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Biomaterials Science Jun 2024Keloid is a type of scar formed by the overexpression of extracellular matrix substances from fibroblasts following inflammation after trauma. The existing keloid... (Review)
Review
Keloid is a type of scar formed by the overexpression of extracellular matrix substances from fibroblasts following inflammation after trauma. The existing keloid treatment methods include drug injection, surgical intervention, light exposure, cryotherapy, However, these methods have limitations such as recurrence, low treatment efficacy, and side effects. Consequently, studies are being conducted on the treatment of keloids from the perspective of inflammatory mechanisms. In this study, keloid models are created to understand inflammatory mechanisms and explore treatment methods to address them. While previous studies have used animal models with gene mutations, chemical treatments, and keloid tissue transplantation, there are limitations in fully reproducing the characteristics of keloids unique to humans, and ethical issues related to animal welfare pose additional challenges. Consequently, studies are underway to create artificial skin models to simulate keloid disease and apply them to the development of treatments for skin diseases. In particular, herein, scaffold technologies that implement three-dimensional (3D) full-thickness keloid models are introduced to enhance mechanical properties as well as biological properties of tissues, such as cell proliferation, differentiation, and cellular interactions. It is anticipated that applying these technologies to the production of artificial skin for keloid simulation could contribute to the development of inflammatory keloid treatment techniques in the future.
Topics: Keloid; Humans; Skin, Artificial; Animals; Models, Biological; Tissue Engineering; Tissue Scaffolds; Skin
PubMed: 38812375
DOI: 10.1039/d4bm00005f -
Frontiers in Medicine 2023Skin cancer is one of the most common forms worldwide, with a significant increase in incidence over the last few decades. Early and accurate detection of this type of...
BACKGROUND
Skin cancer is one of the most common forms worldwide, with a significant increase in incidence over the last few decades. Early and accurate detection of this type of cancer can result in better prognoses and less invasive treatments for patients. With advances in Artificial Intelligence (AI), tools have emerged that can facilitate diagnosis and classify dermatological images, complementing traditional clinical assessments and being applicable where there is a shortage of specialists. Its adoption requires analysis of efficacy, safety, and ethical considerations, as well as considering the genetic and ethnic diversity of patients.
OBJECTIVE
The systematic review aims to examine research on the detection, classification, and assessment of skin cancer images in clinical settings.
METHODS
We conducted a systematic literature search on PubMed, Scopus, Embase, and Web of Science, encompassing studies published until April 4th, 2023. Study selection, data extraction, and critical appraisal were carried out by two independent reviewers. Results were subsequently presented through a narrative synthesis.
RESULTS
Through the search, 760 studies were identified in four databases, from which only 18 studies were selected, focusing on developing, implementing, and validating systems to detect, diagnose, and classify skin cancer in clinical settings. This review covers descriptive analysis, data scenarios, data processing and techniques, study results and perspectives, and physician diversity, accessibility, and participation.
CONCLUSION
The application of artificial intelligence in dermatology has the potential to revolutionize early detection of skin cancer. However, it is imperative to validate and collaborate with healthcare professionals to ensure its clinical effectiveness and safety.
PubMed: 38259845
DOI: 10.3389/fmed.2023.1305954 -
PLoS Neglected Tropical Diseases Aug 2023Deep learning, which is a part of a broader concept of artificial intelligence (AI) and/or machine learning has achieved remarkable success in vision tasks. While there...
BACKGROUND
Deep learning, which is a part of a broader concept of artificial intelligence (AI) and/or machine learning has achieved remarkable success in vision tasks. While there is growing interest in the use of this technology in diagnostic support for skin-related neglected tropical diseases (skin NTDs), there have been limited studies in this area and fewer focused on dark skin. In this study, we aimed to develop deep learning based AI models with clinical images we collected for five skin NTDs, namely, Buruli ulcer, leprosy, mycetoma, scabies, and yaws, to understand how diagnostic accuracy can or cannot be improved using different models and training patterns.
METHODOLOGY
This study used photographs collected prospectively in Côte d'Ivoire and Ghana through our ongoing studies with use of digital health tools for clinical data documentation and for teledermatology. Our dataset included a total of 1,709 images from 506 patients. Two convolutional neural networks, ResNet-50 and VGG-16 models were adopted to examine the performance of different deep learning architectures and validate their feasibility in diagnosis of the targeted skin NTDs.
PRINCIPAL FINDINGS
The two models were able to correctly predict over 70% of the diagnoses, and there was a consistent performance improvement with more training samples. The ResNet-50 model performed better than the VGG-16 model. A model trained with PCR confirmed cases of Buruli ulcer yielded 1-3% increase in prediction accuracy across all diseases, except, for mycetoma, over a model which training sets included unconfirmed cases.
CONCLUSIONS
Our approach was to have the deep learning model distinguish between multiple pathologies simultaneously-which is close to real-world practice. The more images used for training, the more accurate the diagnosis became. The percentages of correct diagnosis increased with PCR-positive cases of Buruli ulcer. This demonstrated that it may be better to input images from the more accurately diagnosed cases in the training models also for achieving better accuracy in the generated AI models. However, the increase was marginal which may be an indication that the accuracy of clinical diagnosis alone is reliable to an extent for Buruli ulcer. Diagnostic tests also have their flaws, and they are not always reliable. One hope for AI is that it will objectively resolve this gap between diagnostic tests and clinical diagnoses with the addition of another tool. While there are still challenges to be overcome, there is a potential for AI to address the unmet needs where access to medical care is limited, like for those affected by skin NTDs.
Topics: Humans; Artificial Intelligence; Buruli Ulcer; Pilot Projects; Deep Learning; Mycetoma; Skin Diseases; Neglected Diseases
PubMed: 37578966
DOI: 10.1371/journal.pntd.0011230 -
Clinics in Plastic Surgery Apr 2024Coverage of burn wounds is crucial to prevent sequalae including dehydration, wound infection, sepsis, shock, scarring, and contracture. To this end, numerous temporary... (Review)
Review
Coverage of burn wounds is crucial to prevent sequalae including dehydration, wound infection, sepsis, shock, scarring, and contracture. To this end, numerous temporary and permanent options for coverage of burn wounds have been described. Temporary options for burn coverage include synthetic dressings, allografts, and xenografts. Permanent burn coverage can be achieved through skin substitutes, cultured epithelial autograft, ReCell, amnion, and autografting. Here, we aim to summarize the available options for burn coverage, as well as important considerations that must be made when choosing the best reconstructive option for a particular patient.
Topics: Humans; Transplantation, Autologous; Autografts; Skin, Artificial; Transplantation, Homologous; Bandages; Skin Transplantation; Burns; Skin
PubMed: 38429047
DOI: 10.1016/j.cps.2023.12.001 -
IEEE Transactions on Haptics 2023When interacting with an object, we use kinesthetic and tactile information to create our perception of the object's properties and to prevent its slippage using grip...
When interacting with an object, we use kinesthetic and tactile information to create our perception of the object's properties and to prevent its slippage using grip force control. We previously showed that applying artificial skin-stretch together with, and in the same direction as, kinesthetic force increases the perceived stiffness. Here, we investigated the effect of the direction of the artificial stretch on stiffness perception and grip force control. We presented participants with kinesthetic force together with negative or positive artificial stretch, in the opposite or the same direction of the natural stretch due to the kinesthetic force, respectively. Our results showed that artificial skin-stretch in both directions augmented the perceived stiffness; however, the augmentation caused by the negative stretch was consistently lower than that caused by the positive stretch. Additionally, we proposed a computational model that predicts the perceptual effects based on the preferred directions of the stimulated mechanoreceptors. When examining the grip force, we found that participants applied higher grip forces during the interactions with positive skin-stretch in comparison to the negative skin-stretch, which is consistent with the perceptual results. These results may be useful in tactile technologies for wearable haptic devices, teleoperation, and robot-assisted surgery.
Topics: Humans; Touch Perception; Skin, Artificial; Touch; Hand Strength
PubMed: 37956003
DOI: 10.1109/TOH.2023.3332295 -
ACS Applied Materials & Interfaces Oct 2023Chameleons are famous for their quick color changing abilities, and it is commonly assumed that they do this for camouflage. However, recent reports revealed that...
Chameleons are famous for their quick color changing abilities, and it is commonly assumed that they do this for camouflage. However, recent reports revealed that chameleons also change color for body temperature regulation. Inspired by the structure of the panther chameleon's skin, a stripe-patterned poly(-isopropylacrylamide) (PNIPAM) and polyacrylamide (PAM) hydrogel film with a laminated structure is fabricated in this work; thus, both camouflage and thermoregulation can be achieved through controlling Vis and NIR light effectively. For the PNIPAM stripe, the upper layer is the native PNIPAM hydrogel and the lower layer is the carbon nanotube-composited PNIPAM hydrogel. Thus, the PNIPAM stripe is capable of reaching 28 °C at a low environmental temperature (12 °C) and a low radiation intensity (20 mW cm), while preventing the body temperature from rising by changing to white under a strong radiation intensity (100 mW cm). For the PAM stripe, the upper layer combines colloidal photonic crystals and displays a tunable structural color by stretching, and the lower layer is mixed with PNIPAM microgels for thermal regulation. Through the fabrication of multifunctional patterns, the film can achieve both dynamic structural color and thermoregulation by precisely controlling solar radiation absorption, scattering, and reflection. More importantly, in the stripe-patterned system, the shrinkage of the PNIPAM stripes can effectively trigger the elongation of the PAM stripe, which endows the structural color changing process to be self-powered completely. The performances show that the stripe-patterned film may have potential applications in intelligent coatings, especially in areas with large temperature differences during the day such as high plains.
Topics: Skin, Artificial; Hydrogels; Light; Temperature; Body Temperature Regulation
PubMed: 37787638
DOI: 10.1021/acsami.3c08872 -
The Journal of Investigative Dermatology Aug 2023Artificial intelligence algorithms to classify melanoma are dependent on their training data, which limits generalizability. The objective of this study was to compare...
Artificial intelligence algorithms to classify melanoma are dependent on their training data, which limits generalizability. The objective of this study was to compare the performance of an artificial intelligence model trained on a standard adult-predominant dermoscopic dataset before and after the addition of additional pediatric training images. The performances were compared using held-out adult and pediatric test sets of images. We trained two models: one (model A) on an adult-predominant dataset (37,662 images from the International Skin Imaging Collaboration) and the other (model A+P) on an additional 1,536 pediatric images. We compared performance between the two models on adult and pediatric held-out test images separately using the area under the receiver operating characteristic curve. We then used Gradient-weighted Class Activation Maps and background skin masking to understand the contributions of the lesion versus background skin to algorithm decision making. Adding images from a pediatric population with different epidemiological and visual patterns to current reference standard datasets improved algorithm performance on pediatric images without diminishing performance on adult images. This suggests a way that dermatologic artificial intelligence models can be made more generalizable. The presence of background skin was important to the pediatric-specific improvement seen between models. Our study highlights the importance of carefully curated and labeled data from diverse inputs to improve the generalizability of AI models for dermatology, in this case applied to dermoscopic images of adult and pediatric lesions to improve melanoma detection.
Topics: Adult; Humans; Child; Skin Neoplasms; Artificial Intelligence; Melanoma; Skin; Skin Diseases
PubMed: 36804150
DOI: 10.1016/j.jid.2022.08.058 -
Scientific Reports Nov 2023Skin Cancer (SC) is one of the most dangerous types of cancer and if not treated in time, it can threaten the patient's life. With early diagnosis of this disease,...
Skin Cancer (SC) is one of the most dangerous types of cancer and if not treated in time, it can threaten the patient's life. With early diagnosis of this disease, treatment methods can be used more effectively and the progression of the disease can be prevented. Machine Learning (ML) techniques can be utilized as a useful and efficient tool for SCD. So far, various methods for automatic SCD based on ML techniques have been presented; However, this research field still requires the application of optimal and efficient models to increase the accuracy of SCD. Therefore, in this article, a new method for SCD using a combination of optimization techniques and Artificial Neural Networks (ANNs) is presented. The proposed method includes four steps: pre-processing, segmentation, feature extraction, and classification. Image segmentation for identifying the lesion region is performed using a Kohonen neural network, where the identified region of interest (ROI) is enhanced using the Greedy Search Algorithm (GSA). The proposed method, uses a Convolutional Neural Network (CNN) for extracting features from ROIs. Also, to classify features, an ANN is used, and by the Improved Gray Wolf Optimization (IGWO) algorithm, the number of neurons and weight vector are adjusted. In this method, a probabilistic model is used to improve the convergence speed of the GWO algorithm. Based on the evaluation results, using the IGWO model to optimize the structure and weight vector of the ANN can be effective in increasing the diagnosis accuracy by at least 5%. The results of implementing the proposed method and comparing its performance with previous methods also show that this method can diagnose SC in the ISIC-2016 and ISIC-2017 databases with an average accuracy of 97.09 and 95.17%, respectively; which improves accuracy by at least 0.5% compared to other methods.
Topics: Humans; Skin Neoplasms; Skin; Neural Networks, Computer; Algorithms; Databases, Factual
PubMed: 37938553
DOI: 10.1038/s41598-023-45039-w -
Postepy Biochemii Sep 2023Malignant melanoma is a dangerous skin cancer, accounting for the majority of skin cancer-related deaths. Many patients with this cancer have the V600E mutation in the...
Malignant melanoma is a dangerous skin cancer, accounting for the majority of skin cancer-related deaths. Many patients with this cancer have the V600E mutation in the BRAF gene. This mutation causes constitutive activation of the MAPK/ERK signaling pathway, significantly contributing to the process of carcinogenesis. We discuss the drug design process on the example of a specific BRAF V600E inhibitor, vemurafenib. We begin with the most commonly used drug design methods. The second part of the article focuses on vemurafenib. We analyze the invention of this BRAF V600E inhibitor and its analogue as well as the course of three stages of clinical trials. Then we provide information about other popular drugs for malignant melanoma, i.e. dacarbazine, ipilimumab and dabrafenib, and about the advantages of therapy with the simultaneous use of two inhibitors. Finally, we briefly discuss the role of artificial intelligence in the future of drug design.
Topics: Humans; Vemurafenib; Antineoplastic Agents; Proto-Oncogene Proteins B-raf; Artificial Intelligence; Indoles; Sulfonamides; Melanoma; Skin Neoplasms; Protein Kinase Inhibitors; Mutation; Drug Resistance, Neoplasm; Melanoma, Cutaneous Malignant
PubMed: 38019740
DOI: 10.18388/pb.2021_498 -
Biomedicines Dec 2023It has increasingly been recognized that electrical currents play a pivotal role in cell migration and tissue repair, in a process named "galvanotaxis". In this review,... (Review)
Review
It has increasingly been recognized that electrical currents play a pivotal role in cell migration and tissue repair, in a process named "galvanotaxis". In this review, we summarize the current evidence supporting the potential benefits of electric stimulation (ES) in the physiology of peripheral nerve repair (PNR). Moreover, we discuss the potential of piezoelectric materials in this context. The use of these materials has deserved great attention, as the movement of the body or of the external environment can be used to power internally the electrical properties of devices used for providing ES or acting as sensory receptors in artificial skin (e-skin). The fact that organic materials sustain spontaneous degradation inside the body means their piezoelectric effect is limited in duration. In the case of PNR, this is not necessarily problematic, as ES is only required during the regeneration period. Arguably, piezoelectric materials have the potential to revolutionize PNR with new biomedical devices that range from scaffolds and nerve-guiding conduits to sensory or efferent components of e-skin. However, much remains to be learned regarding piezoelectric materials, their use in manufacturing of biomedical devices, and their sterilization process, to fine-tune their safe, effective, and predictable in vivo application.
PubMed: 38137416
DOI: 10.3390/biomedicines11123195