Prediction model of alcohol intoxication from facial temperature dynamics based on K-means clustering driven by evolutionary computing

Alcohol intoxication is a significant phenomenon, affecting many social areas, including work procedures or car driving. Alcohol causes certain side effects including changing the facial thermal distribution, which may enable the contactless identification and classification of alcohol-intoxicated people. We adopted a multiregional segmentation procedure to identify and classify symmetrical facial features, which reliably reflects the facial-temperature variations while subjects are drinking alcohol. Such a model can objectively track alcohol intoxication in the form of a facial temperature map. In our paper, we propose the segmentation model based on the clustering algorithm, which is driven by the modified version of the Artificial Bee Colony (ABC) evolutionary optimization with the goal of facial temperature features extraction from the IR (infrared radiation) images. This model allows for a definition of symmetric clusters, identifying facial temperature structures corresponding with intoxication. The ABC algorithm serves as an optimization process for an optimal cluster's distribution to the clustering method the best approximate individual areas linked with gradual alcohol intoxication. In our analysis, we analyzed a set of twenty volunteers, who had IR images taken to reflect the process of alcohol intoxication. The proposed method was represented by multiregional segmentation, allowing for classification of the individual spatial temperature areas into segmentation classes. The proposed method, besides single IR image modelling, allows for dynamical tracking of the alcohol-temperature features within a process of intoxication, from the sober state up to the maximum observed intoxication level.Web of Science118art. no. 99

Breast cancer detection using infrared thermal imaging and a deep learning model

Women’s breasts are susceptible to developing cancer; this is supported by a recent study from 2016 showing that 2.8 million women worldwide had already been diagnosed with breast cancer that year. The medical care of a patient with breast cancer is costly and, given the cost and value of the preservation of the health of the citizen, the prevention of breast cancer has become a priority in public health. Over the past 20 years several techniques have been proposed for this purpose, such as mammography, which is frequently used for breast cancer diagnosis. However, false positives of mammography can occur in which the patient is diagnosed positive by another technique. Additionally, the potential side effects of using mammography may encourage patients and physicians to look for other diagnostic techniques. Our review of the literature first explored infrared digital imaging, which assumes that a basic thermal comparison between a healthy breast and a breast with cancer always shows an increase in thermal activity in the precancerous tissues and the areas surrounding developing breast cancer. Furthermore, through our research, we realized that a Computer-Aided Diagnostic (CAD) undertaken through infrared image processing could not be achieved without a model such as the well-known hemispheric model. The novel contribution of this paper is the production of a comparative study of several breast cancer detection techniques using powerful computer vision techniques and deep learning models

Radiogenomics Framework for Associating Medical Image Features with Tumour Genetic Characteristics

Significant progress has been made in the understanding of human cancers at the molecular genetics level and it is providing new insights into their underlying pathophysiology. This progress has enabled the subclassification of the disease and the development of targeted therapies that address specific biological pathways. However, obtaining genetic information remains invasive and costly. Medical imaging is a non-invasive technique that captures important visual characteristics (i.e. image features) of abnormalities and plays an important role in routine clinical practice. Advancements in computerised medical image analysis have enabled quantitative approaches to extract image features that can reflect tumour genetic characteristics, leading to the emergence of ‘radiogenomics’. Radiogenomics investigates the relationships between medical imaging features and tumour molecular characteristics, and enables the derivation of imaging surrogates (radiogenomics features) to genetic biomarkers that can provide alternative approaches to non-invasive and accurate cancer diagnosis. This thesis presents a new framework that combines several novel methods for radiogenomics analysis that associates medical image features with tumour genetic characteristics, with the main objectives being: i) a comprehensive characterisation of tumour image features that reflect underlying genetic information; ii) a method that identifies radiogenomics features encoding common pathophysiological information across different diseases, overcoming the dependence on large annotated datasets; and iii) a method that quantifies radiogenomics features from multi-modal imaging data and accounts for unique information encoded in tumour heterogeneity sub-regions. The present radiogenomics methods advance radiogenomics analysis and contribute to improving research in computerised medical image analysis

Micro/nanofluidic and lab-on-a-chip devices for biomedical applications

Micro/Nanofluidic and lab-on-a-chip devices have been increasingly used in biomedical research [1]. Because of their adaptability, feasibility, and cost-efficiency, these devices can revolutionize the future of preclinical technologies. Furthermore, they allow insights into the performance and toxic effects of responsive drug delivery nanocarriers to be obtained, which consequently allow the shortcomings of two/three-dimensional static cultures and animal testing to be overcome and help to reduce drug development costs and time [2–4]. With the constant advancements in biomedical technology, the development of enhanced microfluidic devices has accelerated, and numerous models have been reported. Given the multidisciplinary of this Special Issue (SI), papers on different subjects were published making a total of 14 contributions, 10 original research papers, and 4 review papers. The review paper of Ko et al. [1] provides a comprehensive overview of the significant advancements in engineered organ-on-a-chip research in a general way while in the review presented by Kanabekova and colleagues [2], a thorough analysis of microphysiological platforms used for modeling liver diseases can be found. To get a summary of the numerical models of microfluidic organ-on-a-chip devices developed in recent years, the review presented by Carvalho et al. [5] can be read. On the other hand, Maia et al. [6] report a systematic review of the diagnosis methods developed for COVID-19, providing an overview of the advancements made since the start of the pandemic. In the following, a brief summary of the research papers published in this SI will be presented, with organs-on-a-chip, microfluidic devices for detection, and device optimization having been identified as the main topics.info:eu-repo/semantics/publishedVersio

Proceedings of the Cardiff University Engineering Research Conference 2023

The conference was established for the first time in 2023 as part of a programme to sustain the research culture, environment, and dissemination activities of the School of Engineering at Cardiff University in the United Kingdom. The conference served as a platform to celebrate advancements in various engineering domains researched at our School, explore and discuss further advancements in the diverse fields that define contemporary engineering

The research on mechanical properties and compressive behavior of graphene foam with multi-scale model?

Computational simulation is an effective method to study the deformation mechanism and mechanical behaviour of graphene-based porous materials. However, due to limitations in computational methods and costs, existing research model deviate significantly from the real material in terms of the scale of structure. Therefore, building a highly accurate computational model and maintaining an appropriate cost is both necessary and challenging. This paper proposed a multi-scale modelling approach for finite element (FE) analysis based on the concept of structural hierarchy. The stochastic feature of the microstructure of porous materials are also considered. The simulation results of the regular structure model and the Voronoi tessellation model are compared to investigate the effect of regularity on the material properties. Despite some shortcomings, other microstructural features of porous graphene materials can be gradually introduced to improve the material model step by step. Thus the developed multiscale model has great potential to simulate the properties of materials with mesoscopic size structure such as graphene foam (GF)

Advanced imaging and artificial intelligence for diagnostic and prognostic biomarkers in glioblastoma

Conventional magnetic resonance imaging (MRI) has a pivotal role in diagnosis and post-treatment management of glioblastoma, however it has limitations. This work investigates the use of advanced MRI techniques that assess the tumour microenvironment, and artificial intelligence (AI) techniques that compute quantitative features, as potential imaging biomarkers in key clinical issues faced by clinicians, through several retrospective studies. Results show that advanced multiparametric MRI is superior to current standard-of-care imaging for the diagnosis of glioblastoma, and in treatment response assessment. Results of AI techniques on pre-operative imaging show the ability to differentiate between glioblastoma and metastasis with an accuracy of 88.7%, prediction of overall survival with a high level of accuracy, and stratification of patients into high- and low-level groups of MGMT promoter methylation with accuracies between 45-67%. In the early post-treatment phase, AI analysis of imaging can distinguish between disease progression and pseudoprogression with an accuracy of 73.7%, compared to neuroradiologist accuracy of 32.9%. Integrating these techniques into routine clinical practice is essential to improve patient outcomes. Further work is required to validate advanced imaging and AI biomarkers, towards the longer-term goal of using these as clinical decision support tools, to benefit patients with glioblastoma and other brain tumours

Radiolabelled Molecules for Brain Imaging with PET and SPECT

Positron emission tomography (PET) and single-photon emission computed tomography (SPECT) are in vivo molecular imaging methods which are widely used in nuclear medicine for diagnosis and treatment follow-up of many major diseases. These methods use target-specific molecules as probes, which are labeled with radionuclides of short half-lives that are synthesized prior to the imaging studies. These probes are called radiopharmaceuticals. The use of PET and SPECT for brain imaging is of special significance since the brain controls all the body’s functions by processing information from the whole body and the outside world. It is the source of thoughts, intelligence, memory, speech, creativity, emotion, sensory functions, motion control, and other important body functions. Protected by the skull and the blood–brain barrier, the brain is somehow a privileged organ with regard to nutrient supply, immune response, and accessibility for diagnostic and therapeutic measures. Invasive procedures are rather limited for the latter purposes. Therefore, noninvasive imaging with PET and SPECT has gained high importance for a great variety of brain diseases, including neurodegenerative diseases, motor dysfunctions, stroke, epilepsy, psychiatric diseases, and brain tumors. This Special Issue focuses on radiolabeled molecules that are used for these purposes, with special emphasis on neurodegenerative diseases and brain tumors