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Clinics in Dermatology Jun 2024Artificial Intelligence (AI) has evolved to become a significant force in various domains, including medicine. We explore the role of AI in pathology, with a specific...
Artificial Intelligence (AI) has evolved to become a significant force in various domains, including medicine. We explore the role of AI in pathology, with a specific focus on dermatopathology and neoplastic dermatopathology. AI, encompassing Machine Learning (ML) and Deep Learning (DL), has demonstrated its potential in tasks ranging from diagnostic applications on Whole Slide Imaging (WSI) to predictive and prognostic functions in skin pathology. In dermatopathology, studies have assessed AI's ability to identify skin lesions, classify melanomas, and improve diagnostic accuracy. Results indicate that AI, particularly Convolutional Neural Networks (CNNs), can outperform human pathologists in terms of sensitivity and specificity. Moreover, AI aids in predicting disease outcomes, identifying aggressive tumors, and differentiating between various skin conditions. Neoplastic dermatopathology showcases AI's prowess in classifying melanocytic lesions, discriminating between melanomas and nevi, and aiding dermatopathologists in making accurate diagnoses. Studies emphasize the reproducibility and diagnostic aid that AI provides, especially in challenging cases. In inflammatory and lymphoproliferative dermatopathology, limited research exists, but studies show attempts to use AI to differentiate conditions like Mycosis Fungoides and eczema. While some results are promising, further exploration is needed in these areas. We highlight the extraordinary interest AI has garnered in the scientific community and its potential to assist clinicians and pathologists. Despite the advancements, we have stress edthe importance of collaboration between medical professionals, computer scientists, bioinformaticians, and engineers to harness AI's benefits while acknowledging its limitations and risks. The integration of AI into dermatopathology holds great promise, positioning it as a valuable tool rather than as a replacement for human expertise.
PubMed: 38909860
DOI: 10.1016/j.clindermatol.2024.06.010 -
JMIR Medical Informatics Jun 2024Dermoscopy is a growing field that uses microscopy to allow dermatologists and primary care physicians to identify skin lesions. For a given skin lesion, a wide variety...
BACKGROUND
Dermoscopy is a growing field that uses microscopy to allow dermatologists and primary care physicians to identify skin lesions. For a given skin lesion, a wide variety of differential diagnoses exist, which may be challenging for inexperienced users to name and understand.
OBJECTIVE
In this study, we describe the creation of the dermoscopy differential diagnosis explorer (D3X), an ontology linking dermoscopic patterns to differential diagnoses.
METHODS
Existing ontologies that were incorporated into D3X include the elements of visuals ontology and dermoscopy elements of visuals ontology, which connect visual features to dermoscopic patterns. A list of differential diagnoses for each pattern was generated from the literature and in consultation with domain experts. Open-source images were incorporated from DermNet, Dermoscopedia, and open-access research papers.
RESULTS
D3X was encoded in the OWL 2 web ontology language and includes 3041 logical axioms, 1519 classes, 103 object properties, and 20 data properties. We compared D3X with publicly available ontologies in the dermatology domain using a semiotic theory-driven metric to measure the innate qualities of D3X with others. The results indicate that D3X is adequately comparable with other ontologies of the dermatology domain.
CONCLUSIONS
The D3X ontology is a resource that can link and integrate dermoscopic differential diagnoses and supplementary information with existing ontology-based resources. Future directions include developing a web application based on D3X for dermoscopy education and clinical practice.
PubMed: 38904996
DOI: 10.2196/49613 -
Frontiers in Microbiology 2024The concept of a sterile uterus was challenged by recent studies that have described the microbiome of the virgin and pregnant uterus for species including humans and...
INTRODUCTION
The concept of a sterile uterus was challenged by recent studies that have described the microbiome of the virgin and pregnant uterus for species including humans and cattle. We designed two studies that tested whether the microbiome is introduced into the uterus when the virgin heifer is first inseminated and whether the origin of the microbiome is the vagina/cervix.
METHODS
The uterine microbiome was measured immediately before and after an artificial insemination (AI; Study 1; = 7 AI and = 6 control) and 14 d after insemination (Study 2; = 12 AI and = 12 control) in AI and non-AI (control) Holstein heifers. A third study (Study 3; = 5 Holstein heifers) that included additional negative controls was subsequently conducted to support the presence of a unique microbiome within the uterus despite the low microbial biomass and regardless of insemination. Traditional bacteriological culture was performed in addition to 16S rRNA gene sequencing on the same samples to determine whether there were viable organisms in addition to those detected based on DNA sequencing (16S rRNA gene sequence).
RESULTS AND DISCUSSION
Inseminating a heifer did not lead to a large change in the microbiome when assessed by traditional methods of bacterial culture or metataxonomic (16S rRNA gene) sequencing (results of Studies 1 and 2). Very few bacteria were cultured from the body or horn of the uterus regardless of whether an AI was or was not (negative control) performed. The cultured bacterial genera (e.g., , and ) were typical of those found in the soil, environment, skin, mucous membranes, and urogenital tract of animals. Metataxonomic sequencing of 16S rRNA gene generated a large number of amplicon sequence variants (ASV), but these larger datasets that were based on DNA sequencing did not consistently demonstrate an effect of AI on the abundance of ASVs across all uterine locations compared with the external surface of the tract (e.g., perimetrium; positive control samples for environment contamination during slaughter and collection). Major genera identified by 16S rRNA gene sequencing overlapped with those identified with bacterial culture and included , and .
PubMed: 38903779
DOI: 10.3389/fmicb.2024.1385505 -
Frontiers in Chemistry 2024This work provides a brief comparative analysis of the influence of heat creation on micropolar blood-based unsteady magnetised hybrid nanofluid flow over a curved...
This work provides a brief comparative analysis of the influence of heat creation on micropolar blood-based unsteady magnetised hybrid nanofluid flow over a curved surface. The Powell-Eyring fluid model was applied for modelling purposes, and this work accounted for the impacts of both viscous dissipation and Joule heating. By investigating the behaviours of Ag and TiO nanoparticles dispersed in blood, we aimed to understand the intricate phenomenon of hybridisation. A mathematical framework was created in accordance with the fundamental flow assumptions to build the model. Then, the model was made dimensionless using similarity transformations. The problem of a dimensionless system was then effectively addressed using the homotopy analysis technique. A cylindrical surface was used to calculate the flow quantities, and the outcomes were visualised using graphs and tables. Additionally, a study was conducted to evaluate skin friction and heat transfer in relation to blood flow dynamics; heat transmission was enhanced to raise the Biot number values. According to the findings of this study, increasing the values of the unstable parameters results in increase of the blood velocity profile.
PubMed: 38903202
DOI: 10.3389/fchem.2024.1397066 -
Science Advances Jun 2024Skin-like soft optical metamaterials with broadband modulation have been long pursued for practical applications, such as cloaking and camouflage. Here, we propose a...
Skin-like soft optical metamaterials with broadband modulation have been long pursued for practical applications, such as cloaking and camouflage. Here, we propose a skin-like metamaterial for dual-band camouflage based on unique Au nanoparticles assembled hollow pillars (NPAHP), which are implemented by the bottom-up template-assisted self-assembly processes. This dual-band camouflage realizes simultaneously high visible absorptivity (~0.947) and low infrared emissivity (~0.074/0.045 for mid-/long-wavelength infrared bands), ideal for visible and infrared dual-band camouflage at night or in outer space. In addition, this self-assembled metamaterial, with a micrometer thickness and periodic through-holes, demonstrates superior skin-like attachability and permeability, allowing close attachment to a wide range of surfaces including the human body. Last but not least, benefiting from the extremely low infrared emissivity, the skin-like metamaterial exhibits excellent high-temperature camouflage performance, with radiation temperature reduction from 678 to 353 kelvin. This work provides a new paradigm for skin-like metamaterials with flexible multiband modulation for multiple application scenarios.
PubMed: 38896621
DOI: 10.1126/sciadv.adl1896 -
Diagnostics (Basel, Switzerland) May 2024This survey represents the first endeavor to assess the clarity of the dermoscopic language by a chatbot, unveiling insights into the interplay between dermatologists...
This survey represents the first endeavor to assess the clarity of the dermoscopic language by a chatbot, unveiling insights into the interplay between dermatologists and AI systems within the complexity of the dermoscopic language. Given the complex, descriptive, and metaphorical aspects of the dermoscopic language, subjective interpretations often emerge. The survey evaluated the completeness and diagnostic efficacy of chatbot-generated reports, focusing on their role in facilitating accurate diagnoses and educational opportunities for novice dermatologists. A total of 30 participants were presented with hypothetical dermoscopic descriptions of skin lesions, including dermoscopic descriptions of skin cancers such as BCC, SCC, and melanoma, skin cancer mimickers such as actinic and seborrheic keratosis, dermatofibroma, and atypical nevus, and inflammatory dermatosis such as psoriasis and alopecia areata. Each description was accompanied by specific clinical information, and the participants were tasked with assessing the differential diagnosis list generated by the AI chatbot in its initial response. In each scenario, the chatbot generated an extensive list of potential differential diagnoses, exhibiting lower performance in cases of SCC and inflammatory dermatoses, albeit without statistical significance, suggesting that the participants were equally satisfied with the responses provided. Scores decreased notably when practical descriptions of dermoscopic signs were provided. Answers to BCC scenario scores in the diagnosis category (2.9 ± 0.4) were higher than those with SCC (2.6 ± 0.66, = 0.005) and inflammatory dermatoses (2.6 ± 0.67, = 0). Similarly, in the teaching tool usefulness category, BCC-based chatbot differential diagnosis received higher scores (2.9 ± 0.4) compared to SCC (2.6 ± 0.67, = 0.001) and inflammatory dermatoses (2.4 ± 0.81, = 0). The abovementioned results underscore dermatologists' familiarity with BCC dermoscopic images while highlighting the challenges associated with interpreting rigorous dermoscopic images. Moreover, by incorporating patient characteristics such as age, phototype, or immune state, the differential diagnosis list in each case was customized to include lesion types appropriate for each category, illustrating the AI's flexibility in evaluating diagnoses and highlighting its value as a resource for dermatologists.
PubMed: 38893694
DOI: 10.3390/diagnostics14111165 -
Diagnostics (Basel, Switzerland) May 2024The diagnosis and identification of melanoma are not always accurate, even for experienced dermatologists. Histopathology continues to be the gold standard, assessing...
The diagnosis and identification of melanoma are not always accurate, even for experienced dermatologists. Histopathology continues to be the gold standard, assessing specific parameters such as the Breslow index. However, it remains invasive and may lack effectiveness. Therefore, leveraging mathematical modeling and informatics has been a pursuit of diagnostic methods favoring early detection. Fractality, a mathematical parameter quantifying complexity and irregularity, has proven useful in melanoma diagnosis. Nonetheless, no studies have implemented this metric to feed artificial intelligence algorithms for the automatic classification of dermatological lesions, including melanoma. Hence, this study aimed to determine the combined utility of fractal dimension and unsupervised low-computational-requirements machine learning models in classifying melanoma and non-melanoma lesions. We analyzed 39,270 dermatological lesions obtained from the International Skin Imaging Collaboration. Box-counting fractal dimensions were calculated for these lesions. Fractal values were used to implement classification methods by unsupervised machine learning based on principal component analysis and iterated K-means (100 iterations). A clear separation was observed, using only fractal dimension values, between benign or malignant lesions (sensibility 72.4% and specificity 50.1%) and melanoma or non-melanoma lesions (sensibility 72.8% and specificity 50%) and subsequently, the classification quality based on the machine learning model was ≈80% for both benign and malignant or melanoma and non-melanoma lesions. However, the grouping of metastatic melanoma versus non-metastatic melanoma was less effective, probably due to the small sample size included in MM lesions. Nevertheless, we could suggest a decision algorithm based on fractal dimension for dermatological lesion discrimination. On the other hand, it was also determined that the fractal dimension is sufficient to generate unsupervised artificial intelligence models that allow for a more efficient classification of dermatological lesions.
PubMed: 38893659
DOI: 10.3390/diagnostics14111132 -
International Journal of Molecular... May 2024Nanotechnology is revolutionizing fields of high social and economic impact. such as human health preservation, energy conversion and storage, environmental... (Review)
Review
Nanotechnology is revolutionizing fields of high social and economic impact. such as human health preservation, energy conversion and storage, environmental decontamination, and art restoration. However, the possible global-scale application of nanomaterials is raising increasing concerns, mostly related to the possible toxicity of materials at the nanoscale. The possibility of using nanomaterials in cosmetics, and hence in products aimed to be applied directly to the human body, even just externally, is strongly debated. Preoccupation arises especially from the consideration that nanomaterials are mostly of synthetic origin, and hence are often seen as "artificial" and their effects as unpredictable. Melanin, in this framework, is a unique material since in nature it plays important roles that specific cosmetics are aimed to cover, such as photoprotection and hair and skin coloration. Moreover, melanin is mostly present in nature in the form of nanoparticles, as is clearly observable in the ink of some animals, like cuttlefish. Moreover, artificial melanin nanoparticles share the same high biocompatibility of the natural ones and the same unique chemical and photochemical properties. Melanin is hence a natural nanocosmetic agent, but its actual application in cosmetics is still under development, also because of regulatory issues. Here, we critically discuss the most recent examples of the application of natural and biomimetic melanin to cosmetics and highlight the requirements and future steps that would improve melanin-based cosmetics in the view of future applications in the everyday market.
Topics: Melanins; Humans; Hair Color; Animals; Cosmetics; Nanoparticles; Skin Pigmentation; Nanostructures; Nanotechnology
PubMed: 38892049
DOI: 10.3390/ijms25115862 -
BMC Medical Imaging Jun 2024Leishmaniasis is a vector-born neglected parasitic disease belonging to the genus Leishmania. Out of the 30 Leishmania species, 21 species cause human infection that...
BACKGROUND
Leishmaniasis is a vector-born neglected parasitic disease belonging to the genus Leishmania. Out of the 30 Leishmania species, 21 species cause human infection that affect the skin and the internal organs. Around, 700,000 to 1,000,000 of the newly infected cases and 26,000 to 65,000 deaths are reported worldwide annually. The disease exhibits three clinical presentations, namely, the cutaneous, muco-cutaneous and visceral Leishmaniasis which affects the skin, mucosal membrane and the internal organs, respectively. The relapsing behavior of the disease limits its diagnosis and treatment efficiency. The common diagnostic approaches follow subjective, error-prone, repetitive processes. Despite, an ever pressing need for an accurate detection of Leishmaniasis, the research conducted so far is scarce. In this regard, the main aim of the current research is to develop an artificial intelligence based detection tool for the Leishmaniasis from the Geimsa-stained microscopic images using deep learning method.
METHODS
Stained microscopic images were acquired locally and labeled by experts. The images were augmented using different methods to prevent overfitting and improve the generalizability of the system. Fine-tuned Faster RCNN, SSD, and YOLOV5 models were used for object detection. Mean average precision (MAP), precision, and Recall were calculated to evaluate and compare the performance of the models.
RESULTS
The fine-tuned YOLOV5 outperformed the other models such as Faster RCNN and SSD, with the MAP scores, of 73%, 54% and 57%, respectively.
CONCLUSION
The currently developed YOLOV5 model can be tested in the clinics to assist the laboratorists in diagnosing Leishmaniasis from the microscopic images. Particularly, in low-resourced healthcare facilities, with fewer qualified medical professionals or hematologists, our AI support system can assist in reducing the diagnosing time, workload, and misdiagnosis. Furthermore, the dataset collected by us will be shared with other researchers who seek to improve upon the detection system of the parasite. The current model detects the parasites even in the presence of the monocyte cells, but sometimes, the accuracy decreases due to the differences in the sizes of the parasite cells alongside the blood cells. The incorporation of cascaded networks in future and the quantification of the parasite load, shall overcome the limitations of the currently developed system.
Topics: Deep Learning; Humans; Microscopy; Azure Stains; Leishmaniasis; Leishmania
PubMed: 38890604
DOI: 10.1186/s12880-024-01333-1 -
Skin Research and Technology : Official... Jun 2024
Topics: Humans; Artificial Intelligence; Search Engine; Dermatitis
PubMed: 38887862
DOI: 10.1111/srt.13725