Artificial Intelligence in Multiphoton Tomography: Atopic Dermatitis Diagnosis

The Impact of Artificial Intelligence on Allergy Diagnosis and Treatment

PURPOSE OF REVIEW

Artificial intelligence (AI), be it neuronal networks, machine learning or deep learning, has numerous beneficial effects on healthcare systems; however, its potential applications and diagnostic capabilities for immunologic diseases have yet to be explored. Understanding AI systems can help healthcare workers better assimilate artificial intelligence into their practice and unravel its potential in diagnostics, clinical research, and disease management.

RECENT FINDINGS

We reviewed recent advancements in AI systems and their integration in healthcare systems, along with their potential benefits in the diagnosis and management of diseases. We explored machine learning as employed in allergy diagnosis and its learning patterns from patient datasets, as well as the possible advantages of using AI in the field of research related to allergic reactions and even remote monitoring. Considering the ethical challenges and privacy concerns raised by clinicians and patients with regard to integrating AI in healthcare, we explored the new guidelines adapted by regulatory bodies. Despite these challenges, AI appears to have been successfully incorporated into various healthcare systems and is providing patient-centered solutions while simultaneously assisting healthcare workers. Artificial intelligence offers new hope in the field of immunologic disease diagnosis, monitoring, and management and thus has the potential to revolutionize healthcare systems.

Artificial Intelligence: A Snapshot of Its Application in Chronic Inflammatory and Autoimmune Skin Diseases

Immuno-correlated dermatological pathologies refer to skin disorders that are closely associated with immune system dysfunction or abnormal immune responses. Advancements in the field of artificial intelligence (AI) have shown promise in enhancing the diagnosis, management, and assessment of immuno-correlated dermatological pathologies. This intersection of dermatology and immunology plays a pivotal role in comprehending and addressing complex skin disorders with immune system involvement. The paper explores the knowledge known so far and the evolution and achievements of AI in diagnosis; discusses segmentation and the classification of medical images; and reviews existing challenges, in immunological-related skin diseases. From our review, the role of AI has emerged, especially in the analysis of images for both diagnostic and severity assessment purposes. Furthermore, the possibility of predicting patients' response to therapies is emerging, in order to create tailored therapies.

Evaluation of atopic dermatitis severity using artificial intelligence

Atopic dermatitis is a prevalent and persistent chronic inflammatory skin disorder that poses significant challenges when it comes to accurately assessing its severity. The aim of this study was to evaluate deep learning models for automated atopic dermatitis severity scoring using a dataset of Aceh ethnicity individuals in Indonesia. The dataset of clinical images was collected from 250 patients at Dr. Zainoel Abidin Hospital, Banda Aceh, Indonesia and labeled by dermatologists as mild, moderate, severe, or none. Five pretrained convolutional neural networks (CNN) architectures were evaluated: ResNet50, VGGNet19, MobileNetV3, MnasNet, and EfficientNetB0. The evaluation metrics, including accuracy, precision, sensitivity, specificity, and F1-score, were employed to assess the models. Among the models, ResNet50 emerged as the most proficient, demonstrating an accuracy of 89.8%, precision of 90.00%, sensitivity of 89.80%, specificity of 96.60%, and an F1-score of 89.85%. These results highlight the potential of incorporating advanced, data-driven models into the field of dermatology. These models can serve as invaluable tools to assist dermatologists in making early and precise assessments of atopic dermatitis severity and therefore improve patient care and outcomes.

Current state and prospects of artificial intelligence in allergy

The field of medicine is witnessing an exponential growth of interest in artificial intelligence (AI), which enables new research questions and the analysis of larger and new types of data. Nevertheless, applications that go beyond proof of concepts and deliver clinical value remain rare, especially in the field of allergy. This narrative review provides a fundamental understanding of the core concepts of AI and critically discusses its limitations and open challenges, such as data availability and bias, along with potential directions to surmount them. We provide a conceptual framework to structure AI applications within this field and discuss forefront case examples. Most of these applications of AI and machine learning in allergy concern supervised learning and unsupervised clustering, with a strong emphasis on diagnosis and subtyping. A perspective is shared on guidelines for good AI practice to guide readers in applying it effectively and safely, along with prospects of field advancement and initiatives to increase clinical impact. We anticipate that AI can further deepen our knowledge of disease mechanisms and contribute to precision medicine in allergy.

Artificial intelligence-assisted dermatology diagnosis: From unimodal to multimodal

Artificial Intelligence (AI) is progressively permeating medicine, notably in the realm of assisted diagnosis. However, the traditional unimodal AI models, reliant on large volumes of accurately labeled data and single data type usage, prove insufficient to assist dermatological diagnosis. Augmenting these models with text data from patient narratives, laboratory reports, and image data from skin lesions, dermoscopy, and pathologies could significantly enhance their diagnostic capacity. Large-scale pre-training multimodal models offer a promising solution, exploiting the burgeoning reservoir of clinical data and amalgamating various data types. This paper delves into unimodal models' methodologies, applications, and shortcomings while exploring how multimodal models can enhance accuracy and reliability. Furthermore, integrating cutting-edge technologies like federated learning and multi-party privacy computing with AI can substantially mitigate patient privacy concerns in dermatological datasets and further fosters a move towards high-precision self-diagnosis. Diagnostic systems underpinned by large-scale pre-training multimodal models can facilitate dermatology physicians in formulating effective diagnostic and treatment strategies and herald a transformative era in healthcare.

Submicron resolution techniques: Multiphoton microscopy in skin disease

Non-invasive optical examination plays a crucial role in various aspects of dermatology, such as diagnosis, management and research. Multiphoton microscopy uses a unique submicron technology to stimulate autofluorescence (AF), allowing for the observation of cellular structure, assessment of redox status and quantification of collagen fibres. This advanced imaging technique offers dermatologists novel insights into the skin's structure, positioning it as a promising 'stethoscope' for future development in the field. This review provides an overview of multiphoton microscopy's principles, technology and application in studying normal skin, tumour and inflammatory diseases, as well as collagen-related and pigmentary diseases.

Exploring the Intersection of Artificial Intelligence and Clinical Healthcare: A Multidisciplinary Review

Artificial intelligence (AI) plays a more and more important role in our everyday life due to the advantages that it brings when used, such as 24/7 availability, a very low percentage of errors, ability to provide real time insights, or performing a fast analysis. AI is increasingly being used in clinical medical and dental healthcare analyses, with valuable applications, which include disease diagnosis, risk assessment, treatment planning, and drug discovery. This paper presents a narrative literature review of AI use in healthcare from a multi-disciplinary perspective, specifically in the cardiology, allergology, endocrinology, and dental fields. The paper highlights data from recent research and development efforts in AI for healthcare, as well as challenges and limitations associated with AI implementation, such as data privacy and security considerations, along with ethical and legal concerns. The regulation of responsible design, development, and use of AI in healthcare is still in early stages due to the rapid evolution of the field. However, it is our duty to carefully consider the ethical implications of implementing AI and to respond appropriately. With the potential to reshape healthcare delivery and enhance patient outcomes, AI systems continue to reveal their capabilities.

Artificial Intelligence: Exploring the Future of Innovation in Allergy Immunology

PURPOSE OF REVIEW

Artificial intelligence (AI) has increasingly been used in healthcare. Given the capacity of AI to handle large data and complex relationships between variables, AI is well suited for applications in healthcare. Recently, AI has been applied to allergy research.

RECENT FINDINGS

In this article, we review how AI technologies have been utilized in basic science and clinical allergy research for asthma, atopic dermatitis, rhinology, adverse reactions to drugs and vaccines, food allergy, anaphylaxis, urticaria, and eosinophilic gastrointestinal disorders. We discuss barriers for AI adoption to improve the care of patients with atopic diseases. These studies demonstrate the utility of applying AI to the field of allergy to help investigators expand their understanding of disease pathogenesis, improve diagnostic accuracy, enable prediction for treatments and outcomes, and for drug discovery.

Multiphoton FLIM Analyses of Native and UVA-Modified Synthetic Melanins

To better understand the impact of solar light exposure on human skin, the chemical characterization of native melanins and their structural photo-modifications is of central interest. As the methods used today are invasive, we investigated the possibility of using multiphoton fluorescence lifetime (FLIM) imaging, along with phasor and bi-exponential fitting analyses, as a non-invasive alternative method for the chemical analysis of native and UVA-exposed melanins. We demonstrated that multiphoton FLIM allows the discrimination between native DHI, DHICA, Dopa eumelanins, pheomelanin, and mixed eu-/pheo-melanin polymers. We exposed melanin samples to high UVA doses to maximize their structural modifications. The UVA-induced oxidative, photo-degradation, and crosslinking changes were evidenced via an increase in fluorescence lifetimes along with a decrease in their relative contributions. Moreover, we introduced a new phasor parameter of a relative fraction of a UVA-modified species and provided evidence for its sensitivity in assessing the UVA effects. Globally, the fluorescence lifetime properties were modulated in a melanin-dependent and UVA dose-dependent manner, with the strongest modifications being observed for DHICA eumelanin and the weakest for pheomelanin. Multiphoton FLIM phasor and bi-exponential analyses hold promising perspectives for in vivo human skin mixed melanins characterization under UVA or other sunlight exposure conditions.

bSRWPSO-FKNN: A boosted PSO with fuzzy K-nearest neighbor classifier for predicting atopic dermatitis disease

Introduction

Atopic dermatitis (AD) is an allergic disease with extreme itching that bothers patients. However, diagnosing AD depends on clinicians' subjective judgment, which may be missed or misdiagnosed sometimes.

Methods

This paper establishes a medical prediction model for the first time on the basis of the enhanced particle swarm optimization (SRWPSO) algorithm and the fuzzy K-nearest neighbor (FKNN), called bSRWPSO-FKNN, which is practiced on a dataset related to patients with AD. In SRWPSO, the Sobol sequence is introduced into particle swarm optimization (PSO) to make the particle distribution of the initial population more uniform, thus improving the population's diversity and traversal. At the same time, this study also adds a random replacement strategy and adaptive weight strategy to the population updating process of PSO to overcome the shortcomings of poor convergence accuracy and easily fall into the local optimum of PSO. In bSRWPSO-FKNN, the core of which is to optimize the classification performance of FKNN through binary SRWPSO.

Results

To prove that the study has scientific significance, this paper first successfully demonstrates the core advantages of SRWPSO in well-known algorithms through benchmark function validation experiments. Secondly, this article demonstrates that the bSRWPSO-FKNN has practical medical significance and effectiveness through nine public and medical datasets.

Discussion

The 10 times 10-fold cross-validation experiments demonstrate that bSRWPSO-FKNN can pick up the key features of AD, including the content of lymphocytes (LY), Cat dander, Milk, Dermatophagoides Pteronyssinus/Farinae, Ragweed, Cod, and Total IgE. Therefore, the established bSRWPSO-FKNN method practically aids in the diagnosis of AD.

Two-Photon Imaging for Non-Invasive Corneal Examination

Two-photon imaging (TPI) microscopy, namely, two-photon excited fluorescence (TPEF), fluorescence lifetime imaging (FLIM), and second-harmonic generation (SHG) modalities, has emerged in the past years as a powerful tool for the examination of biological tissues. These modalities rely on different contrast mechanisms and are often used simultaneously to provide complementary information on morphology, metabolism, and structural properties of the imaged tissue. The cornea, being a transparent tissue, rich in collagen and with several cellular layers, is well-suited to be imaged by TPI microscopy. In this review, we discuss the physical principles behind TPI as well as its instrumentation. We also provide an overview of the current advances in TPI instrumentation and image analysis. We describe how TPI can be leveraged to retrieve unique information on the cornea and to complement the information provided by current clinical devices. The present state of corneal TPI is outlined. Finally, we discuss the obstacles that must be overcome and offer perspectives and outlooks to make clinical TPI of the human cornea a reality.

Artificial Intelligence in Dermatology Image Analysis: Current Developments and Future Trends

BACKGROUND

Thanks to the rapid development of computer-based systems and deep-learning-based algorithms, artificial intelligence (AI) has long been integrated into the healthcare field. AI is also particularly helpful in image recognition, surgical assistance and basic research. Due to the unique nature of dermatology, AI-aided dermatological diagnosis based on image recognition has become a modern focus and future trend. Key scientific concepts of review: The use of 3D imaging systems allows clinicians to screen and label skin pigmented lesions and distributed disorders, which can provide an objective assessment and image documentation of lesion sites. Dermatoscopes combined with intelligent software help the dermatologist to easily correlate each close-up image with the corresponding marked lesion in the 3D body map. In addition, AI in the field of prosthetics can assist in the rehabilitation of patients and help to restore limb function after amputation in patients with skin tumors.

THE AIM OF THE STUDY

For the benefit of patients, dermatologists have an obligation to explore the opportunities, risks and limitations of AI applications. This study focuses on the application of emerging AI in dermatology to aid clinical diagnosis and treatment, analyzes the current state of the field and summarizes its future trends and prospects so as to help dermatologists realize the impact of new technological innovations on traditional practices so that they can embrace and use AI-based medical approaches more quickly.

Artificial intelligence performance in image-based ovarian cancer identification: A systematic review and meta-analysis

BACKGROUND

Accurate identification of ovarian cancer (OC) is of paramount importance in clinical treatment success. Artificial intelligence (AI) is a potentially reliable assistant for the medical imaging recognition. We systematically review articles on the diagnostic performance of AI in OC from medical imaging for the first time.

METHODS

The Medline, Embase, IEEE, PubMed, Web of Science, and the Cochrane library databases were searched for related studies published until August 1, 2022. Inclusion criteria were studies that developed or used AI algorithms in the diagnosis of OC from medical images. The binary diagnostic accuracy data were extracted to derive the outcomes of interest: sensitivity (SE), specificity (SP), and Area Under the Curve (AUC). The study was registered with the PROSPERO, CRD42022324611.

FINDINGS

Thirty-four eligible studies were identified, of which twenty-eight studies were included in the meta-analysis with a pooled SE of 88% (95%CI: 85-90%), SP of 85% (82-88%), and AUC of 0.93 (0.91-0.95). Analysis for different algorithms revealed a pooled SE of 89% (85-92%) and SP of 88% (82-92%) for machine learning; and a pooled SE of 88% (84-91%) and SP of 84% (80-87%) for deep learning. Acceptable diagnostic performance was demonstrated in subgroup analyses stratified by imaging modalities (Ultrasound, Magnetic Resonance Imaging, or Computed Tomography), sample size (≤300 or >300), AI algorithms versus clinicians, year of publication (before or after 2020), geographical distribution (Asia or non Asia), and the different risk of bias levels (≥3 domain low risk or < 3 domain low risk).

INTERPRETATION

AI algorithms exhibited favorable performance for the diagnosis of OC through medical imaging. More rigorous reporting standards that address specific challenges of AI research could improve future studies.

FUNDING

This work was supported by the Natural Science Foundation of China (No. 82073647 to Q-JW and No. 82103914 to T-TG), LiaoNing Revitalization Talents Program (No. XLYC1907102 to Q-JW), and 345 Talent Project of Shengjing Hospital of China Medical University (No. M0268 to Q-JW and No. M0952 to T-TG).

COVID-19 and artificial intelligence: Experts and dermatologists perspective

INTRODUCTION

Artificial intelligence (AI) has an important role to play in future healthcare offerings. Machine learning and artificial neural networks are subsets of AI that refer to the incorporation of human intelligence into computers to think and behave like humans.

OBJECTIVE

The objective of this review article is to discuss perspectives on the AI in relation to Coronavirus disease (COVID-19).

METHODS

Google Scholar and PubMed databases were searched to retrieve articles related to COVID-19 and AI. The current evidence is analysed and perspectives on the usefulness of AI in COVID-19 is discussed.

RESULTS

The coronavirus pandemic has rendered the entire world immobile, crashing economies, industries, and health care. Telemedicine or tele-dermatology for dermatologists has become one of the most common solutions to tackle this crisis while adhering to social distancing for consultations. While it has not yet achieved its full potential, AI is being used to combat coronavirus disease on multiple fronts. AI has made its impact in predicting disease onset by issuing early warnings and alerts, monitoring, forecasting the spread of disease and supporting therapy. In addition, AI has helped us to build a model of a virtual protein structure and has played a role in teaching as well as social control.

CONCLUSION

Full potential of AI is yet to be realized. Expert data collection, analysis, and implementation are needed to improve this advancement.

Automatic SCOring of Atopic Dermatitis using Deep Learning (ASCORAD): A Pilot Study

Atopic dermatitis (AD) is a chronic, itchy skin condition that affects 15–20% of children but may occur at any age. It is estimated that 16.5 million US adults (7.3%) have AD that initially began at age >2 years, with nearly 40% affected by moderate or severe disease. Therefore, a quantitative measurement that tracks the evolution of AD severity could be extremely useful in assessing patient evolution and therapeutic efficacy. Currently, SCOring Atopic Dermatitis (SCORAD) is the most frequently used measurement tool in clinical practice. However, SCORAD has the following disadvantages: (i) time consuming—calculating SCORAD usually takes about 7–10 minutes per patient, which poses a heavy burden on dermatologists and (ii) inconsistency—owing to the complexity of SCORAD calculation, even well-trained dermatologists could give different scores for the same case. In this study, we introduce the Automatic SCORAD, an automatic version of the SCORAD that deploys state-of-the-art convolutional neural networks that measure AD severity by analyzing skin lesion images. Overall, we have shown that Automatic SCORAD may prove to be a rapid and objective alternative method for the automatic assessment of AD, achieving results comparable with those of human expert assessment while reducing interobserver variability.

Accurate diagnosis of atopic dermatitis by combining transcriptome and microbiota data with supervised machine learning

Atopic dermatitis (AD) is a common skin disease in childhood whose diagnosis requires expertise in dermatology. Recent studies have indicated that host genes–microbial interactions in the gut contribute to human diseases including AD. We sought to develop an accurate and automated pipeline for AD diagnosis based on transcriptome and microbiota data. Using these data of 161 subjects including AD patients and healthy controls, we trained a machine learning classifier to predict the risk of AD. We found that the classifier could accurately differentiate subjects with AD and healthy individuals based on the omics data with an average F1-score of 0.84. With this classifier, we also identified a set of 35 genes and 50 microbiota features that are predictive for AD. Among the selected features, we discovered at least three genes and three microorganisms directly or indirectly associated with AD. Although further replications in other cohorts are needed, our findings suggest that these genes and microbiota features may provide novel biological insights and may be developed into useful biomarkers of AD prediction.

Analysis on the Characterization of Multiphoton Microscopy Images for Malignant Neoplastic Colon Lesion Detection under Deep Learning Methods

Background:

Colorectal cancer has a high incidence rate worldwide, with over 1.8 million new cases and 880,792 deaths in 2018. Fortunately, its early detection significantly increases the survival rate, reaching a cure rate of 90% when diagnosed at a localized stage. Colonoscopy is the gold standard technique for detection and removal of colorectal lesions with potential to evolve into cancer. When polyps are found in a patient, the current procedure is their complete removal. However, in this process, gastroenterologists cannot assure complete resection and clean margins which are given by the histopathology analysis of the removed tissue, which is performed at laboratory.

Aims:

In this paper, we demonstrate the capabilities of multiphoton microscopy (MPM) technology to provide imaging biomarkers that can be extracted by deep learning techniques to identify malignant neoplastic colon lesions and distinguish them from healthy, hyperplastic, or benign neoplastic tissue, without the need for histopathological staining.

Materials and Methods:

To this end, we present a novel MPM public dataset containing 14,712 images obtained from 42 patients and grouped into 2 classes. A convolutional neural network is trained on this dataset and a spatially coherent predictions scheme is applied for performance improvement.

Results:

We obtained a sensitivity of 0.8228 ± 0.1575 and a specificity of 0.9114 ± 0.0814 on detecting malignant neoplastic lesions. We also validated this approach to estimate the self-confidence of the network on its own predictions, obtaining a mean sensitivity of 0.8697 and a mean specificity of 0.9524 with the 18.67% of the images classified as uncertain.

Conclusions:

This work lays the foundations for performing in vivo optical colon biopsies by combining this novel imaging technology together with deep learning algorithms, hence avoiding unnecessary polyp resection and allowing in situ diagnosis assessment.

Automatic segmentation of skin cells in multiphoton data using multi-stage merging

We propose a novel automatic segmentation algorithm that separates the components of human skin cells from the rest of the tissue in fluorescence data of three-dimensional scans using non-invasive multiphoton tomography. The algorithm encompasses a multi-stage merging on preprocessed superpixel images to ensure independence from a single empirical global threshold. This leads to a high robustness of the segmentation considering the depth-dependent data characteristics, which include variable contrasts and cell sizes. The subsequent classification of cell cytoplasm and nuclei are based on a cell model described by a set of four features. Two novel features, a relationship between outer cell and inner nucleus (OCIN) and a stability index, were derived. The OCIN feature describes the topology of the model, while the stability index indicates segment quality in the multi-stage merging process. These two new features, combined with the local gradient magnitude and compactness, are used for the model-based fuzzy evaluation of the cell segments. We exemplify our approach on an image stack with 200 × 200 × 100  μm³, including the skin layers of the stratum spinosum and the stratum basale of a healthy volunteer. Our image processing pipeline contributes to the fully automated classification of human skin cells in multiphoton data and provides a basis for the detection of skin cancer using non-invasive optical biopsy.

Special Series Guest Editorial: Artificial Intelligence and Machine Learning in Biomedical Optics

Guest editors Behrouz Shabestri, Mark Anastasio, Baowei Fei, and Frédéric Leblond provide an overview of the JBO Special Series on Artificial Intelligence Machine Learning in Biomedical Optics.

Reflectance Confocal Microscopy, Optical Coherence Tomography, and Multiphoton Microscopy in Inflammatory Skin Disease Diagnosis

BACKGROUND AND OBJECTIVES

Technological advances in medicine have brought about many novel skin imaging devices. This review aims to evaluate the scientific evidence supporting the use of noninvasive optical imaging techniques to aid in the diagnosis and prognosis of inflammatory skin diseases.

STUDY DESIGN/MATERIALS AND METHODS

PubMed and Scopus were searched in September 2020 according to PRISMA guidelines for articles using reflectance confocal microscopy (RCM), optical coherence tomography (OCT), and multiphoton microscopy (MPM) in inflammatory skin diseases, excluding studies monitoring treatment efficacy.

RESULTS

At the time of the study, there were 66 articles that addressed the utilization of noninvasive imaging in interface, spongiotic, psoriasiform, vesiculobullous, and fibrosing/sclerosing inflammatory skin dermatoses: RCM was utilized in 46, OCT in 16, and MPM in 5 articles. RCM was most investigated in psoriasiform dermatoses, whereas OCT and MPM were both most investigated in spongiotic dermatoses, including atopic dermatitis and allergic contact dermatitis.

CONCLUSIONS

There is preliminary evidence to support the diagnostic potential of noninvasive optical imaging techniques in inflammatory skin diseases. Improvements in the devices and further correlation with histology will help broaden their utility. Additional studies are needed to determine the parameters for diagnostic features, disease differentiation, and staging of inflammatory skin conditions. Lasers Surg. Med. © 2021 Wiley Periodicals LLC.

Influence of number of membership functions on prediction of membrane systems using adaptive network based fuzzy inference system (ANFIS)

In membrane separation technologies, membrane modules are used to separate chemical components. In membrane technology, understanding the behavior of fluids inside membrane module is challenging, and numerical methods are possible by using computational fluid dynamics (CFD). On the other hand, the optimization of membrane technology via CFD needs time and computational costs. Artificial Intelligence (AI) and CFD together can model a chemical process, including membrane technology and phase separation. This process can learn the process by learning the neural networks, and point by point learning of CFD mesh elements (computing nodes), and the fuzzy logic system can predict this process. In the current study, the adaptive neuro-fuzzy inference system (ANFIS) model and different parameters of ANFIS for learning a process based on membrane technology was used. The purpose behind using this model is to see how different tuning parameters of the ANFIS model can be used for increasing the exactness of the AI model and prediction of the membrane technology. These parameters were changed in this study, and the accuracy of the prediction was investigated. The results indicated that with low number of inputs, poor regression was obtained, less than 0.32 (R-value), but by increasing the number of inputs, the AI algorithm led to an increase in the prediction capability of the model. When the number of inputs increased to 4, the R-value was increased to 0.99, showing the high accuracy of model as well as its high capability in prediction of membrane process. The AI results were in good agreement with the CFD results. AI results were achieved in a limited time and with low computational costs. In terms of the categorization of CFD data-set, the AI framework plays a critical role in storing data in short memory, and the recovery mechanism can be very easy for users. Furthermore, the results were compared with Particle Swarm Optimization (PSOFIS), and Genetic Algorithm (GAFIS). The time for prediction and learning were compared to study the capability of the methods in prediction and their accuracy.

Skin Barriers in Dermal Drug Delivery: Which Barriers Have to Be Overcome and How Can We Measure Them?

Although, drugs are required in the various skin compartments such as viable epidermis, dermis, or hair follicles, to efficiently treat skin diseases, drug delivery into and across the skin is still challenging. An improved understanding of skin barrier physiology is mandatory to optimize drug penetration and permeation. The various barriers of the skin have to be known in detail, which means methods are needed to measure their functionality and outside-in or inside-out passage of molecules through the various barriers. In this review, we summarize our current knowledge about mechanical barriers, i.e., stratum corneum and tight junctions, in interfollicular epidermis, hair follicles and glands. Furthermore, we discuss the barrier properties of the basement membrane and dermal blood vessels. Barrier alterations found in skin of patients with atopic dermatitis are described. Finally, we critically compare the up-to-date applicability of several physical, biochemical and microscopic methods such as transepidermal water loss, impedance spectroscopy, Raman spectroscopy, immunohistochemical stainings, optical coherence microscopy and multiphoton microscopy to distinctly address the different barriers and to measure permeation through these barriers in vitro and in vivo.