% on the test set using both spectral and spatial information. Here, we provide a benchmark based on machine learning for further developments in the field of in-vivo brain tumour detection and delineation using hyperspectral imaging to be used as a real-time decision support tool during neurosurgical workflows.

Diabetic Foot Ulcer Imaging: An Overview and Future Directions

Diabetic foot ulcers (DFUs) affect one in every three people with diabetes. Imaging plays a vital role in objectively complementing the gold-standard visual yet subjective clinical assessments of DFUs during the wound treatment process. Herein, an overview of the various imaging techniques used to image DFUs is summarized. Conventional imaging modalities (e.g., computed tomography, magnetic resonance imaging, positron emission tomography, single-photon emitted computed tomography, and ultrasound) are used to diagnose infections, impact on the bones, foot deformities, and blood flow in patients with DFUs. Transcutaneous oximetry is a gold standard to assess perfusion in DFU cases with vascular issues. For a wound to heal, an adequate oxygen supply is needed to facilitate reparative processes. Several optical imaging modalities can assess tissue oxygenation changes in and around the wounds apart from perfusion measurements. These include hyperspectral imaging, multispectral imaging, diffuse reflectance spectroscopy, near-infrared (NIR) spectroscopy, laser Doppler flowmetry or imaging, and spatial frequency domain imaging. While perfusion measurements are dynamically monitored at point locations, tissue oxygenation measurements are static two-dimensional spatial maps. Recently, we developed a spatio-temporal NIR-based tissue oxygenation imaging approach to map for the extent of asynchrony in the oxygenation flow patterns in and around DFUs. Researchers also measure other parameters such as thermal maps, bacterial infections (from fluorescence maps), pH, collagen, and trans-epidermal water loss to assess DFUs. A future direction for DFU imaging would ideally be a low-cost, portable, multi-modal imaging platform that can provide a visual and physiological assessment of wounds for comprehensive wound care intervention and management.

Alternative intraoperative optical imaging modalities for fluorescence angiography in gastrointestinal surgery: spectral imaging and imaging photoplethysmography

INTRODUCTION

Intraoperative near-infrared fluorescence angiography with indocyanine green (ICG-FA) is a well-established modality in gastrointestinal surgery. Its main drawback is the application of a fluorescent agent with possible side effects for patients. The goal of this review paper is the presentation of alternative, non-invasive optical imaging methods and their comparison with ICG-FA.

MATERIAL AND METHODS

The principles of ICG-FA, spectral imaging, imaging photoplethysmography (iPPG), and their applications in gastrointestinal surgery are described based on selected published works.

RESULTS

The main applications of the three modalities are the evaluation of tissue perfusion, the identification of risk structures, and tissue segmentation or classification. While the ICG-FA images are mainly evaluated visually, leading to subjective interpretations, quantitative physiological parameters and tissue segmentation are provided in spectral imaging and iPPG. The combination of ICG-FA and spectral imaging is a promising method.

CONCLUSIONS

Non-invasive spectral imaging and iPPG have shown promising results in gastrointestinal surgery. They can overcome the main drawbacks of ICG-FA, i.e. the use of contrast agents, the lack of quantitative analysis, repeatability, and a difficult standardization of the acquisition. Further technical improvements and clinical evaluations are necessary to establish them in daily clinical routine.

Clinical and Translational Imaging and Sensing of Diabetic Microangiopathy: A Narrative Review

Microvascular changes in diabetes affect the function of several critical organs, such as the kidneys, heart, brain, eye, and skin, among others. The possibility of detecting such changes early enough in order to take appropriate actions renders the development of appropriate tools and techniques an imperative need. To this end, several sensing and imaging techniques have been developed or employed in the assessment of microangiopathy in patients with diabetes. Herein, we present such techniques; we provide insights into their principles of operation while discussing the characteristics that make them appropriate for such use. Finally, apart from already established techniques, we present novel ones with great translational potential, such as optoacoustic technologies, which are expected to enter clinical practice in the foreseeable future.

Optimal Hyperspectral Band Selection for Tissue Oxygenation Mapping with Generative Adversarial Network

Tissue oxygenation assessment using hyperspectral imaging is an emerging technique for the diagnosis and pre- and post-treatment monitoring of ischemic patients. However, the high spectral resolution of hyperspectral imaging leads to large data sizes and a long imaging time. In this study, we propose a method that utilizes multi-objective evolutionary algorithms to determine the optimal hyperspectral band combination when developing a deep learning model for predicting tissue oxygenation from hyperspectral images. Our results confirm that the deep learning model effectively predicts tissue oxygenation images for various oxygenation states. Moreover, we demonstrate that a high-performance prediction model can be developed using only a small number of spectral bands, indicating the potential for more efficient non-contact tissue oxygenation mapping with the proposed method.Clinical Relevance- The proposed method allows for the non-contact and efficient acquisition of two-dimensional tissue oxygenation information in various oxygenation states.

Concurrent Optical- and Magnetic-Stimulation-Induced Changes on Wound Healing Parameters, Analyzed by Hyperspectral Imaging: An Exploratory Case Series

The effects of concurrent optical and magnetic stimulation (COMS) therapy on wound-healing-related parameters, such as tissue oxygenation and water index, were analyzed by hyperspectral imaging: an exploratory case series. Background: Oedema and inadequate perfusion have been identified as key factors in delayed wound healing and have been linked to reduced mitochondrial respiration. Targeting mitochondrial dysfunction is a promising approach in the treatment of therapy refractory wounds. This sub-study aimed to investigate the effects of concurrent optical and magnetic stimulation (COMS) on oedema and perfusion through measuring tissue oxygenation and water index, using hyperspectral imaging. Patients and methods: In a multi-center, prospective, comparative clinical trial, eleven patients with chronic leg and foot ulcers were treated with COMS additively to Standard of Care (SOC). Hyperspectral images were collected during patient visits before and after treatment to assess short- and long-term hemodynamic and immunomodulatory effects through changes in tissue oxygenation and water index. Results: The average time for wound onset in the eleven patients analyzed was 183 days, with 64% of them being considered unresponsive to SOC. At week 12, the rate of near-complete and complete wound closure was 64% and 45%, respectively. COMS therapy with SOC resulted in an increased short-term tissue oxygenation over the 8-week treatment phase, with oxygen levels decreasing in-between patient visits. The study further found a decrease in tissue water content after the therapy, with a general accumulation of water levels in-between patient visits. This study's long-term analysis was hindered by the lack of absolute values in hyperspectral imaging and the dynamic nature of patient parameters during visits, resulting in high interpatient and intervisit variability. Conclusions: This study showed that COMS therapy as an adjunct to SOC had a positive short-term effect on inflammation and tissue oxygenation in chronic wounds of various etiologies. These results further supported the body of evidence for safety and effectiveness of COMS therapy as a treatment option, especially for stagnant wounds that tended to stay in the inflammatory phase and required efficient phase transition towards healing.

Hyperspectral imaging-based cutaneous wound classification using neighbourhood extraction 3D convolutional neural network

OBJECTIVES

Hyperspectral imaging is an emerging imaging modality that beginning to gain attention for medical research and has an important potential in clinical applications. Nowadays, spectral imaging modalities such as multispectral and hyperspectral have proven their ability to provide important information that can help to better characterize the wound. Oxygenation changes in the wounded tissue differ from normal tissue. This causes the spectral characteristics to be different. In this study, it is classified cutaneous wounds with neighbourhood extraction 3-dimensional convolutional neural network method.

METHODS

The methodology of hyperspectral imaging performed to obtain the most useful information about the wounded and normal tissue is explained in detail. When the hyperspectral signatures of wounded and normal tissues are compared on the hyperspectral image, it is revealed that there is a relative difference between them. By taking advantage of these differences, cuboids that also consider neighbouring pixels are generated, and a uniquely designed 3-dimensional convolutional neural network model is trained with the cuboids to extract both spatial and spectral information.

RESULTS

The effectiveness of the proposed method was evaluated for different cuboid spatial dimensions and training/testing rates. The best result with 99.69% was achieved when the training/testing rate was 0.9/0.1 and the cuboid spatial dimension was 17. It is observed that the proposed method outperforms the 2-dimensional convolutional neural network method and achieves high accuracy even with much less training data. The obtained results using the neighbourhood extraction 3-dimensional convolutional neural network method show that the proposed method highly classifies the wounded area. In addition, the classification performance and the2computation time of the neighbourhood extraction 3-dimensional convolutional neural network methodology were analyzed and compared with existing 2-dimensional convolutional neural network.

CONCLUSIONS

As a clinical diagnostic tool, hyperspectral imaging, with neighbourhood extraction 3-dimensional convolutional neural network, has yielded remarkable results for the classification of wounded and normal tissues. Skin color does not play any role in the success of the proposed method. Since only the reflectance values of the spectral signatures are different for various skin colors. For different ethnic groups, The spectral signatures of wounded tissue and the spectral signatures of normal tissue show similar spectral characteristics among themselves.

Comparison of Antera 3D® and TcPO2 for Evaluation of Blood Flow in Skin

Background

There is a need for quick skin blood flow tests that can be performed in the wound healing field. Antera 3D® is a compact scanner using multispectral imaging. It can perform quick assessment of skin conditions. The purpose of the present study was to investigate the ability of the Antera 3D® to assess skin blood flow in comparison with transcutaneous partial pressure of oxygen (TcPO2) measurements.

Methods

This study was conducted on 13 patients with a history of lower extremity ulcers. Measurements of hemoglobin average level (hereafter, Hb score) measured by Antera 3D® and TcPO2 measured by a transcutaneous blood gas monitor were obtained at the same sites on the dorsal foot and lower leg. Differences in Hb score and TcPO2 were analyzed by t-test for each measurement site and for the presence of peripheral arterial disease (PAD). The correlation between TcPO2 and Hb score was analyzed by Pearson's correlation coefficient.

Results

Twenty-four limbs were tested. Hb score was higher (P < 0.001) and TcPO2 was lower (P = 0.056) in the dorsal foot compared to the lower leg. In the dorsal foot, Hb score was higher (P = 0.023) and TcPO2 was lower (P = 0.046) in patients with PAD compared to those without PAD. A significant negative correlation (r = -0.68; 95% confidence interval -0.85 to -0.38, P < 0.001) between TcPO2 and Hb score was observed in the dorsal foot.

Conclusion

The negative correlation between TcPO2 and Hb score may reflect compensatory peripheral vasodilation due to occlusion or stenosis of central arteries. This study showed that Hb score measured by Antera 3D® may be related to skin blood flow.

The Application of Hyperspectral Imaging to the Measurement of Pressure Injury Area

Wound size measurement is an important indicator of wound healing. Nurses measure wound size in terms of length × width in wound healing assessment, but it is easy to overestimate the extent of the wound due to irregularities around it. Using hyperspectral imaging (HIS) to measure the area of a pressure injury could provide more accurate data than manual measurement, ensure that the same tool is used for standardized assessment of wounds, and reduce the measurement time. This study was a pilot cross-sectional study, and a total of 30 patients with coccyx sacral pressure injuries were recruited to the rehabilitation ward after approval by the human subjects research committee. We used hyperspectral images to collect pressure injury images and machine learning (k-means) to automatically classify wound areas in combination with the length × width rule (LW rule) and image morphology algorithm for wound judgment and area calculation. The results calculated from the data were compared with the calculations made by the nursing staff using the length × width rule. The use of hyperspectral images, machine learning, the length × width rule (LW rule), and an image morphology algorithm to calculate the wound area yielded more accurate measurements than did nurses, effectively reduced the chance of human error, reduced the measurement time, and produced real-time data. HIS can be used by nursing staff to assess wounds with a standardized approach so as to ensure that proper wound care can be provided.

In vivo evaluation of a hyperspectral imaging system for minimally invasive surgery (HSI-MIS)

BACKGROUND

Hyperspectral Imaging (HSI) is a reliable and safe imaging method for taking intraoperative perfusion measurements. This is the first study translating intraoperative HSI to an in vivo laparoscopic setting using a CE-certified HSI-system for minimally invasive surgery (HSI-MIS). We aim to compare it to an established HSI-system for open surgery (HSI-Open).

METHODS

Intraoperative HSI was done using the HSI-MIS and HSI-Open at the Region of Interest (ROI). 19 patients undergoing gastrointestinal resections were analyzed in this study. The HSI-MIS-acquired images were aligned with those from the HSI-Open, and spectra and parameter images were compared pixel-wise. We calculated the Mean Absolute Error (MAE) for Tissue Oxygen Saturation (StO₂), Near-Infrared Perfusion Index (NIR-PI), Tissue Water Index (TWI), and Organ Hemoglobin Index (OHI), as well as the Root Mean Squared Error (RMSE) over the whole spectrum. Our analysis of parameters was optimized using partial least squares (PLS) regression. Two experienced surgeons carried out an additional color-change analysis, comparing the ROI images and deciding whether they provided the same (acceptable) or different visual information (rejected).

RESULTS

HSI and subsequent image registration was possible in 19 patients. MAE results for the original calculation were StO_{2 orig.} 17.2% (± 7.7%)_, NIR-PI_orig. 16.0 (± 9.5), TWI_orig. 18.1 (± 7.9), OHI_orig. 14.4 (± 4.5). For the PLS calculation, they were StO_{2 PLS} 12.6% (± 5.2%), NIR-PI_PLS 10.3 (± 6.0), TWI_PLS 10.6 (± 5.1), and OHI_PLS 11.6 (± 3.0). The RMSE between both systems was 0.14 (± 0.06). In the color-change analysis; both surgeons accepted more images generated using the PLS method.

CONCLUSION

Intraoperative HSI-MIS is a new technology and holds great potential for future applications in surgery. Parameter deviations are attributable to technical differences and can be reduced by applying improved calculation methods. This study is an important step toward the clinical implementation of HSI for minimally invasive surgery.

Comparison of Artificial Neural Network and Polynomial Approximation Models for Reflectance Spectra Reconstruction

Knowledge of surface reflection of an object is essential in many technological fields, including graphics and cultural heritage. Compared to direct multi- or hyper-spectral capturing approaches, commercial RGB cameras allow for a high resolution and fast acquisition, so the idea of mapping this information into a reflectance spectrum (RS) is promising. This study compared two modelling approaches based on a training set of RGB-reflectance pairs, one implementing artificial neural networks (ANN) and the other one using multivariate polynomial approximation (PA). The effect of various parameters was investigated: the ANN learning algorithm-standard backpropagation (BP) or Levenberg-Marquardt (LM), the number of hidden layers (HLs) and neurons, the degree of multivariate polynomials in PA, the number of inputs, and the training set size on both models. In the two-layer ANN with significantly fewer inputs than outputs, a better MSE performance was found where the number of neurons in the first HL was smaller than in the second one. For ANNs with one and two HLs with the same number of neurons in the first layer, the RS reconstruction performance depends on the choice of BP or LM learning algorithm. RS reconstruction methods based on ANN and PA are comparable, but the ANN models' better fine-tuning capabilities enable, under realistic constraints, finding ANNs that outperform PA models. A profiling approach was proposed to determine the initial number of neurons in HLs-the search centre of ANN models for different training set sizes.

Observation of Tissue Oxygenation Changes Using Remote Photoplesysmography with a Smartphone

BACKGROUND

Tissue oxygenation is a critical marker of tissue status and can be used to evaluate and track wound progress, the viability of transplanted tissue, and burns. Thus, the determination of tissue oxygenation (preferably remotely) is of great importance.

AIM

Explore the impact of oxygenation changes on tissue color.

MATERIAL AND METHODS

The rPPG of both hands was acquired using a stand-mounted smartphone (iPhone 8) placed about 10 cm above the hands. A 60 s baseline was followed by occlusion of one arm using a cuff inflated to 200 mmHg for approximately 2 min. The cuff was then rapidly deflated, followed by a 60 s recovery period. The reference muscle oxygenation signal (SmO2) was acquired using the near-infrared contact Moxy device (Fortiori Design LLC) placed on the forearm distal to the occlusion. The data were collected on both hands of 28 healthy volunteers.

RESULTS

rPPG can observe changes in tissue oxygenation, which was confirmed across 28 participants using a robust reference standard.

CONCLUSION

We have an initial confirmation of the notion that rPPG can monitor changes in tissue oxygenation. However, a spectrum of rPPG and SmO2 reductions is observed, which should be explored in future work.

Initial Clinical Experience with a Simple, Home System for Early Detection and Monitoring of Diabetic Foot Ulcers: The Foot Selfie

BACKGROUND

Diabetic foot ulcers (DFUs) are a leading cause of disability and morbidity. There is an unmet need for a simple, practical, home method to detect DFUs early and remotely monitor their healing.

METHOD

We developed a simple, inexpensive, smartphone-based, "Foot Selfie" system that enables patients to photograph the plantar surface of their feet without assistance and transmit images to a remote server. In a pilot study, patients from a limb-salvage clinic were asked to image their feet daily for six months and to evaluate the system by questionnaire at five time points. Transmitted results were reviewed weekly.

RESULTS

Fifteen patients (10 male) used the system after approximately 5 minutes of instruction. Participants uploaded images on a median of 76% of eligible study days. The system captured and transmitted diagnostic quality images of the entire plantar surface of both feet, permitting clinical-management decisions on a remote basis. We monitored 12 active wounds and 39 pre-ulcerative lesions (five wounds and 13 pre-ulcerative lesions at study outset); we observed healing of seven wounds and reversal of 20 pre-ulcerative lesions. Participants rated the system as useful, empowering, and preferable to their previous methods of foot screening.

CONCLUSIONS

With minimal training, patients transmitted diagnostic-quality images from home on most days, allowing clinicians to review serial images. This system permits inexpensive home foot screening and monitoring of DFUs. Further studies are needed to determine whether it can reduce morbidity of DFUs and/or the associated cost of care. Artificial intelligence integration could improve scalability.

Evaluation of Preprocessing Methods on Independent Medical Hyperspectral Databases to Improve Analysis

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.

Assessment of Hyperspectral Imaging in Pressure Injury Healing

OBJECTIVE

To analyze the blood oxygen concentrations (StO 2 ) of different stages of pressure injury (PI) tissue using hyperspectral images to serve as a guideline for the treatment and care of PIs.

METHODS

This study used a prospective design. A total of 30 patients with sacral PIs were recruited from the rehabilitation ward of a teaching hospital. The authors used a hyperspectral detector to collect wound images and the Beer-Lambert law to estimate changes in tissue StO 2 in different stages of PI.

RESULTS

The tissue StO 2 of healthy skin and that of stage 1 PI skin were similar, whereas the tissue StO 2 of the wound in stage 2 PIs was significantly higher than that of healthy skin and scabbed tissue (medians, 82.5%, 74.4%, and 68.3%; P < .05). In stage 3 PIs, StO 2 was highest in subcutaneous tissue and adipose tissue (82.5%) and lowest in peripheral scabs (68.35%). The tissue StO 2 was highest in subcutaneous tissue in stage 4 PIs, and this tissue was red in the hyperspectral spectrum. The scab-covered area of unstageable PIs had the lowest StO 2 of all PI tissue types (median, 44.3%).

CONCLUSIONS

Hyperspectral imaging provides physiologic information on wound microcirculation, which can enable better evaluation of healing status. Assessing tissue StO 2 data can provide a clinical index of wound healing.

Comparison of Hyperspectral Imaging and Microvascular Doppler for Perfusion Monitoring of Free Flaps in an In Vivo Rodent Model

To reduce microvascular free flap failure (MFF), monitoring is crucial for the early detection of malperfusion and allows timely salvage. Therefore, the aim of this study was to evaluate hyperspectral imaging (HSI) in comparison to micro-Doppler sonography (MDS) to monitor MFF perfusion in an in vivo rodent model. Bilateral groin flaps were raised on 20 Sprague−Dawley rats. The femoral artery was transected on the trial side and re-anastomosed. Flaps and anastomoses were assessed before, during, and after the period of ischemia every ten minutes for overall 60 min using HSI and MDS. The contralateral sides’ flaps served as controls. Tissue-oxygenation saturation (StO2), near-infrared perfusion index (NPI), hemoglobin (THI), and water distribution (TWI) were assessed by HSI, while blood flow was assessed by MDS. HSI correlates with the MDS signal in the case of sufficient and completely interrupted perfusion. HSI was able to validly and reproducibly detect tissue perfusion status using StO2 and NPI. After 40 min, flap perfusion decreased due to the general aggravation of hemodynamic circulatory situation, which resulted in a significant drop of StO2 (p < 0.005) and NPI (p < 0.005), whereas the Doppler signal remained unchanged. In accordance, HSI might be suitable to detect MFF general complications in an early stage and further decrease MFF failure rates, whereas MDS may only be used for direct complications at the anastomose site.

[Artificial intelligence and hyperspectral imaging for image-guided assistance in minimally invasive surgery]

BACKGROUND

Intraoperative imaging assists surgeons during minimally invasive procedures. Hyperspectral imaging (HSI) is a noninvasive and noncontact optical technique with great diagnostic potential in medicine. The combination with artificial intelligence (AI) approaches to analyze HSI data is called intelligent HSI in this article.

OBJECTIVE

What are the medical applications and advantages of intelligent HSI for minimally invasive visceral surgery?

MATERIAL AND METHODS

Within various clinical studies HSI data from multiple in vivo tissue types and oncological resections were acquired using an HSI camera system. Different AI algorithms were evaluated for detection and discrimination of organs, risk structures and tumors.

RESULTS

In an experimental animal study 20 different organs could be differentiated with high precision (> 95%) using AI. In vivo, the parathyroid glands could be discriminated from surrounding tissue with an F1 score of 47% and sensitivity of 75%, and the bile duct with an F1 score of 79% and sensitivity of 90%. Furthermore, ex vivo tumor tissue could be successfully detected with an area under the receiver operating characteristic (ROC) curve (AUC) larger than 0.91.

DISCUSSION

This study demonstrates that intelligent HSI can automatically and accurately detect different tissue types. Despite great progress in the last decade intelligent HSI still has limitations. Thus, accurate AI algorithms that are easier to understand for the user and an extensive standardized and continuously growing database are needed. Further clinical studies should support the various medical applications and lead to the adoption of intelligent HSI in the clinical routine practice.

Review on the Application of Hyperspectral Imaging Technology of the Exposed Cortex in Cerebral Surgery

The study of brain science is vital to human health. The application of hyperspectral imaging in biomedical fields has grown dramatically in recent years due to their unique optical imaging method and multidimensional information acquisition. Hyperspectral imaging technology can acquire two-dimensional spatial information and one-dimensional spectral information of biological samples simultaneously, covering the ultraviolet, visible and infrared spectral ranges with high spectral resolution, which can provide diagnostic information about the physiological, morphological and biochemical components of tissues and organs. This technology also presents finer spectral features for brain imaging studies, and further provides more auxiliary information for cerebral disease research. This paper reviews the recent advance of hyperspectral imaging in cerebral diagnosis. Firstly, the experimental setup, image acquisition and pre-processing, and analysis methods of hyperspectral technology were introduced. Secondly, the latest research progress and applications of hyperspectral imaging in brain tissue metabolism, hemodynamics, and brain cancer diagnosis in recent years were summarized briefly. Finally, the limitations of the application of hyperspectral imaging in cerebral disease diagnosis field were analyzed, and the future development direction was proposed.

Quantitative Luminescence Photography of a Swellable Hydrogel Dressing with a Traffic-Light Response to Oxygen

Sensor-integrated wound dressings are emerging tools applicable to a wide variety of medical applications from emergency triage to at-home monitoring. Uncomfortable, unnecessary wound dressing changes may be avoided by providing quantitative insight into tissue characteristics related to wound healing such as tissue oxygenation, pH, and exudate/transudate volume. Here, a simple cost-effective methodology for quantifying oxygen and pH in a swellable hydrogel dressing using a single photograph is presented. The red and green luminescence of a novel dendritic polyamine Pt-porphyrin and fluorescein conjugate quantitatively responds to oxygen and pH, respectively, and enables robust sensing. The porphyrin conjugate, when combined with a four-arm star polyethylene glycol (PEG) amine polymer, rapidly crosslinks at room temperature with an N-hydroxysuccinimide (NHS)-PEG crosslinker to form a color-changing hydrogel dressing with tunable swelling capabilities applicable to a variety of wound environments. An inexpensive digital single-lens reflex (DSLR) camera modified with bandpass filters captures the hydrogel luminescence using simple macroscopic photography, and conversion to HSB colorspace allows for intensity-independent image analysis of the hydrogels' dual modality response. The hydrogel formulation exhibits a robust and validated visible red-orange-green "traffic light" spectrum in response to oxygen changes, regardless of swelling state, pH, or autofluorescence from skin, thereby enabling the clinician friendly naked-eye feedback.

Potential Use of Novel Image and Signal Processing Methods to Develop a Quantitative Assessment of the Severity of Acute Radiation Dermatitis in Breast Cancer Radiotherapy

More than 95% of patients who undergo radiotherapy report symptoms of radiation dermatitis, which is a side effect of this therapy. Erythema, edema, dry and moist desquamation intensify with each fraction of irradiation and can significantly reduce a patient's quality of life. Therefore, an effective skin care procedure is needed for skin that has been exposed to ionizing radiation in order to avoid unplanned treatment interruptions. The methods that are currently used to assess the severity of an acute radiation reaction are based on visual scales (RTOG, EORTC, NCI CTCAE, LENT-SOMA). Because the assessment is made subjectively, the results depend on the researchers, their experience and perceptiveness. Until now, several studies have been carried out to check the possibility of using an objective methods like hyperspectral imaging, thermal imaging, laser Doppler flowmetry, dielectric and electrochemical methods, reflection spectrophotometry and Courage-Khazaka Multi-skin instrument to radiation-induced dermatitis assessment. Unfortunately, due to various limitations that occurred in the research, none of these techniques was successfully implement as alternative for visual assessment. The continuous development of technology enables researchers to access new techniques that might constitute useful diagnostic and cognitive tools. Infrared thermal imaging, hyperspectral imaging and reflectance spectroscopy are examples of the visual techniques that have been used for many years in various fields of medicine, including dermatology and chronic wound or burn care. They provide information on the skin parameters, such as the temperature, concentration and distribution of chromophores (eg, hemoglobin and melanin), saturation or perfusion changes. The aim of this study is to review the available literature on the use of imaging methods in the clinical assessment of skin with lesions of various origins, evaluation of their suitability for the assessment of radiation reaction and consideration the possibility of creating a quantitative scale for assessing severity of acute radiation dermatitis.

Evaluation of Hyperspectral Imaging for Follow-Up Assessment after Revascularization in Peripheral Artery Disease

Background: Assessment of tissue oxygenation is an important aspect of detection and monitoring of patients with peripheral artery disease (PAD). Hyperspectral imaging (HSI) is a non-contact technology for assessing microcirculatory function by quantifying tissue oxygen saturation (StO₂). This study investigated whether HSI can be used to monitor skin oxygenation in patients with PAD after appropriate treatment of the lower extremities. Methods: For this purpose, 37 patients with PAD were studied by means of ankle–brachial index (ABI) and HSI before and after surgical or endovascular therapy. Thereby, the oxygenation parameter StO₂ and near infrared (NIR) perfusion index were quantified in seven angiosomes on the diseased lower leg and foot. In addition, the effects of skin temperature and physical activity on StO₂ and the NIR perfusion index and the respective inter-operator variability of these parameters were investigated in 25 healthy volunteers. Results: In all patients, the ABI significantly increased after surgical and endovascular therapy. In parallel, HSI revealed significant changes in both StO₂ and NIR perfusion index in almost all studied angiosomes depending on the performed treatment. The increase in tissue oxygenation saturation was especially pronounced after surgical treatment. Neither heat nor cold, nor physical activity, nor repeated assessments of HSI parameters by independent investigators significantly affected the results on StO₂ and the NIR perfusion index. Conclusions: Tissue oxygen saturation data obtained with HSI are robust to external confounders, such as temperature and physical activity, and do not show inter-operator variability; therefore, can be used as an additional technique to established methods, such as the ABI, to monitor peripheral perfusion in patients with PAD.

Improved Optical Tissue Model for Tissue Oximetry Imaging Applications

INTRODUCTION

Chronic, non-healing wounds are a growing concern in healthcare delivery. Tissue oxygenation is recognised as critical to successful wound healing. However, the quality and quantity of the information extracted by hyperspectral imaging depend on the optical tissue model. This article aims to develop a simplified and computationally efficient approach comparable in quality with the two-layer model.

METHODS

We have considered the epidermal layer as a 'thin-film' within the dermal layer. By considering the mismatched boundary and developing a four-flux model for light transport within the tissue, we have obtained a quasi two-layer model with a closed-form solution similar to the single-layer model.

RESULTS

We have compared the developed model with the two-layer model (reference) and the single-layer model for the broad range of physiologically relevant parameters. The thickness of the epithelium: 50, 80, and 120 μm. Melanin concentration: 1, 2, 4, 8, 16, and 32%. Blood concentration: 0.2%, 1%, and 7%. Oxygen saturation: 60%, 80%, and 99%. Our initial results show that the accuracy of the proposed quasi two-layer model significantly (by a factor of 10) outperforms the single-layer model and is in close agreement with the two-layer model.

CONCLUSIONS

The proposed quasi two-layer model significantly (by the factor of 10) outperforms the single-layer model and is closely aligned with the two-layer model.

Hyperspectral imaging for perioperative monitoring of microcirculatory tissue oxygenation and tissue water content in pancreatic surgery - an observational clinical pilot study

BACKGROUND

Hyperspectral imaging (HSI) could provide extended haemodynamic monitoring of perioperative tissue oxygenation and tissue water content to visualize effects of haemodynamic therapy and surgical trauma. The objective of this study was to assess the capacity of HSI to monitor skin microcirculation and possible relations to perioperative organ dysfunction in patients undergoing pancreatic surgery.

METHODS

The hyperspectral imaging TIVITA® Tissue System was used to evaluate superficial tissue oxygenation (StO2), deeper layer tissue oxygenation (near-infrared perfusion index (NPI)), haemoglobin distribution (tissue haemoglobin index (THI)) and tissue water content (tissue water index (TWI)) in 25 patients undergoing pancreatic surgery. HSI parameters were measured before induction of anaesthesia (t1), after induction of anaesthesia (t2), postoperatively before anaesthesia emergence (t3), 6 h after emergence of anaesthesia (t4) and three times daily (08:00, 14:00, 20:00 ± 1 h) at the palm and the fingertips until the second postoperative day (t5-t10). Primary outcome was the correlation of HSI with perioperative organ dysfunction assessed with the perioperative change of SOFA score.

RESULTS

Two hundred and fifty HSI measurements were performed in 25 patients. Anaesthetic induction led to a significant increase of tissue oxygenation parameters StO2 and NPI (t1-t2). StO2 and NPI decreased significantly from t2 until the end of surgery (t3). THI of the palm showed a strong correlation with haemoglobin levels preoperatively (t2: r = 0.83, p < 0.001) and 6 h postoperatively (t4: r = 0.71, p = 0.001) but not before anaesthesia emergence (t3: r = 0.35, p = 0.10). TWI of the palm and the fingertip rose significantly between pre- and postoperative measurements (t2-t3). Higher blood loss, syndecan level and duration of surgery were associated with a higher increase of TWI. The perioperative change of HSI parameters (∆t1-t3) did not correlate with the perioperative change of the SOFA score.

CONCLUSION

This is the first study using HSI skin measurements to visualize tissue oxygenation and tissue water content in patients undergoing pancreatic surgery. HSI was able to measure short-term changes of tissue oxygenation during anaesthetic induction and pre- to postoperatively. TWI indicated a perioperative increase of tissue water content. Perioperative use of HSI could be a useful extension of haemodynamic monitoring to assess the microcirculatory response during haemodynamic therapy and major surgery.

TRIAL REGISTRATION

German Clinical Trial Register, DRKS00017313 on 5 June 2019.

VNIR-NIR hyperspectral imaging fusion targeting intraoperative brain cancer detection

Currently, intraoperative guidance tools used for brain tumor resection assistance during surgery have several limitations. Hyperspectral (HS) imaging is arising as a novel imaging technique that could offer new capabilities to delineate brain tumor tissue in surgical-time. However, the HS acquisition systems have some limitations regarding spatial and spectral resolution depending on the spectral range to be captured. Image fusion techniques combine information from different sensors to obtain an HS cube with improved spatial and spectral resolution. This paper describes the contributions to HS image fusion using two push-broom HS cameras, covering the visual and near-infrared (VNIR) [400–1000 nm] and near-infrared (NIR) [900–1700 nm] spectral ranges, which are integrated into an intraoperative HS acquisition system developed to delineate brain tumor tissue during neurosurgical procedures. Both HS images were registered using intensity-based and feature-based techniques with different geometric transformations to perform the HS image fusion, obtaining an HS cube with wide spectral range [435–1638 nm]. Four HS datasets were captured to verify the image registration and the fusion process. Moreover, segmentation and classification methods were evaluated to compare the performance results between the use of the VNIR and NIR data, independently, with respect to the fused data. The results reveal that the proposed methodology for fusing VNIR–NIR data improves the classification results up to 21% of accuracy with respect to the use of each data modality independently, depending on the targeted classification problem.

Beer-Lambert law for optical tissue diagnostics: current state of the art and the main limitations

SIGNIFICANCE

Beer-Lambert law (BLL) is a widely used tool for contact and remote determination of absorber concentration in various media, including living tissues. Originally proposed in the 18th century as a simple exponential expression, it has survived numerous modifications and updates. The basic assumptions of this law may not be fulfilled in real measurement conditions. This can lead to mistaken or misinterpreted results. In particular, the effects to be additionally taken into account in the tissue measurements include anisotropy, scattering, fluorescence, chemical equilibria, interference, dichroism, spectral bandwidth disagreements, stray radiation, and instrumental effects.

AIM

We review the current state of the art and the main limitations of remote tissue diagnostics using the BLL. Historical development of updating this law by taking into account specific additional factors such as light scattering and photon pathlengths in diffuse reflectance is described, along with highlighting the main risks to be considered by interpreting the measured data.

APPROACH

Literature data related to extension and modification of the BLL related to tissue assessment and concentration estimation of specific tissue molecules are collected and analyzed. The main emphasis here is put on the optical measurements of living tissue chromophore concentrations and estimation of physiological parameters, e.g., blood oxygen saturation.

RESULTS

Modified expressions of the BLL suitable for several specific cases of living tissue characterization are presented and discussed.

CONCLUSIONS

Applications of updated/modified Beer-Lambert law (MBLL) with respect to particular measurement conditions are helpful for obtaining more reliable data on the target tissue physiological state and biochemical content. MBLL accounting for the role of scattering in several ways appears to be a successful approach. Extended MBLL and BLL in the time domain form could provide more accurate results, but this requires more time resources to be spent.

A comparison of hyperspectral imaging with routine vascular noninvasive techniques to assess the healing prognosis in patients with diabetic foot ulcers

OBJECTIVE

To compare the potential healing prognosis of the different routine noninvasive techniques implemented in the International Working Group Diabetic Foot Guidelines with the novel use of hyperspectral imaging (HSI) in patients with diabetic foot ulcers (DFUs).

METHODS

Twenty-one patients with active DFUs participated in this 1-year prospective study in a specialized diabetic foot unit between December 2018 and January 2020. HSI was performed at baseline to quantify tissue oxygenation and should be presented on an anatomical map by analyzing the following parameters: (1) oxygen saturation of the hemoglobin, (2) tissue hemoglobin index, (3) the near-infrared perfusion index, and (4) tissue water index. In addition, transcutaneous oxygen pressure (TcpO₂), systolic toe and ankle pressures, ankle-brachial index, and toe-brachial index values were calculated for the ulcerated limb. The primary outcome measure was wound healing, defined as complete epithelization without any drainage confirmed for at least 10 days after closure was first documented at 24 weeks.

RESULTS

During the follow-up period 14 patients (66.66 %) healed and 7 patients did not heal (33.3%) by 24 weeks. The TcpO₂ optimal cut-off point as determined by a balance of sensitivity and specificity of 28.5 mm Hg that yielded a sensitivity of 91% and a specificity of 100%, and area under the curve of 0.989 (P = .005; 95% confidence interval [CI], 0.945-1.000). Followed by the oxygen saturation of the hemoglobin optimal cut-off point as determined by a balance of sensitivity and specificity of 48.5 mm Hg that yielded a sensitivity of 93% and a specificity of 0.71%, and area under the curve of 0.932 (P = .013; 95% CI, 0.787-1.000). The logistic regression analyses showed that TcpO₂ was the only variable associated with wound healing at 24 weeks (P < .001; 95% CI, 0.046-0.642).

CONCLUSIONS

The HSI was shown to be effective in the prognosis of DFU healing compared with other noninvasive test; only TcpO₂ values resulted in better diagnosis potential in wound healing.

Functional Imaging in Wounds: Imaging Modalities of Today and Tomorrow

Wound care is a multidisciplinary field with significant economic burden to our healthcare system. Not only does wound care cost the US healthcare system $20 billion annually, but wounds also remarkably impact the quality of life of patients; wounds pose significant risk of mortality, as the five-year mortality rate for diabetic foot ulcers (DFUs) and ischemic ulcers is notably higher compared to commonly encountered cancers such as breast and prostate. Although it is important to measure how wounds may or may not be improving over time, the only relative "marker" for this is wound area measurement-area measurements can help providers determine if a wound is on a healing or non-healing trajectory. Because wound area measurements are currently the only readily available "gold standard" for predicting healing outcomes, there is a pressing need to understand how other relative biomarkers may play a role in wound healing. Currently, wound care centers across the nation employ various techniques to obtain wound area measurements; length and width of a wound can be measured with a ruler, but this carries a high amount of inter- and intrapersonal error as well as uncertainty. Acetate tracings could be used to limit the amount of error but do not account for depth, thereby making them inaccurate. Here, we discuss current imaging modalities and how they can serve to accurately measure wound size and serve as useful adjuncts in wound assessment. Moreover, new imaging modalities are also discussed and how up-and-coming technologies can provide important information on "biomarkers" for wound healing.

Application of Hyperspectral Imaging for Maturity and Soluble Solids Content Determination of Strawberry With Deep Learning Approaches

Maturity degree and quality evaluation are important for strawberry harvest, trade, and consumption. Deep learning has been an efficient artificial intelligence tool for food and agro-products. Hyperspectral imaging coupled with deep learning was applied to determine the maturity degree and soluble solids content (SSC) of strawberries with four maturity degrees. Hyperspectral image of each strawberry was obtained and preprocessed, and the spectra were extracted from the images. One-dimension residual neural network (1D ResNet) and three-dimension (3D) ResNet were built using 1D spectra and 3D hyperspectral image as inputs for maturity degree evaluation. Good performances were obtained for maturity identification, with the classification accuracy over 84% for both 1D ResNet and 3D ResNet. The corresponding saliency maps showed that the pigments related wavelengths and image regions contributed more to the maturity identification. For SSC determination, 1D ResNet model was also built, with the determination of coefficient (R ²) over 0.55 of the training, validation, and testing sets. The saliency maps of 1D ResNet for the SSC determination were also explored. The overall results showed that deep learning could be used to identify strawberry maturity degree and determine SSC. More efforts were needed to explore the use of 3D deep learning methods for the SSC determination. The close results of 1D ResNet and 3D ResNet for classification indicated that more samples might be used to improve the performances of 3D ResNet. The results in this study would help to develop 1D and 3D deep learning models for fruit quality inspection and other researches using hyperspectral imaging, providing efficient analysis approaches of fruit quality inspection using hyperspectral imaging.

Hyperspectral imaging in wound care: A systematic review

Multispectral and hyperspectral imaging (HSI) are emerging imaging techniques with the potential to transform the way patients with wounds are cared for, but it is not clear whether current systems are capable of delivering real-time tissue characterisation and treatment guidance. We conducted a systematic review of HSI systems that have been assessed in patients, published over the past 32 years. We analysed 140 studies, including 10 different HSI systems. Current in vivo HSI systems generate a tissue oxygenation map. Tissue oxygenation measurements may help to predict those patients at risk of wound formation or delayed healing. No safety concerns were reported in any studies. A small number of studies have demonstrated the capabilities of in vivo label-free HSI, but further work is needed to fully integrate it into the current clinical workflow for different wound aetiologies. As an emerging imaging modality for medical applications, HSI offers great potential for non-invasive disease diagnosis and guidance when treating patients with both acute and chronic wounds.

Algorithm for Mapping Kidney Tissue Water Content during Normothermic Machine Perfusion Using Hyperspectral Imaging

The preservation of kidneys using normothermic machine perfusion (NMP) prior to transplantation has the potential for predictive evaluation of organ quality. Investigations concerning the quantitative assessment of physiological tissue parameters and their dependence on organ function lack in this context. In this study, hyperspectral imaging (HSI) in the wavelength range of 500–995 nm was conducted for the determination of tissue water content (TWC) in kidneys. The quantitative relationship between spectral data and the reference TWC values was established by partial least squares regression (PLSR). Different preprocessing methods were applied to investigate their influence on predicting the TWC of kidneys. In the full wavelength range, the best models for absorbance and reflectance spectra provided Rp2 values of 0.968 and 0.963, as well as root-mean-square error of prediction (RMSEP) values of 2.016 and 2.155, respectively. Considering an optimal wavelength range (800–980 nm), the best model based on reflectance spectra (Rp2 value of 0.941, RMSEP value of 3.202). Finally, the visualization of TWC distribution in all pixels of kidneys’ HSI image was implemented. The results show the feasibility of HSI for a non-invasively and accurate TWC prediction in kidneys, which could be used in the future to assess the quality of kidneys during the preservation period.

Quantifying dermal microcirculatory changes of neuropathic and neuroischemic diabetic foot ulcers using spatial frequency domain imaging: a shade of things to come?

INTRODUCTION

The use of non-invasive vascular and perfusion diagnostics are an important part of assessing lower extremity ulceration and amputation risk in patients with diabetes mellitus. Methods for detecting impaired microvascular vasodilatory function in patients with diabetes may have the potential to identify sites at risk of ulceration prior to clinically identifiable signs. Spatial frequency domain imaging (SFDI) uses patterned near-infrared and visible light spectroscopy to determine tissue oxygen saturation and hemoglobin distribution within the superficial and deep dermis, showing distinct microcirculatory and oxygenation changes that occur prior to neuropathic and neuroischemic ulceration.

RESEARCH DESIGNS AND METHODS

35 patients with diabetes mellitus and a history of diabetic foot ulceration were recruited for monthly imaging with SFDI. Two patients who ulcerated during the year-long longitudinal study were selected for presentation of their clinical course alongside the dermal microcirculation biomarkers from SFDI.

RESULTS

Patient 1 developed a neuropathic ulcer portended by a focal increase in tissue oxygen saturation and decrease in superficial papillary hemoglobin concentration 3 months prior. Patient 2 developed bilateral neuroischemic ulcers showing decreased tissue oxygen saturation and increased superficial papillary and deep dermal reticular hemoglobin concentrations.

CONCLUSIONS

Wounds of different etiology show unique dermal microcirculatory changes prior to gross ulceration. Before predictive models can be developed from SFDI, biomarker data must be correlated with the clinical course of patients who ulcerate while being followed longitudinally.

TRIAL REGISTRATION NUMBER

NCT03341559.

Hyperspectral imaging in wound care: A systematic review

Multispectral and hyperspectral imaging (HSI) are emerging imaging techniques with the potential to transform the way patients with wounds are cared for, but it is not clear whether current systems are capable of delivering real-time tissue characterisation and treatment guidance. We conducted a systematic review of HSI systems that have been assessed in patients, published over the past 32 years. We analysed 140 studies, including 10 different HSI systems. Current in vivo HSI systems generate a tissue oxygenation map. Tissue oxygenation measurements may help to predict those patients at risk of wound formation or delayed healing. No safety concerns were reported in any studies. A small number of studies have demonstrated the capabilities of in vivo label-free HSI, but further work is needed to fully integrate it into the current clinical workflow for different wound aetiologies. As an emerging imaging modality for medical applications, HSI offers great potential for non-invasive disease diagnosis and guidance when treating patients with both acute and chronic wounds.