Machine learning utilising spectral derivative data improves cellular health classification through hyperspectral infra-red spectroscopy

The objective differentiation of facets of cellular metabolism is important for several clinical applications, including accurate definition of tumour boundaries and targeted wound debridement. To this end, spectral biomarkers to differentiate live and necrotic/apoptotic cells have been defined using in vitro methods. The delineation of different cellular states using spectroscopic methods is difficult due to the complex nature of these biological processes. Sophisticated, objective classification methods will therefore be important for such differentiation. In this study, spectral data from healthy/traumatised cell samples using hyperspectral imaging between 2500-3500 nm were collected using a portable prototype device. Machine learning algorithms, in the form of clustering, have been performed on a variety of pre-processing data types including 'raw' unprocessed, smoothed resampling, background subtracted and spectral derivative. The resulting clusters were utilised as a diagnostic tool for the assessment of cellular health and quantified using both sensitivity and specificity to compare the different analysis methods. The raw data exhibited differences for one of the three different trauma types applied, although unable to accurately cluster all the traumatised samples due to signal contamination from the chemical insult. The background subtracted and smoothed data sets reduced the accuracy further, due to the apparent removal of key spectral features which exhibit cellular health. However, the spectral derivative data-types significantly improved the accuracy of clustering compared to other data types, with both sensitivity and specificity for the background subtracted data set being >94% highlighting its utility to account for unknown signal contamination while maintaining important cellular spectral features

Infrared hyperspectral imaging for point-of-care wound assessment

Wound healing assessment and management are both important in ensuring a correct healing sequence. Most of these assessment techniques involve simple observation with the naked eye, which causes two main issues: the parameters assessed are highly subjective, and they rely upon the knowledge and experience of a trained medical professional. Any failure or incorrect management can result in further complications and even fatality, therefore quantitative wound assessment techniques are the next step towards a more accessible and reliable wound management strategy. Current research in this field is focused on utilising non-invasive imaging techniques, mainly within the visible and infrared (IR) range, to identify the biological and chemical changes during the wound healing process. Any abnormalities can then be identified earlier to aid in the correct diagnosis and treatment of the wound. Technologies that utilise concepts of non-contact imaging, such as optical imaging and spectroscopy can be used to obtain spatial and spectral maps of biomarkers, which provide valuable information on the wound (e.g., precursors to improper healing or delineate viable and necrotic tissue). This work extends this research further by investigating two different imaging modalities, Negative Contrast Imaging (NCI), along with Spatial Frequency Domain Imaging (SFDI) for the applications of point of care wound assessment. Intelligent data analysis algorithms, in the form of k-means clustering and principal component analysis were applied to spectral data, collected from wound biopsies as part of a previous study, highlighting the ability to diagnose wound healing status from the contrast of spectral information, which is not reliant upon a subjective clinical diagnosis. These methods provided the motivation for a larger cell culture trauma study, in which the NCI was utilised to obtain spectral reflectance maps across a 2.5- 3.5 μm wavelength region of both healthy and traumatised human epidermal fibroblasts, induced via chemical assays. Using the same intelligent analysis tools, along with pre-processing methods including spectral derivatives, the resulting clusters can be utilised as a diagnostic tool for the assessment of cellular health and were quantifiable metrics were defined to compare the different analysis methods Near infrared (NIR) methodologies were also investigated, with two areas of SFDI identified for further advancements. Current SFDI acquisition and optical property parameter recovery is performed via a pixel-wise process, generating large amounts of data and a high computational burden for parameter recovery. Data reduction, through the application of Compressive Sensing (CS) at both the image acquisition and data analysis stages provided up to a 90% reduction in data, whilst maintaining <10% error in recovered absorption and reduced scattering optical maps. This pixel-wise methodology also affects the forward modelling and inverse problem (imaging), based upon the diffusion approximation or Monte-Carlo methods due to their pixel-independent nature. NIRFAST, an existing FEM based NIR modelling tool, was adapted to produce pixel-dependent forward modelling for heterogenic samples, providing a mechanism towards a pixel dependent SFDI image modelling and parameter recovery system

Application-Dependent Wavelength Selection For Hyperspectral Imaging Technologies

Hyperspectral imaging has proven to provide benefits in numerous application domains, including agriculture, biomedicine, remote sensing, and food quality management. Unlike standard color imagery composed of these broad wavelength bands, hyperspectral images are collected over numerous (possibly hundreds) of narrow wavelength bands, thereby offering vastly more information content than standard imagery. It is this higher information content which enables improved performance in complex classification and regression tasks. However, this successful technology is not without its disadvantages which include high cost, slow data capture, high data storage requirements, and computational complexity. This research seeks to overcome these disadvantages through the development of algorithms and methods to enable the benefits of hyperspectral imaging in inexpensive portable devices that collect spectral data at only a handful (i.e., 5-7) of wavelengths specifically selected for the application of interest.This dissertation focuses on two applications of practical interest: fish fillet species classification for the prevention of food fraud and tissue oxygenation estimation for wound monitoring. Genetic algorithm, self-organizing map, and simulated annealing approaches for wavelength selection are investigated for the first application, combined with common machine learning classifiers for species classification. The simulated annealing approach for wavelength selection is carried over to the wound monitoring application and is combined with the Extended Modified Lambert-Beer law, a tissue oxygenation method that has proven to be robust to differences in melanin concentrations. Analyses for this second application included spectral convolutions to represent data collection with the envisioned inexpensive portable devices. Results of this research showed that high species classification accuracy (\u3e 90%) and low tissue oxygenation error (\u3c 1%) is achievable with just 5-7 selected wavelengths. Furthermore, the proposed wavelength selection and estimation algorithms for the wound monitoring application were found to be robust to variations in the peak wavelength and relatively wide bandwidths of the types of LEDs that may be featured in the designs of such devices

Machine learning in the prevention, diagnosis and management of diabetic foot ulcers: A systematic review

This is the final version. Available on open access from IEEE via the DOI in this record. Diabetic foot ulcers (DFUs) are a serious complication for people with diabetes. They result in increased morbidity and pressures on health system resources. Developments in machine learning (ML) offer an opportunity for improved care of individuals at risk of DFUs, to identify and synthesise evidence about the current uses and accuracy of ML in the interventional care and management of DFUs, and, to provide a reference for areas of future research. PubMed, Google Scholar, Web of Science and Scopus were searched using the Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies (PRISMA-DTA) guidelines for papers involving ML and DFUs. In order to be included, studies needed to mention ML, DFUs, and report relevant outcome measures regarding ML algorithm accuracy. Bias in included studies was assessed using the quality assessment tool for diagnostic accuracy (QUADAS2). 37 out of 3769 papers were included after applying eligibility criteria. Included papers reported accuracy measures for multiple types of ML algorithms in DFU studies. Whilst varying across the ML algorithm used, all studies reported at least 90% accuracy compared to gold standards using a minimum of one reported ML algorithm for processing or recording data. Applications where ML had positive effects on DFU data analysis and outcomes include image segmentation and classification, raw data analysis and risk assessment. ML offers an effective and accurate solution to guide analysis and procurement of data from interventions which are designed for the care of DFUs in small samples and study conditions. Current research is limited, and, for the development of more applicable ML algorithms, future research should address the following: direct comparison of ML applications with current standards of care, health economic analyses and large scale data collection. There is currently no evidence to confidently suggest that ML methods in DFU diagnosis are ready for implementation and use in healthcare settings

An intelligent telemedicine system for detection of diabetic foot complications

Early identification and timely treatment of diabetic foot complications are essential in preventing their devastating consequences such as lower-extremity amputation and mortality. Frequent and automatic risk assessment by an intelligent telemedicine system may be feasible and cost-effective. As the first step to approach such a telemedicine system, an experimental setup that combined three promising imaging modalities, namely spectral imaging, infrared thermal imaging, and photometric stereo imaging, was developed and investigated. \ud \ud The spectral imaging system in the experimental setup contains nine cameras in a matrix configuration, fitted with the preselected optical filters. Using the spectral images acquired, front-end pixel classifiers were developed to detect the diabetic foot complications automatically. Taking the image annotations based on live assessment as ground truth, the validation results indicate that these front-end classifiers can identify the diabetic foot complications with acceptable performance. However, future studies are needed on enhancing the performance of current pixel classifiers and designing the back-end classifiers.\ud \ud With the infrared thermal imaging, images of temperature distributions can be acquired from patients’ feet. The temperature differences between the corresponding areas of the contralateral feet are clinically significant parameters for identifying the diabetic foot complications. To detect this temperature differences automatically, an asymmetric analysis were proposed and investigated. Results show that the corresponding points on the two feet can be identified irrespective of the shapes, sizes or poses of the feet. \ud \ud With the photometric stereo imaging, a feasibility study were conducted to detect diabetic foot complications with the 3D surface reconstruction. The results indicate that this imaging technology may be promising but subjected to some limitations currently, such as the movement in patients' foot during image acquisition. To determine the potential value of this modality in the future telemedicine system, further improvement is required.\ud \ud The outcomes of the studies presented in this thesis showed the feasibility of developing a telemedicine system to detect diabetic foot complications with the three imaging modalities. The studies acted as the precursors for developing an intelligent telemedicine system, which proposed potential detection methodologies and provided the directions for the future study

Implementation of Thermal and Spectral Image Analysis for Neuropathic Foot

Diabetes is a grave metabolic disease described by high glucose levels. The feet of pa-tients with diabetes are at the danger of a variety of neurotic results including peripheral vascular infection, disfigurement, ulceration, and necrosis (infection caused by localized death of living cells or tissue) leading to amputation. The way to deal with the diabetic foot is anticipation and early location. Sadly, currently health provider’s focus on re-sponsive diabetes mind and the accessibility of lacking subjective demonstrative screen-ing methodology makes doctors miss the finding of a few patients. The main objective is that diabetic foot demonstrates basic neuropathic and vascular symptoms. When a foot patient is inactive, the thermal recuperation will be much slow-er. This thermal response speed can be used as a quantitative measure for the study of diabetic foot condition. In our study, thermal recovery of the foot following cold pressure is discovered using a thermal camera. The captured thermal image is then analysed, and the temperature re-covery at each point on the foot is extracted and calibrated using a thermal control ap-pears, and the precarious regions are recognized. In addition, LED-based spectral imag-ing is tested to estimate oxygen saturation in the foot. In this subject, we show our examinations on the following parts of the implementation of medical application analytic system based on: measurement protocols, thermal image segmentation, new techniques to perform model analysis of gathered images, and our preliminary discoveries focused on small scale clinical investigation of some patients, which demonstrate the potential of the diagnostic system.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format

High Performance Functional Bio-based Polymers for Skin-contact Products

Beauty masks, diapers, wound dressings, wipes, protective clothes and biomedical products: all these high-value and/or large-volume products must be highly compatible with human skin and they should have specific functional properties, such as anti-microbial, anti-inflammatory and anti-oxidant properties. They are currently partially or totally produced using fossil-based sources, with evident issues linked to their end of life, as their waste generates an increasing environmental concern. On the contrary, biopolymers and active biomolecules from biobased sources could be used to produce new materials that are highly compatible with the skin and also biodegradable. The final products can be obtained by exploiting safe and smart nanotechnologies such as the extrusion of bionanocomposites and electrospinning/electrospray, as well as innovative surface modification and control methodologies. For all these reasons, recently, many researchers, such as those involved in the European POLYBIOSKIN project activities, have been working in the field of biomaterials with anti-microbial, anti-inflammatory and anti-oxidant properties, as well as biobased materials which are renewable and biodegradable. The present book gathered research and review papers dedicated to materials and technologies for high-performance products where the attention paid to health and environmental impact is efficiently integrated, considering both the skin-compatibility of the selected materials and their source/end of life

Unveiling the role of artificial intelligence for wound assessment and wound healing prediction

Wound healing is a very dynamic and complex process as it involves the patient, wound-level parameters, as well as biological, environmental, and socioeconomic factors. Its process includes hemostasis, inflammation, proliferation, and remodeling. Evaluation of wound components such as angiogenesis, inflammation, restoration of connective tissue matrix, wound contraction, remodeling, and re-epithelization would detail the healing process. Understanding key mechanisms in the healing process is critical to wound research. Elucidating its healing complexity would enable control and optimize the processes for achieving faster healing, preventing wound complications, and undesired outcomes such as infection, periwound dermatitis and edema, hematomas, dehiscence, maceration, or scarring. Wound assessment is an essential step for selecting an appropriate treatment and evaluating the wound healing process. The use of artificial intelligence (AI) as advanced computer-assisted methods is promising for gaining insights into wound assessment and healing. As AI-based approaches have been explored for various applications in wound care and research, this paper provides an overview of recent studies exploring the application of AI and its technical developments and suitability for accurate wound assessment and prediction of wound healing. Several studies have been done across the globe, especially in North America, Europe, Oceania, and Asia. The results of these studies have shown that AI-based approaches are promising for wound assessment and prediction of wound healing. However, there are still some limitations and challenges that need to be addressed. This paper also discusses the challenges and limitations of AI-based approaches for wound assessment and prediction of wound healing. The paper concludes with a discussion of future research directions and recommendations for the use of AI-based approaches for wound assessment and prediction of wound healing

Microscopy and Analysis

Microscopes represent tools of the utmost importance for a wide range of disciplines. Without them, it would have been impossible to stand where we stand today in terms of understanding the structure and functions of organelles and cells, tissue composition and metabolism, or the causes behind various pathologies and their progression. Our knowledge on basic and advanced materials is also intimately intertwined to the realm of microscopy, and progress in key fields of micro- and nanotechnologies critically depends on high-resolution imaging systems. This volume includes a series of chapters that address highly significant scientific subjects from diverse areas of microscopy and analysis. Authoritative voices in their fields present in this volume their work or review recent trends, concepts, and applications, in a manner that is accessible to a broad readership audience from both within and outside their specialist area