Monitoring kidney optical properties during cold storage preservation with spatial frequency domain imaging

Emerging Technologies

In this article, an array of new developments in burn care, from diagnosis to post-burn reconstruction and re-integration, will be discussed. Multidisciplinary advances have allowed the implementation of technologies that provide more accurate assessments of burn depth, improved outcomes when treating full-thickness burns, and enhanced scar tissue management. Incorporating these new treatment modalities into current practice is essential to improving the standard of burn care and developing the next generation of burn wound management methodologies.

Evolution of Burn Care: Past, Present, and Future

Burn care evolved slowly from primitive treatments depicted in cave drawings 3500 years ago to a vibrant medical specialty which has made remarkable progress over the past 200 years. This evolution involved all areas of burn care including superficial dressings, wound assessment, fluid resuscitation, infection control, pathophysiology, nutritional support, burn surgery, and inhalation injury. Major advances that contributed to current standards of care and improved outcomes are highlighted in this article. New innovations are making possible a future where severe burn injuries will require less morbid interventions for acute care and outcomes will restore patients more closely to their pre-injury condition.

What are the triggers for palliative care referral in burn intensive care units? Results from a qualitative study based on healthcare professionals' views, clinical experiences and practices

BACKGROUND

Burns are a global public health problem, accounting for around 300,000 deaths annually. Burns have significant consequences for patients, families, healthcare teams and systems. Evidence suggests that the integration of palliative care in burn intensive care units improves patients' comfort, decision-making processes and family care. Research is needed on how to optimise palliative care referrals.

AIM

To identify triggers for palliative care referral in critically burned patients based on professionals' views, experiences and practices.

DESIGN

Qualitative study using in-depth interviews.

SETTING/PARTICIPANTS

All five Burn Intensive Care Units reference centres across Portugal were invited; three participated. Inclusion criteria: Professionals with experience/working in these settings. A total of 15 professionals (12 nurses and 3 physicians) participated. Reflexive thematic analysis was performed.

RESULTS

Three main triggers for palliative care referral were identified: (i) Burn severity and extension, (ii) Co-morbidities and (iii) Multiorgan failure. Other triggers were also generated: (i) Rehabilitative palliative care related to patients' suffering and changes in body image, (ii) Family suffering and/or dysfunctional and complex family processes, (iii) Long stay in the burn intensive care unit and (iv) Uncontrolled pain.

CONCLUSIONS

This study identifies triggers for palliative care in burn intensive care units based on professionals' views, clinical experiences and practices. The systematisation and use of triggers could help streamline referral pathways and strengthen the integration of palliative care in burn intensive care units. Research is needed on the use of these triggers in clinical practice to enhance decision-making processes, early and high-quality integrated palliative care and proportionate patient and family centred care.

Non-invasive medical imaging technology for the diagnosis of burn depth

Currently, the clinical diagnosis of burn depth primarily relies on physicians' judgements based on patients' symptoms and physical signs, particularly the morphological characteristics of the wound. This method highly depends on individual doctors' clinical experience, proving challenging for less experienced or primary care physicians, with results often varying from one practitioner to another. Therefore, scholars have been exploring an objective and quantitative auxiliary examination technique to enhance the accuracy and consistency of burn depth diagnosis. Non-invasive medical imaging technology, with its significant advantages in examining tissue surface morphology, blood flow in deep and changes in structure and composition, has become a hot topic in burn diagnostic technology research in recent years. This paper reviews various non-invasive medical imaging technologies that have shown potential in burn depth diagnosis. These technologies are summarized and synthesized in terms of imaging principles, current research status, advantages and limitations, aiming to provide a reference for clinical application or research for burn specialists.

Application of Artificial Intelligence Methodologies to Chronic Wound Care and Management: A Scoping Review

Significance: As the number of hard-to-heal wound cases rises with the aging of the population and the spread of chronic diseases, health care professionals struggle to provide safe and effective care to all their patients simultaneously. This study aimed at providing an in-depth overview of the relevant methodologies of artificial intelligence (AI) and their potential implementation to support these growing needs of wound care and management. Recent Advances: MEDLINE, Compendex, Scopus, Web of Science, and IEEE databases were all searched for new AI methods or novel uses of existing AI methods for the diagnosis or management of hard-to-heal wounds. We only included English peer-reviewed original articles, conference proceedings, published patent applications, or granted patents (not older than 2010) where the performance of the utilized AI algorithms was reported. Based on these criteria, a total of 75 studies were eligible for inclusion. These varied by the type of the utilized AI methodology, the wound type, the medical record/database configuration, and the research goal. Critical Issues: AI methodologies appear to have a strong positive impact and prospects in the wound care and management arena. Another important development that emerged from the findings is AI-based remote consultation systems utilizing smartphones and tablets for data collection and connectivity. Future Directions: The implementation of machine-learning algorithms in the diagnosis and managements of hard-to-heal wounds is a promising approach for improving the wound care delivered to hospitalized patients, while allowing health care professionals to manage their working time more efficiently.

Optical imaging of the small intestine immune compartment across scales

The limitations of 2D microscopy constrain our ability to observe and understand tissue-wide networks that are, by nature, 3-dimensional. Optical projection tomography (OPT) enables the acquisition of large volumes (ranging from micrometres to centimetres) in various tissues. We present a multi-modal workflow for the characterization of both structural and quantitative parameters of the mouse small intestine. As proof of principle, we evidence its applicability for imaging the mouse intestinal immune compartment and surrounding mucosal structures. We quantify the volumetric size and spatial distribution of Isolated Lymphoid Follicles (ILFs) and quantify the density of villi throughout centimetre-long segments of intestine. Furthermore, we exhibit the age and microbiota dependence for ILF development, and leverage a technique that we call reverse-OPT for identifying and homing in on regions of interest. Several quantification capabilities are displayed, including villous density in the autofluorescent channel and the size and spatial distribution of the signal of interest at millimetre-scale volumes. The concatenation of 3D imaging with reverse-OPT and high-resolution 2D imaging allows accurate localisation of ROIs and adds value to interpretations made in 3D. Importantly, OPT may be used to identify sparsely-distributed regions of interest in large volumes whilst retaining compatibility with high-resolution microscopy modalities, including confocal microscopy. We believe this pipeline to be approachable for a wide-range of specialties, and to provide a new method for characterisation of the mouse intestinal immune compartment.

Triage of in vivo burn injuries and prediction of wound healing outcome using neural networks and modeling of the terahertz permittivity based on the double Debye dielectric parameters

The initial assessment of the depth of a burn injury during triage forms the basis for determination of the course of the clinical treatment plan. However, severe skin burns are highly dynamic and hard to predict. This results in a low accuracy rate of about 60 - 75% in the diagnosis of partial-thickness burns in the acute post-burn period. Terahertz time-domain spectroscopy (THz-TDS) has demonstrated a significant potential for non-invasive and timely estimation of the burn severity. Here, we describe a methodology for the measurement and numerical modeling of the dielectric permittivity of the in vivo porcine skin burns. We use the double Debye dielectric relaxation theory to model the permittivity of the burned tissue. We further investigate the origins of dielectric contrast between the burns of various severity, as determined histologically based on the percentage of the burned dermis, using the empirical Debye parameters. We demonstrate that the five parameters of the double Debye model can form an artificial neural network classification algorithm capable of automatic diagnosis of the severity of the burn injuries, and predicting its ultimate wound healing outcome by forecasting its re-epithelialization status in 28 days. Our results demonstrate that the Debye dielectric parameters provide a physics-based approach for the extraction of the biomedical diagnostic markers from the broadband THz pulses. This method can significantly boost dimensionality reduction of THz training data in artificial intelligence models and streamline machine learning algorithms.

Burn characterization using object-oriented hyperspectral image classification

This paper presents a new approach based on hyperspectral imaging combined with an object-oriented classification method that allows the generation of burn depth classification maps facilitating easier characterization of burns. Hyperspectral images of 14 patients diagnosed with burns on the upper and lower limbs were acquired using a pushbroom hyperspectral imaging system. The images were analyzed using an object-oriented classification approach that uses objects with specific spectral, textural and spatial attributes as the minimum unit for classifying information. The method performance was evaluated in terms of overall accuracy, sensitivity, precision and specificity computed from the confusion matrix. The results revealed that the approach proposed in this study performed well in differentiating burn classes with a high level of overall accuracy (95.99% ± 0.60%), precision (97.30% ± 2.46%), sensitivity (97.23% ± 3.02%) and specificity (98.02% ± 1.98%). In conclusion, the object-based approach for burns hyperspectral images classification can provide maps that can help surgeons identify with better precision different depths of burn wounds.

Burn depth assessment using hyperspectral imaging in a prospective single center study

BACKGROUND

The assessment of thermal burn depth remains challenging. Over the last decades, several optical systems were developed to determine burn depth. So far, only laser doppler imaging (LDI) has been shown to be reliable while others such as infrared thermography or spectrophotometric intracutaneous analysis have been less accurate. The aim of our study is to evaluate hyperspectral imaging (HSI) as a new optical device.

METHODS

Patients suffering thermal trauma treated in a burn unit in Germany between November 2019 and September 2020 were included. Inclusion criteria were age ≥18 years, 2^nd or 3^rd degree thermal burns, written informed consent and presentation within 24 h after injury. Clinical assessment and hyperspectral imaging were performed 24, 48 and 72 h after the injury. Patients in whom secondary wound closure was complete within 21 days (group A) were compared to patients in whom secondary wound closure took more than 21 days or where skin grafting was indicated (group B). Demographic data and the primary parameters generated by HSI were documented. A Mann Whitney-U test was performed to compare the groups. A p-value below 0.05 was considered to be statistically significant. The data generated using HSI were combined to create the HSI burn index (BI). Using a logistic regression and receiver operating characteristics curve (ROC) sensitivity and specificity of the BI were calculated. The trial was officially registered on DRKS (registration number: DRKS00022843).

RESULTS

Overall, 59 patients with burn wounds were eligible for inclusion. Ten patients were excluded because of a poor data quality. Group A comprised 36 patients with a mean age of 41.5 years and a mean burnt body surface area of 2.7%. In comparison, 13 patients were allocated to group B because of the need for a skin graft (n = 10) or protracted secondary wound closure lasting more than 21 days. The mean age of these patients was 46.8 years. They had a mean affected body surface area of 4.0%. 24, 48, and 72 h after trauma the BI was 1.0 ± 0.28, 1.2 ± 0.29 and 1.55 ± 0.27 in group A and 0.78 ± 0.14, 1.05 ± 0.23 and 1.23 ± 0.27 in group B. At every time point significant differences were demonstrated between the groups. At 24 h, ROC analysis demonstrated BI threshold of 0.95 (sensitivity 0.61/specificity 1.0), on the second day of 1.17 (sensitivity 0.51/specificity 0.81) and on the third day of 1.27 (sensitivity 0.92/specificity 0.71).

CONCLUSION

Changes in microcirculation within the first 72 h after thermal trauma were reflected by an increasing BI in both groups. After 72 h, the BI is able to predict the need for a skin graft with a sensitivity of 92% and a specificity of 71%.

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.

Phase function estimation from a diffuse optical image via deep learning

Objective. The phase function is a key element of a light propagation model for Monte Carlo (MC) simulation, which is usually fitted with an analytic function with associated parameters. In recent years, machine learning methods were reported to estimate the parameters of the phase function of a particular form such as the Henyey–Greenstein phase function but, to our knowledge, no studies have been performed to determine the form of the phase function. Approach. Here we design a convolutional neural network (CNN) to estimate the phase function from a diffuse optical image without any explicit assumption on the form of the phase function. Specifically, we use a Gaussian mixture model (GMM) as an example to represent the phase function generally and learn the model parameters accurately. The GMM is selected because it provides the analytic expression of phase function to facilitate deflection angle sampling in MC simulation, and does not significantly increase the number of free parameters. Main Results. Our proposed method is validated on MC-simulated reflectance images of typical biological tissues using the Henyey–Greenstein phase function with different anisotropy factors. The mean squared error of the phase function is 0.01 and the relative error of the anisotropy factor is 3.28%. Significance. We propose the first data-driven CNN-based inverse MC model to estimate the form of scattering phase function. The effects of field of view and spatial resolution are analyzed and the findings provide guidelines for optimizing the experimental protocol in practical applications.

Analysis on factors behind sentinel lymph node metastasis in breast cancer by color ultrasonography, molybdenum target, and pathological detection

BACKGROUND

This study aimed to identify the factors underlying the metastasis of breast cancer and sentinel lymph nodes and to screen and analyze the risk factors of sentinel lymph node metastasis to provide a reference and basis for clinical work.

METHODS

A total of 99 patients with breast cancer were enrolled in this study. These patients received treatment in our hospital between May 2017 and May 2020. The general information, characteristics of the color Doppler echocardiography, molybdenum, conventional pathology, and molecular pathology of the patients were collected. Factors influencing sentinel lymph node metastasis in breast cancer patients were retrospectively analyzed.

RESULTS

In this study, age, tumor diameter, BI-RADS category, pathology type, expression profiles of CK5/6, EGFR, and CK19, and TP53 and BRAC1/2 mutations were independent risk factors for sentinel lymph node metastasis in breast cancer (P < 0.05). The number and locations of tumors, quadrant of tumors, regularity of tumor margins, presence of blood flow signals, presence of posterior echo attenuation, presence of calcification, histological grade, molecular typing, and mutations of BRAF, ATM, and PALB2 were irrelevant factors (P > 0.05).

CONCLUSIONS

In conclusion, age, tumor diameter, BI-RADS category, invasive type, expression of CK5/6, EGFR, and CK19, and mutations in TP53 and BRAC1/2 were positively correlated with sentinel lymph node metastasis. These independent risk factors should be given more attention in clinical studies to strengthen the management and control of sentinel lymph node metastasis in high-risk breast cancer and support early chemotherapy or targeted therapy.

Deep learning in macroscopic diffuse optical imaging

SIGNIFICANCE

Biomedical optics system design, image formation, and image analysis have primarily been guided by classical physical modeling and signal processing methodologies. Recently, however, deep learning (DL) has become a major paradigm in computational modeling and has demonstrated utility in numerous scientific domains and various forms of data analysis.

AIM

We aim to comprehensively review the use of DL applied to macroscopic diffuse optical imaging (DOI).

APPROACH

First, we provide a layman introduction to DL. Then, the review summarizes current DL work in some of the most active areas of this field, including optical properties retrieval, fluorescence lifetime imaging, and diffuse optical tomography.

RESULTS

The advantages of using DL for DOI versus conventional inverse solvers cited in the literature reviewed herein are numerous. These include, among others, a decrease in analysis time (often by many orders of magnitude), increased quantitative reconstruction quality, robustness to noise, and the unique capability to learn complex end-to-end relationships.

CONCLUSIONS

The heavily validated capability of DL's use across a wide range of complex inverse solving methodologies has enormous potential to bring novel DOI modalities, otherwise deemed impractical for clinical translation, to the patient's bedside.

Technical Note: On the development of an outcome-driven frequency filter for improving Radiomics-based modeling of Human Papilloma Virus (HPV) in patients with oropharyngeal squamous cell carcinomas

PURPOSE

To implement an outcome-driven frequency filter for improving radiomics-based modeling of human papilloma virus (HPV) for patients with oropharyngeal squamous cell carcinoma (OPSCC).

METHODS AND MATERIALS

One hundred twenty-eight OPSCC patients with known HPV status (60-HPV+ and 68-HPV-, confirmed by immunohistochemistry-P16 protein testing) were retrospectively studied. A 3D Discrete Fourier Transform was applied on contrast-enhanced CT images of patient gross tumor volumes (GTV's) to transform intensity distributions to the frequency domain and estimate frequency power spectrums of HPV- and HPV+ patient cohorts. Statistical analyses were performed to rank frequency bands contributing towards prediction of HPV status. An outcome-driven frequency filter was designed accordingly and applied to GTV frequency information. 3D Inverse-Discrete-Fourier-Transform was applied to reconstruct HPV-related frequency-filtered images. Radiomics features (11 feature-categories) were extracted from pre- and post- frequency filtered images using our previously published 'ROdiomiX' software. Least-Absolute-Shrinkage-and-Selection-Operation (Lasso) combined with a Generalized-Linear-Model (Lasso-GLM) was developed to identify and rank feature subsets with optimal information for prediction of HPV+/-. Radiomics-based Lasso-GLM classifiers (pre- and post-frequency filtered) were constructed and validated using a random permutation sampling and nested cross-validation techniques. Average Area Under Receiver Operating Characteristic (AUC), and Positive and Negative Predictive values (PPV, NPV) were computed to estimate generalization error and prediction performance.

RESULTS

Among 192 radiomic features, 15 features were found to be statistically significant discriminators between HPV+/- cohorts on post-frequency filtered CE-CT images; 14 such radiomic features were observed on pre-frequency filtered datasets. Discriminant features included tumor morphology and intensity contrast. Performances for prediction of HPV for the pre- and post-frequency filtered Lasso-GLM classifiers were: AUC/PPV/NPV = 0.789/0.755/0.805 and 0.850/0.808/0.877 respectively. Nested-CV performances for prediction of HPV for the pre- and post-frequency filtered Lasso-GLM classifiers were: AUC/PPV/NPV = 0.814/0.725/0.877 and 0.890/0.820/0.911 respectively.

CONCLUSION

Albeit subject to confirmation in a larger cohort, this pilot study presents encouraging results on the importance of frequency analysis prior to radiomic feature extraction toward enhancement of model performance for characterizing HPV in patients with OPSCC. This article is protected by copyright. All rights reserved.

A systematic review of machine learning and automation in burn wound evaluation: A promising but developing frontier

BACKGROUND

Visual evaluation is the most common method of evaluating burn wounds. Its subjective nature can lead to inaccurate diagnoses and inappropriate burn center referrals. Machine learning may provide an objective solution. The objective of this study is to summarize the literature on ML in burn wound evaluation.

METHODS

A systematic review of articles published between January 2000 and January 2021 was performed using PubMed and MEDLINE (OVID). Articles reporting on ML or automation to evaluate burn wounds were included. Keywords included burns, machine/deep learning, artificial intelligence, burn classification technology, and mobile applications. Data were extracted on study design, method of data acquisition, machine learning techniques, and machine learning accuracy.

RESULTS

Thirty articles were included. Nine studies used machine learning and automation to estimate percent total body surface area (%TBSA) burned, 4 calculated fluid estimations, 19 estimated burn depth, 5 estimated need for surgery, and 2 evaluated scarring. Models calculating %TBSA burned demonstrated accuracies comparable to or better than paper methods. Burn depth classification models achieved accuracies of >83%.

CONCLUSION

Machine learning provides an objective adjunct that may improve diagnostic accuracy in evaluating burn wound severity. Existing models remain in the early stages with future studies needed to assess their clinical feasibility.

Diagnostics for Wound Infections

Significance: Infections can significantly delay the healing process in chronic wounds, placing an enormous economic burden on health care resources. Identification of infection biomarkers and imaging modalities to observe and quantify them has seen progress over the years. Recent Advances: Traditionally, clinicians determine the presence of infection through visual observation of wounds and confirm their diagnosis through wound culture. Many laboratory markers, including C-reactive protein, procalcitonin, presepsin, and bacterial protease activity, have been quantified to assist diagnosis of infection. Moreover, imaging modalities like plain radiography, computed tomography, magnetic resonance imaging, ultrasound imaging, spatial frequency domain imaging, thermography, autofluorescence imaging, and biosensors have emerged for real-time wound infection diagnosis and showed their unique advantages in deeper wound infection diagnosis. Critical Issues: While traditional diagnostic approaches provide valuable information, they are time-consuming and depend on clinicians' experiences. There is a need for noninvasive wound infection diagnostics that are highly specific, rapid, and accurate, and do not require extensive training. Future Directions: While innovative diagnostics utilizing various imaging instrumentation are being developed, new biomarkers have been investigated as potential indicators for wound infection. Products may be developed to either qualitatively or quantitatively measure these biomarkers. This review summarizes and compares all available diagnostics for wound infection, including those currently used in clinics and still under development. This review could serve as a valuable resource for clinicians treating wound infections as well as patients and wound care providers who would like to be informed of the recent developments.

Machine learning estimation of tissue optical properties

Dynamic, in vivo measurement of the optical properties of biological tissues is still an elusive and critically important problem. Here we develop a technique for inverting a Monte Carlo simulation to extract tissue optical properties from the statistical moments of the spatio-temporal response of the tissue by training a 5-layer fully connected neural network. We demonstrate the accuracy of the method across a very wide parameter space on a single homogeneous layer tissue model and demonstrate that the method is insensitive to parameter selection of the neural network model itself. Finally, we propose an experimental setup capable of measuring the required information in real time in an in vivo environment and demonstrate proof-of-concept level experimental results.