Predictors of nonresponse to dupilumab in patients with atopic dermatitis: A machine learning analysis

Advancements in artificial intelligence for atopic dermatitis: diagnosis, treatment, and patient management

Atopic dermatitis (AD) is a common and complex skin disease that significantly affects the quality of life of patients. The latest advances in artificial intelligence (AI) technology have introduced new methods for diagnosing, treating, and managing AD. AI has various innovative applications in the diagnosis and treatment of atopic dermatitis, with particular emphasis on its significant benefits in medical diagnosis, treatment monitoring, and patient care. AI algorithms, especially those that use deep learning techniques, demonstrate strong performance in recognizing skin images and effectively distinguishing different types of skin lesions, including common AD manifestations. In addition, artificial intelligence has also shown promise in creating personalized treatment plans, simplifying drug development processes, and managing clinical trials. Despite challenges in data privacy and model transparency, the potential of artificial intelligence in advancing AD care is enormous, bringing the future to precision medicine and improving patient outcomes. This manuscript provides a comprehensive review of the application of AI in the process of AD disease for the first time, aiming to play a key role in the advancement of AI in skin health care and further enhance the clinical diagnosis and treatment of AD.

Atopic dermatitis stratification: current and future perspective on skin and blood transcriptomic and proteomic profiling

INTRODUCTION

Atopic dermatitis (AD) is a common, chronic inflammatory skin disorder driven by an intricate interplay of genetic, environmental, and immunological factors.

AREAS COVERED

As a clinically heterogenous condition, AD may be stratified into subtypes based on factors including, chronicity, immunoglobulin E levels, severity, age, and ethnicity. Transcriptomic and proteomic analyses in skin and blood help elucidate the underlying molecular mechanisms of these AD subtypes, referred to as AD endotypes. Further characterizing AD endotypes using reliable biomarkers can facilitate the development of more effective and personalized therapeutics and improve our tools for monitoring disease progression and therapeutic response across a diverse subset of patients. Here, we aim to provide perspective on the latest research regarding AD stratification using skin and blood-based studies and insight into the implications of these findings on the future of AD research and clinical practice.

EXPERT OPINION

The precise stratification of AD endotypes will allow for the development of reliable biomarkers and a more personalized medical treatment approach. Clinical practice and trials will eventually be able to bridge clinical with molecular data to optimize individualized treatments and more effectively monitor treatment response.

An updated reappraisal of dupilumab in children and adolescents with moderate-severe atopic dermatitis

Atopic dermatitis (AD) is still a demanding challenge in clinical practice. Type 2 inflammation is the most common inflammatory pathway in children and adolescents with AD. Anti-inflammatory drugs, mainly corticosteroids (CS) and immunomodulant agents are the primary therapeutic approach to dampening type 2 inflammation. However, AD patients may require long-term high CS doses or drug combinations with possibly significant adverse effects to achieve and maintain disease control. In this regard, the advent of biologics constituted a breakthrough in managing this condition. Dupilumab is a monoclonal antibody directed against the IL-4 receptor α-subunit (IL-4Rα), antagonizing both IL-4 and IL-13 and is approved for pediatric severe AD. This review presents and discusses the most recent published studies on dupilumab in children and adolescents with AD. There is convincing evidence that dupilumab is safe and effective in managing AD. It can reduce skin lesions and associated itching, reduce the need for additional medications, and improve disease control and quality of life. However, a thorough diagnostic pathway is mandatory, especially considering the different AD phenotypes. The ideal eligible candidate is a child or adolescent with AD requiring systemic treatment because of severe clinical manifestations and impaired quality of life.

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.

Distinct T cell signatures are associated with Staphylococcus aureus skin infection in pediatric atopic dermatitis

Atopic dermatitis (AD) is an inflammatory skin condition with a childhood prevalence of up to 25%. Microbial dysbiosis is characteristic of AD, with Staphylococcus aureus the most frequent pathogen associated with disease flares and increasingly implicated in disease pathogenesis. Therapeutics to mitigate the effects of S. aureus have had limited efficacy and S. aureus-associated temporal disease flares are synonymous with AD. An alternative approach is an anti-S. aureus vaccine, tailored to AD. Experimental vaccines have highlighted the importance of T cells in conferring protective anti-S. aureus responses; however, correlates of T cell immunity against S. aureus in AD have not been identified. We identify a systemic and cutaneous immunological signature associated with S. aureus skin infection (ADS.aureus) in a pediatric AD cohort, using a combined Bayesian multinomial analysis. ADS.aureus was most highly associated with elevated cutaneous chemokines IP10 and TARC, which preferentially direct Th1 and Th2 cells to skin. Systemic CD4+ and CD8+ T cells, except for Th2 cells, were suppressed in ADS.aureus, particularly circulating Th1, memory IL-10+ T cells, and skin-homing memory Th17 cells. Systemic γδ T cell expansion in ADS.aureus was also observed. This study suggests that augmentation of protective T cell subsets is a potential therapeutic strategy in the management of S. aureus in AD.

Parameter-based transfer learning for severity classification of atopic dermatitis using hyperspectral imaging

BACKGROUND/PURPOSE

Because atopic dermatitis (AD) is a chronic inflammatory skin condition that causes structural changes, there is a growing need for noninvasive research methods to evaluate this condition. Hyperspectral imaging (HSI) captures skin structure features by exploiting light wavelength variations in penetration depth. In this study, parameter-based transfer learning was deployed to classify the severity of AD using HSI. Therefore, we aimed to obtain an optimal combination of classification results from the four models after constructing different source- and target-domain datasets.

METHODS

We designated psoriasis, skin cancer, eczema, and AD datasets as the source datasets, and the set of images acquired via hyperspectral camera as the target dataset for wavelength-specific AD classification. We compared the severity classification performances of 96 combinations of sources, models, and targets.

RESULTS

The highest classification performance of 83% was achieved when ResNet50 was trained on the augmented psoriasis dataset as the source, with the resulting parameters used to train the model on the target Near-infrared radiation (NIR) dataset. The second highest classification accuracy of 81% was achieved when ResNet50 was trained on the unaugmented psoriasis dataset as the source, with the resulting parameters used to train the model on the target R dataset. ResNet50 demonstrated potential as a generalized model for both the source and target data, also confirming that the psoriasis dataset is an effective training resource.

CONCLUSION

The present study not only demonstrates the feasibility of the severity classification of AD based on hyperspectral images, but also showcases combinations and research scalability for domain exploration.

Machine learning analysis of pretreatment skin biopsies predicts nonresponse to dupilumab in patients with eczematous dermatitis

While dupilumab has revolutionized the treatment of atopic dermatitis (AD), a subset of patients may fail to respond or worsen after dupilumab initiation. Using a retrospective cohort of 53 dupilumab responders and 17 nonresponders, we developed a logistic regression classifier to predict nonresponse using 7 cytokine staining and histological features derived from pretreatment biopsies. Our model demonstrated an accuracy of 95.7%, a sensitivity of 88.2%, a specificity of 98.1% and a PPV of 93.8% for predicting nonresponse using leave-one-out cross-validation, underscoring treatment-relevant immunological heterogeneity in eczema and demonstrating the potential of using machine learning and tissue biomarkers to predict dupilumab nonresponse.

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: 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.

Immunopathogenesis of Atopic Dermatitis: Focus on Interleukins as Disease Drivers and Therapeutic Targets for Novel Treatments

Atopic dermatitis is a chronic, recurrent inflammatory skin disorder manifesting by eczematous lesions and intense pruritus. Atopic dermatitis develops primarily as a result of an epidermal barrier defect and immunological imbalance. Advances in understanding these pathogenetic hallmarks, and particularly the complex role of interleukins as atopic dermatitis drivers, resulted in achieving significant therapeutic breakthroughs. Novel medications involve monoclonal antibodies specifically blocking the function of selected interleukins and small molecules such as Janus kinase inhibitors limiting downstream signaling to reduce the expression of a wider array of proinflammatory factors. Nevertheless, a subset of patients remains refractory to those treatments, highlighting the complexity of atopic dermatitis immunopathogenesis in different populations. In this review, we address the immunological heterogeneity of atopic dermatitis endotypes and phenotypes and present novel interleukin-oriented therapies for this disease.