AIMC Topic: Hypersensitivity

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Progress and Prospects of Transdermal Treatment of Allergic Skin Diseases with Natural Drugs based on Nanotechnology.

AAPS PharmSciTech
Allergic skin disease conditions represent a significant global health challenge, with conventional therapies frequently associated with local dermal irritation and systemic adverse effects. Nanotechnology-enabled transdermal drug delivery platforms ...

An exploratory machine learning study on paediatric abdominal pain phenotyping and prediction.

PloS one
BACKGROUND: The exact mechanisms underlying paediatric abdominal pain (AP) remain unclear due to patient heterogeneity. This preliminary study aimed to identify AP phenotypes and develop predictive models to explore associated factors, with the goal ...

Factors associated with allergic diseases in Chinese children aged 6-14 years.

BMC public health
BACKGROUND AND OBJECTIVES: We aimed to identify and optimize contributing factors associated with allergic diseases by machine/deep learning algorithms among school-age children aged 6-14 years.

AI-Driven Biomarker Discovery and Personalized Allergy Treatment: Utilizing Machine Learning and NGS.

Current allergy and asthma reports
PURPOSEĀ OF REVIEW: This review explores the transformative potential of artificial intelligence (AI) and next-generation sequencing (NGS) in allergy diagnostics and treatment. It focuses on leveraging these technologies to enhance precision in biomar...

Application and research progress of artificial intelligence in allergic diseases.

International journal of medical sciences
Artificial intelligence (AI), as a new technology that can assist or even replace some human functions, can collect and analyse large amounts of textual, visual and auditory data through techniques such as Reinforcement Learning, Machine Learning, De...

Machine learning-derived asthma and allergy trajectories in children: a systematic review and meta-analysis.

European respiratory review : an official journal of the European Respiratory Society
INTRODUCTION: Numerous studies have characterised trajectories of asthma and allergy in children using machine learning, but with different techniques and mixed findings. The present work aimed to summarise the evidence and critically appraise the me...

Artificial intelligence in pediatric allergy research.

European journal of pediatrics
UNLABELLED: Atopic dermatitis, food allergy, allergic rhinitis, and asthma are among the most common diseases in childhood. They are heterogeneous diseases, can co-exist in their development, and manifest complex associations with other disorders and...

Future of allergy and immunology: Is artificial intelligence the key in the digital era?

Annals of allergy, asthma & immunology : official publication of the American College of Allergy, Asthma, & Immunology
Artificial intelligence (AI) is reshaping allergy and immunology by integrating cutting-edge technology to enhance patient outcomes and redefine clinical practices and research. This review evaluates AI's evolving role, emphasizing its impact on diag...

Machine learning-derived phenotypic trajectories of asthma and allergy in children and adolescents: protocol for a systematic review.

BMJ open
INTRODUCTION: Development of asthma and allergies in childhood/adolescence commonly follows a sequential progression termed the 'atopic march'. Recent reports indicate, however, that these diseases are composed of multiple distinct phenotypes, with p...

Allergy Wheal and Erythema Segmentation Using Attention U-Net.

Journal of imaging informatics in medicine
The skin prick test (SPT) is a key tool for identifying sensitized allergens associated with immunoglobulin E-mediated allergic diseases such as asthma, allergic rhinitis, atopic dermatitis, urticaria, angioedema, and anaphylaxis. However, the SPT is...