Autism spectrum disorder detection using facial images: A performance comparison of pretrained convolutional neural networks.

Autism spectrum disorder (ASD) is a complex psychological syndrome characterized by persistent difficulties in social interaction, restricted behaviours, speech, and nonverbal communication. The impacts of this disorder and the severity of symptoms vary from person to person. In most cases, symptoms of ASD appear at the age of 2 to 5 and continue throughout adolescence and into adulthood. While this disorder cannot be cured completely, studies have shown that early detection of this syndrome can assist in maintaining the behavioural and psychological development of children. Experts are currently studying various machine learning methods, particularly convolutional neural networks, to expedite the screening process. Convolutional neural networks are considered promising frameworks for the diagnosis of ASD. This study employs different pre-trained convolutional neural networks such as ResNet34, ResNet50, AlexNet, MobileNetV2, VGG16, and VGG19 to diagnose ASD and compared their performance. Transfer learning was applied to every model included in the study to achieve higher results than the initial models. The proposed ResNet50 model achieved the highest accuracy, 92%, compared to other transfer learning models. The proposed method also outperformed the state-of-the-art models in terms of accuracy and computational cost.

A General Bayesian Functional Spatial Partitioning Method for Multiple Region Discovery Applied to Prostate Cancer MRI.

Current protocols to estimate the number, size, and location of cancerous lesions in the prostate using multiparametric magnetic resonance imaging (mpMRI) are highly dependent on reader experience and expertise. Automatic voxel-wise cancer classifiers do not directly provide estimates of number, location, and size of cancerous lesions that are clinically important. Existing spatial partitioning methods estimate linear or piecewise-linear boundaries separating regions of local stationarity in spatially registered data and are inadequate for the application of lesion detection. Frequentist segmentation and clustering methods often require pre-specification of the number of clusters and do not quantify uncertainty. Previously, we developed a novel Bayesian functional spatial partitioning method to estimate the boundary surrounding a single cancerous lesion using data derived from mpMRI. We propose a Bayesian functional spatial partitioning method for multiple lesion detection with an unknown number of lesions. Our method utilizes functional estimation to model the smooth boundary curves surrounding each cancerous lesion. In a Reversible Jump Markov Chain Monte Carlo (RJ-MCMC) framework, we develop novel jump steps to jointly estimate and quantify uncertainty in the number of lesions, their boundaries, and the spatial parameters in each lesion. Through simulation we show that our method is robust to the shape of the lesions, number of lesions, and region-specific spatial processes. We illustrate our method through the detection of prostate cancer lesions using MRI.

Advancements in Nanoporous Materials for Biomedical Imaging and Diagnostics.

This review explores the latest advancements in nanoporous materials and their applications in biomedical imaging and diagnostics. Nanoporous materials possess unique structural features, including high surface area, tunable pore size, and versatile surface chemistry, making them highly promising platforms for a range of biomedical applications. This review begins by providing an overview of the various types of nanoporous materials, including mesoporous silica nanoparticles, metal-organic frameworks, carbon-based materials, and nanoporous gold. The synthesis method for each material, their current research trends, and prospects are discussed in detail. Furthermore, this review delves into the functionalization and surface modification techniques employed to tailor nanoporous materials for specific biomedical imaging applications. This section covers chemical functionalization, bioconjugation strategies, and surface coating and encapsulation methods. Additionally, this review examines the diverse biomedical imaging techniques enabled by nanoporous materials, such as fluorescence imaging, magnetic resonance imaging (MRI), computed tomography (CT) imaging, ultrasound imaging, and multimodal imaging. The mechanisms underlying these imaging techniques, their diagnostic applications, and their efficacy in clinical settings are thoroughly explored. Through an extensive analysis of recent research findings and emerging trends, this review underscores the transformative potential of nanoporous materials in advancing biomedical imaging and diagnostics. The integration of interdisciplinary approaches, innovative synthesis techniques, and functionalization strategies offers promising avenues for the development of next-generation imaging agents and diagnostic tools with enhanced sensitivity, specificity, and biocompatibility.

A teaching model for biomedical imaging informatics.

INTRODUCTION: Radiology played a leading role in the transformation of medicine to a digital environment. To ensure the smooth operation and managing workflow in a digital imaging environment, dedicated, well-trained individuals are needed. The objective of this study was to develop a teaching and learning model for imaging informatics. METHODS: Quantitative and qualitative data were collected through a literature review and three structured questionnaires. Results from literature and questionnaires informed the Delphi statements. Three Delphi rounds with medical informatics and higher education experts were completed - all data contributed to developing the teaching and learning model. RESULTS: Literature provided the frame of reference related to regulation and inclusions in the model. Six summated imaging informatics themes with categories that included topics, teaching, learning and assessment as well as project management, and clinical engineering were included in the first Delphi questionnaire. The three-round Delphi resulted in consensus achieved for 142 of the 184 statements and the stability of 37 statements. The model created aligns with the context, goals, content, learning experiences and assessment that lead to holistic student development. CONCLUSION: Feedback from a variety of sources assisted the development of the teaching model for image informatics. The model can be regarded as having a broad scope but also depth since expert refinement strengthened the final inclusions. The flexible, holistic nature of this model addresses not only the educational impediments associated with curriculum development but additionally catalyses a pragmatic approach to implementation and operationalisation thereof. IMPLICATIONS FOR PRACTICE: The developed teaching and learning model could serve to improve the training of radiographers and IT specialists to become certified imaging informatics professionals. This model may be incorporated to assist the integration of all systems - to improve quality and service.

Stretching the Limits of MRI-Stretchable and Modular Coil Array Using Conductive Thread Technology.

Objective: We propose a modular stretchable coil design using conductive threads and commercially available embroidery machines. The coil design increases customizability of coil arrays for individual patients and each body part. Methods: Eight rectangular coils were constructed with custom-fabricated stretchable tinsel copper threads incorporated onto textile. Tune, match, and detune circuits were incorporated on the coil. A hook-and-loop mechanism was used to attach and decouple the modular coils. Phantom and in vivo scans at various anatomical flexion angles were acquired to highlight performance, and a temperature test was performed to verify safety. Results: In vivo MRI experiments demonstrate high sensitivity and coverage of each anatomy. As the coils are stretched, the sensitive volume increases at a rate of 10.93 mL/cm2. The SNR reduction of a single coil was greater during compression than when stretched, but this did not affect image quality for the array. The modularity of the array allows for adaptability for any anatomy with simple on-demand adjustment to the number and position of coil elements. Conclusion: The images demonstrated high sensitivity and coverage of the stretchable array for various anatomies and flexion angles. Stretching the coils increases the sensitive volume, allowing for a larger region to be effectively imaged. The resonance shift and SNR decrease during stretch and compression support further investigation of methods to reduce frequency shift in stretchable coils. Significance: The proposed array design allows for highly stretchable, flexible, modular, and conformal patient-centered coils that allow for increased imaging quality, greater comfort, and rapid production.

CNN-based glioma detection in MRI: A deep learning approach.

BACKGROUND: More than a million people are affected by brain tumors each year; high-grade gliomas (HGGs) and low-grade gliomas (LGGs) present serious diagnostic and treatment hurdles, resulting in shortened life expectancies. Glioma segmentation is still a significant difficulty in clinical settings, despite improvements in Magnetic Resonance Imaging (MRI) and diagnostic tools. Convolutional neural networks (CNNs) have seen recent advancements that offer promise for increasing segmentation accuracy, addressing the pressing need for improved diagnostic and therapeutic approaches. OBJECTIVE: The study intended to develop an automated glioma segmentation algorithm using CNN to accurately identify tumor components in MRI images. The goal was to match the accuracy of experienced radiologists with commercial instruments, hence improving diagnostic precision and quantification. METHODS: 285 MRI scans of high-grade gliomas (HGGs) and low-grade gliomas (LGGs) were analyzed in the study. T1-weighted sequences were utilised for segmentation both pre-and post-contrast agent administration, along with T2-weighted sequences (with and without Fluid Attenuation by Inversion Recovery [FAIRE]). The segmentation performance was assessed with a U-Net network, renowned for its efficacy in medical image segmentation. DICE coefficients were computed for the tumour core with contrast enhancement, the entire tumour, and the tumour nucleus without contrast enhancement. RESULTS: The U-Net network produced DICE values of 0.7331 for the tumour core with contrast enhancement, 0.8624 for the total tumour, and 0.7267 for the tumour nucleus without contrast enhancement. The results align with previous studies, demonstrating segmentation accuracy on par with professional radiologists and commercially accessible segmentation tools. CONCLUSION: The study developed a CNN-based automated segmentation system for gliomas, achieving high accuracy in recognising glioma components in MRI images. The results confirm the ability of CNNs to enhance the accuracy of brain tumour diagnoses, suggesting a promising avenue for future research in medical imaging and diagnostics. This advancement is expected to improve diagnostic processes for clinicians and patients by providing more precise and quantitative results.

Numerical Study towards In Vivo Tracking of Micro-/Nanoplastic Based on X-ray Fluorescence Imaging.

There is a rising awareness of the toxicity of micro- and nanoplastics (MNPs); however, fundamental precise information on MNP-biodistribution in organisms is currently not available. X-ray fluorescence imaging (XFI) is introduced as a promising imaging modality to elucidate the effective MNP bioavailability and is expected to enable exact measurements on the uptake over the physical barriers of the organism and bioaccumulation in different organs. This is possible because of the ability of XFI to perform quantitative studies with a high spatial resolution and the possibility to conduct longitudinal studies. The focus of this work is a numerical study on the detection limits for a selected XFI-marker, here, palladium, to facilitate the design of future preclinical in vivo studies. Based on Monte Carlo simulations using a 3D voxel mouse model, the palladium detection thresholds in different organs under in vivo conditions in a mouse are estimated. The minimal Pd-mass in the scanning position at a reasonable significance level is determined to be <20 ng/mm2 for abdominal organs and <16 µg/mm2 for the brain. MNPs labelled with Pd and homogeneously distributed in the organ would be detectable down to a concentration of <1 µg/mL to <2.5 mg/mL in vivo. Long-term studies with a chronic MNP exposure in low concentrations are therefore possible such that XFI measurements could, in the future, contribute to MNP health risk assessment in small animals and humans.

BEATS: BEAmline for synchrotron X-ray microTomography at SESAME.

The ID10 beamline of the SESAME (Synchrotron-light for Experimental Science and Applications in the Middle East) synchrotron light source in Jordan was inaugurated in June 2023 and is now open to scientific users. The beamline, which was designed and installed within the European Horizon 2020 project BEAmline for Tomography at SESAME (BEATS), provides full-field X-ray radiography and microtomography imaging with monochromatic or polychromatic X-rays up to photon energies of 100 keV. The photon source generated by a 2.9 T wavelength shifter with variable gap, and a double-multilayer monochromator system allow versatile application for experiments requiring either an X-ray beam with high intensity and flux, and/or a partially spatial coherent beam for phase-contrast applications. Sample manipulation and X-ray detection systems are designed to allow scanning samples with different size, weight and material, providing image voxel sizes from 13 µm down to 0.33 µm. A state-of-the-art computing infrastructure for data collection, three-dimensional (3D) image reconstruction and data analysis allows the visualization and exploration of results online within a few seconds from the completion of a scan. Insights from 3D X-ray imaging are key to the investigation of specimens from archaeology and cultural heritage, biology and health sciences, materials science and engineering, earth, environmental sciences and more. Microtomography scans and preliminary results obtained at the beamline demonstrate that the new beamline ID10-BEATS expands significantly the range of scientific applications that can be targeted at SESAME.

Functional hydrogels for hepatocellular carcinoma: therapy, imaging, and in vitro model.

Hepatocellular carcinoma (HCC) is among the most common malignancies worldwide and is characterized by high rates of morbidity and mortality, posing a serious threat to human health. Interventional embolization therapy is the main treatment against middle- and late-stage liver cancer, but its efficacy is limited by the performance of embolism, hence the new embolic materials have provided hope to the inoperable patients. Especially, hydrogel materials with high embolization strength, appropriate viscosity, reliable security and multifunctionality are widely used as embolic materials, and can improve the efficacy of interventional therapy. In this review, we have described the status of research on hydrogels and challenges in the field of HCC therapy. First, various preparation methods of hydrogels through different cross-linking methods are introduced, then the functions of hydrogels related to HCC are summarized, including different HCC therapies, various imaging techniques, in vitro 3D models, and the shortcomings and prospects of the proposed applications are discussed in relation to HCC. We hope that this review is informative for readers interested in multifunctional hydrogels and will help researchers develop more novel embolic materials for interventional therapy of HCC.

Heteroatom Doping Promoted Ultrabright and Ultrastable Photoluminescence of Water-Soluble Au/Ag Nanoclusters for Visual and Efficient Drug Delivery to Cancer Cells.

Photoluminescence (PL) metal nanoclusters (NCs) have attracted extensive attention due to their excellent physicochemical properties, good biocompatibility, and broad application prospects. However, developing water-soluble PL metal NCs with a high quantum yield (QY) and high stability for visual drug delivery remains a great challenge. Herein, we have synthesized ultrabright l-Arg-ATT-Au/Ag NCs (Au/Ag NCs) with a PL QY as high as 73% and excellent photostability by heteroatom doping and surface rigidization in aqueous solution. The as-prepared Au/Ag NCs can maintain a high QY of over 61% in a wide pH range and various ionic environments as well as a respectable resistance to photobleaching. The results from structure characterization and steady-state and time-resolved spectroscopic analysis reveal that Ag doping into Au NCs not only effectively modifies the electronic structure and photostability but also significantly regulates the interfacial dynamics of the excited states and enhances the PL QY of Au/Ag NCs. Studies in vitro indicate Au/Ag NCs have a high loading capacity and pH-triggered release ability of doxorubicin (DOX) that can be visualized from the quenching and recovery of PL intensity and lifetime. Imaging-guided experiments in cancer cells show that DOX of Au/Ag NCs-DOX agents can be efficiently delivered and released in the nucleus with preferential accumulation in the nucleolus, facilitating deep insight into the drug action sites and pharmacological mechanisms. Moreover, the evaluation of anticancer activity in vivo reveals an outstanding suppression rate of 90.2% for mice tumors. These findings demonstrate Au/Ag NCs to be a superior platform for bioimaging and visual drug delivery in biomedical applications.

Nanoengineered Silica-Based Biomaterials for Regenerative Medicine.

The paradigm of regenerative medicine is undergoing a transformative shift with the emergence of nanoengineered silica-based biomaterials. Their unique confluence of biocompatibility, precisely tunable porosity, and the ability to modulate cellular behavior at the molecular level makes them highly desirable for diverse tissue repair and regeneration applications. Advancements in nanoengineered silica synthesis and functionalization techniques have yielded a new generation of versatile biomaterials with tailored functionalities for targeted drug delivery, biomimetic scaffolds, and integration with stem cell therapy. These functionalities hold the potential to optimize therapeutic efficacy, promote enhanced regeneration, and modulate stem cell behavior for improved regenerative outcomes. Furthermore, the unique properties of silica facilitate non-invasive diagnostics and treatment monitoring through advanced biomedical imaging techniques, enabling a more holistic approach to regenerative medicine. This review comprehensively examines the utilization of nanoengineered silica biomaterials for diverse applications in regenerative medicine. By critically appraising the fabrication and design strategies that govern engineered silica biomaterials, this review underscores their groundbreaking potential to bridge the gap between the vision of regenerative medicine and clinical reality.

Multi-detector fusion and Bayesian smoothing for tracking viral and chromatin structures.

Automatic tracking of viral and intracellular structures displayed as spots with varying sizes in fluorescence microscopy images is an important task to quantify cellular processes. We propose a novel probabilistic tracking approach for multiple particle tracking based on multi-detector and multi-scale data fusion as well as Bayesian smoothing. The approach integrates results from multiple detectors using a novel intensity-based covariance intersection method which takes into account information about the image intensities, positions, and uncertainties. The method ensures a consistent estimate of multiple fused particle detections and does not require an optimization step. Our probabilistic tracking approach performs data fusion of detections from classical and deep learning methods as well as exploits single-scale and multi-scale detections. In addition, we use Bayesian smoothing to fuse information of predictions from both past and future time points. We evaluated our approach using image data of the Particle Tracking Challenge and achieved state-of-the-art results or outperformed previous methods. Our method was also assessed on challenging live cell fluorescence microscopy image data of viral and cellular proteins expressed in hepatitis C virus-infected cells and chromatin structures in non-infected cells, acquired at different spatial-temporal resolutions. We found that the proposed approach outperforms existing methods.

Macroscopic inelastic scattering imaging using a hyperspectral line-scanning system identifies invasive breast cancer in lumpectomy and mastectomy specimens.

Significance: Of patients with early-stage breast cancer, 60% to 75% undergo breast-conserving surgery. Of those, 20% or more need a second surgery because of an incomplete tumor resection only discovered days after surgery. An intraoperative imaging technology allowing cancer detection on the margins of breast specimens could reduce re-excision procedure rates and improve patient survival. Aim: We aimed to develop an experimental protocol using hyperspectral line-scanning Raman spectroscopy to image fresh breast specimens from cancer patients. Our objective was to determine whether macroscopic specimen images could be produced to distinguish invasive breast cancer from normal tissue structures. Approach: A hyperspectral inelastic scattering imaging instrument was used to interrogate eight specimens from six patients undergoing breast cancer surgery. Machine learning models trained with a different system to distinguish cancer from normal breast structures were used to produce tissue maps with a field-of-view of 1 cm 2 classifying each pixel as either cancer, adipose, or other normal tissues. The predictive model results were compared with spatially correlated histology maps of the specimens. Results: A total of eight specimens from six patients were imaged. Four of the hyperspectral images were associated with specimens containing cancer cells that were correctly identified by the new ex vivo pathology technique. The images associated with the remaining four specimens had no histologically detectable cancer cells, and this was also correctly predicted by the instrument. Conclusions: We showed the potential of hyperspectral Raman imaging as an intraoperative breast cancer margin assessment technique that could help surgeons improve cosmesis and reduce the number of repeat procedures in breast cancer surgery.

Robust prostate disease classification using transformers with discrete representations.

PURPOSE: Automated prostate disease classification on multi-parametric MRI has recently shown promising results with the use of convolutional neural networks (CNNs). The vision transformer (ViT) is a convolutional free architecture which only exploits the self-attention mechanism and has surpassed CNNs in some natural imaging classification tasks. However, these models are not very robust to textural shifts in the input space. In MRI, we often have to deal with textural shift arising from varying acquisition protocols. Here, we focus on the ability of models to generalise well to new magnet strengths for MRI. METHOD: We propose a new framework to improve the robustness of vision transformer-based models for disease classification by constructing discrete representations of the data using vector quantisation. We sample a subset of the discrete representations to form the input into a transformer-based model. We use cross-attention in our transformer model to combine the discrete representations of T2-weighted and apparent diffusion coefficient (ADC) images. RESULTS: We analyse the robustness of our model by training on a 1.5 T scanner and test on a 3 T scanner and vice versa. Our approach achieves SOTA performance for classification of lesions on prostate MRI and outperforms various other CNN and transformer-based models in terms of robustness to domain shift and perturbations in the input space. CONCLUSION: We develop a method to improve the robustness of transformer-based disease classification of prostate lesions on MRI using discrete representations of the T2-weighted and ADC images.

Compressed Sensing for Biomedical Photoacoustic Imaging: A Review.

Photoacoustic imaging (PAI) is a rapidly developing emerging non-invasive biomedical imaging technique that combines the strong contrast from optical absorption imaging and the high resolution from acoustic imaging. Abnormal biological tissues (such as tumors and inflammation) generate different levels of thermal expansion after absorbing optical energy, producing distinct acoustic signals from normal tissues. This technique can detect small tissue lesions in biological tissues and has demonstrated significant potential for applications in tumor research, melanoma detection, and cardiovascular disease diagnosis. During the process of collecting photoacoustic signals in a PAI system, various factors can influence the signals, such as absorption, scattering, and attenuation in biological tissues. A single ultrasound transducer cannot provide sufficient information to reconstruct high-precision photoacoustic images. To obtain more accurate and clear image reconstruction results, PAI systems typically use a large number of ultrasound transducers to collect multi-channel signals from different angles and positions, thereby acquiring more information about the photoacoustic signals. Therefore, to reconstruct high-quality photoacoustic images, PAI systems require a significant number of measurement signals, which can result in substantial hardware and time costs. Compressed sensing is an algorithm that breaks through the Nyquist sampling theorem and can reconstruct the original signal with a small number of measurement signals. PAI based on compressed sensing has made breakthroughs over the past decade, enabling the reconstruction of low artifacts and high-quality images with a small number of photoacoustic measurement signals, improving time efficiency, and reducing hardware costs. This article provides a detailed introduction to PAI based on compressed sensing, such as the physical transmission model-based compressed sensing method, two-stage reconstruction-based compressed sensing method, and single-pixel camera-based compressed sensing method. Challenges and future perspectives of compressed sensing-based PAI are also discussed.

Svetlana a supervised segmentation classifier for Napari.

We present Svetlana (SuperVised sEgmenTation cLAssifier for NapAri), an open-source Napari plugin dedicated to the manual or automatic classification of segmentation results. A few recent software tools have made it possible to automatically segment complex 2D and 3D objects such as cells in biology with unrivaled performance. However, the subsequent analysis of the results is oftentimes inaccessible to non-specialists. The Svetlana plugin aims at going one step further, by allowing end-users to label the segmented objects and to pick, train and run arbitrary neural network classifiers. The resulting network can then be used for the quantitative analysis of biophysical phenoma. We showcase its performance through challenging problems in 2D and 3D and provide a comprehensive discussion on its strengths and limits.

Calibration-free structured-light-based 3D scanning system in laparoscope for robotic surgery.

Accurate 3D shape measurement is crucial for surgical support and alignment in robotic surgery systems. Stereo cameras in laparoscopes offer a potential solution; however, their accuracy in stereo image matching diminishes when the target image has few textures. Although stereo matching with deep learning has gained significant attention, supervised learning requires a large dataset of images with depth annotations, which are scarce for laparoscopes. Thus, there is a strong demand to explore alternative methods for depth reconstruction or annotation for laparoscopes. Active stereo techniques are a promising approach for achieving 3D reconstruction without textures. In this study, a 3D shape reconstruction method is proposed using an ultra-small patterned projector attached to a laparoscopic arm to address these issues. The pattern projector emits a structured light with a grid-like pattern that features node-wise modulation for positional encoding. To scan the target object, multiple images are taken while the projector is in motion, and the relative poses of the projector and a camera are auto-calibrated using a differential rendering technique. In the experiment, the proposed method is evaluated by performing 3D reconstruction using images obtained from a surgical robot and comparing the results with a ground-truth shape obtained from X-ray CT.

Proof-of-concept of a robotic-driven photogrammetric scanner for intra-operative knee cartilage repair.

This work presents a proof-of-concept of a robotic-driven intra-operative scanner designed for knee cartilage lesion repair, part of a system for direct in vivo bioprinting. The proposed system is based on a photogrammetric pipeline, which reconstructs the cartilage and lesion surfaces from sets of photographs acquired by a robotic-handled endoscope, and produces 3D grafts for further printing path planning. A validation on a synthetic phantom is presented, showing that, despite the cartilage smooth and featureless surface, the current prototype can accurately reconstruct osteochondral lesions and their surroundings with mean error values of 0.199 ± 0.096 mm but with noticeable concentration on areas with poor lighting or low photographic coverage. The system can also accurately generate grafts for bioprinting, although with a slight tendency to underestimate the actual lesion sizes, producing grafts with coverage errors of -12.2 ± 3.7, -7.9 ± 4.9, and -15.2 ± 3.4% for the medio-lateral, antero-posterior, and craneo-caudal directions, respectively. Improvements in lighting and acquisition for enhancing reconstruction accuracy are planned as future work, as well as integration into a complete bioprinting pipeline and validation with ex vivo phantoms.

Super-resolution techniques for biomedical applications and challenges.

Super-resolution (SR) techniques have revolutionized the field of biomedical applications by detailing the structures at resolutions beyond the limits of imaging or measuring tools. These techniques have been applied in various biomedical applications, including microscopy, magnetic resonance imaging (MRI), computed tomography (CT), X-ray, electroencephalogram (EEG), ultrasound, etc. SR methods are categorized into two main types: traditional non-learning-based methods and modern learning-based approaches. In both applications, SR methodologies have been effectively utilized on biomedical images, enhancing the visualization of complex biological structures. Additionally, these methods have been employed on biomedical data, leading to improvements in computational precision and efficiency for biomedical simulations. The use of SR techniques has resulted in more detailed and accurate analyses in diagnostics and research, essential for early disease detection and treatment planning. However, challenges such as computational demands, data interpretation complexities, and the lack of unified high-quality data persist. The article emphasizes these issues, underscoring the need for ongoing development in SR technologies to further improve biomedical research and patient care outcomes.

Biomimicking Atherosclerotic Vessels: A Relevant and (Yet) Sub-Explored Topic.

Atherosclerosis represents the etiologic source of several cardiovascular events, including myocardial infarction, cerebrovascular accidents, and peripheral artery disease, which remain the leading cause of mortality in the world. Numerous strategies are being delineated to revert the non-optimal projections of the World Health Organization, by both designing new diagnostic and therapeutic approaches or improving the interventional procedures performed by physicians. Deeply understanding the pathological process of atherosclerosis is, therefore, mandatory to accomplish improved results in these trials. Due to their availability, reproducibility, low expensiveness, and rapid production, biomimicking physical models are preferred over animal experimentation because they can overcome some limitations, mainly related to replicability and ethical issues. Their capability to represent any atherosclerotic stage and/or plaque type makes them valuable tools to investigate hemodynamical, pharmacodynamical, and biomechanical behaviors, as well as to optimize imaging systems and, thus, obtain meaningful prospects to improve the efficacy and effectiveness of treatment on a patient-specific basis. However, the broadness of possible applications in which these biomodels can be used is associated with a wide range of tissue-mimicking materials that are selected depending on the final purpose of the model and, consequently, prioritizing some materials' properties over others. This review aims to summarize the progress in fabricating biomimicking atherosclerotic models, mainly focusing on using materials according to the intended application.