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Current state and prospects of artificial intelligence in allergy

ALLERGY (2023)

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Summary: This study aimed to describe the developmental trajectories of childhood wheezing in South Africa and the UK. Four wheeze phenotypes were identified in the South African cohort: never, early transient, late onset, and recurrent. All-cause lower respiratory tract infection and respiratory syncytial virus infection were associated with recurrent wheeze.

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An integrated cell atlas of the lung in health and disease

Lisa Sikkema et al.

Summary: Single-cell technologies are used to study human tissues, but individual studies have limitations in terms of sample size and cell type definitions. The integrated Human Lung Cell Atlas (HLCA) combines 49 datasets from 486 individuals to create a comprehensive atlas of the human respiratory system. The HLCA provides new annotations for different cell types and allows for the identification of gene modules associated with demographic variables and changes in gene expression.

NATURE MEDICINE (2023)

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A Laboratory Evaluation of the New Automated Pollen Sensor Beenose: Pollen Discrimination Using Machine Learning Techniques

Houssam El Azari et al.

Summary: The monitoring of airborne pollen has become increasingly important due to the rise in pollen-induced allergies. This paper introduces a new optical pollen sensor, Beenose, which can automatically count and identify pollen grains in real-time using measurements at multiple scattering angles. Different statistical and machine learning methods are discussed for distinguishing different pollen species. The results show that Beenose can accurately cluster pollen species based on their size properties and separate pollen particles from non-pollen ones. However, further parameters need to be considered for more robust pollen identification, particularly for species with similar optical behavior.

SENSORS (2023)

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An artificial intelligence algorithm-based smartphone application for daily cough monitoring

Ji-Su Shim et al.

ALLERGY (2023)

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Reinforcement Learning in Healthcare: A Survey

Chao Yu et al.

Summary: As a subfield of machine learning, reinforcement learning aims to optimize decision making by using interaction samples of an agent with its environment and the potentially delayed feedbacks. It tackles sequential decision-making problems with sampled, evaluative, and delayed feedbacks simultaneously. In healthcare domains, it is a suitable candidate for developing powerful solutions in various areas such as treatment regimes, medical diagnosis, and control or scheduling problems. Challenges, open issues, potential solutions, and future research directions are also discussed.

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Modeling Wheezing Spells Identifies Phenotypes with Different Outcomes and Genetic Associates

Sadia Haider et al.

Summary: This study proposes a more comprehensive method to describe the development of wheezing and identifies five different phenotypes of wheezing. The spell-based approach proves to be more robust and able to capture the characteristics of wheezing development better than binary information.

AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE (2022)

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Modelling trajectories of parentally reported and physician-confirmed atopic dermatitis in a birth cohort study

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Summary: This study aimed to identify longitudinal trajectories of atopic dermatitis (AD) in childhood using different data sources and definitions, finding variations in AD categories and associations with risk factors under different definitions of AD.

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Sex-specific developmental trajectories of eczema from infancy to age 26 years: A birth cohort study

Ali H. Ziyab et al.

Summary: This study investigated the developmental trajectories of eczema from infancy to early adulthood, identifying different patterns and risk factors influencing disease development. Distinct trajectories of eczema were found in males and females, with predisposing risk factors such as FLG gene variants, parental history of eczema, maternal eczema, and high birthweight. Results suggest that males and females may experience diverse courses of eczema development.

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Nuclear Medicine and Artificial Intelligence: Best Practices for Algorithm Development

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Endotyping allergic rhinitis in children: A machine learning approach

Velia Malizia et al.

Summary: This study used a machine learning approach to identify different endotypes of childhood allergic rhinitis (AR) based on nasal cytology (NC). The results showed that LCA can help identify different AR phenotypes, supporting personalized healthcare in the context of precision medicine.

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

Ziyuan Jiang et al.

Summary: We developed an accurate and automated pipeline for diagnosing atopic dermatitis (AD) based on transcriptome and microbiota data. Our findings suggest that certain genes and microbiota features may serve as useful biomarkers for predicting AD.

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

Jashin J. Wu et al.

Summary: This study assessed predictors of nonresponse to dupilumab in patients with atopic dermatitis (AD). The results showed that systemic dupilumab therapy can be associated with a relatively high prevalence of nonresponse indicators, and the common predictors of nonresponse were the use of ibuprofen and a higher Charlson Comorbidity Index.

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Establishing subclasses of childhood eczema, their risk factors and prognosis

Diego J. Lopez et al.

Summary: This study identified five distinct subclasses of eczema and found that they were associated with different risk factors and allergic outcomes, suggesting their potential to identify high-risk groups for monitoring and intervention.

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Implementation of the MASK-Air® App for Rhinitis and Asthma in Older Adults: MASK@Puglia Pilot Study

Maria Teresa Ventura et al.

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Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI

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Nasal DNA methylation at three CpG sites predicts childhood allergic disease

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A Predictive Machine Learning Tool for Asthma Exacerbations: Results from a 12-Week, Open-Label Study Using an Electronic Multi-Dose Dry Powder Inhaler with Integrated Sensors

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Data-driven research on eczema: Systematic characterization of the field and recommendations for the future

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Summary: The study summarizes the current and future trends of data-driven research on atopic dermatitis (AD)/eczema, and identifies areas that could benefit from the application of these methods. The analysis highlights five key themes: allergic co-morbidities, image analysis and classification, disaggregation, quality of life and treatment response, and risk factors and prevalence. Machine learning and artificial intelligence methods are mainly used in studies on quality of life, prevalence, risk factors, allergic co-morbidities, and disaggregation, while Bayesian statistics is focused on treatment, risk factors, and allergy research.

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Application of Machine Learning Algorithms for Asthma Management with mHealth: A Clinical Review

Kevin C. H. Tsang et al.

Summary: Applying machine learning to asthma mHealth data has great potential, but current research is limited. Most studies use traditional machine learning techniques, with only a few exploring deep learning algorithms. Future research should focus on data collection and validation to improve the generalizability of the studies.

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The 2021 landscape of FDA-approved artificial intelligence/machine learning-enabled medical devices: An analysis of the characteristics and intended use

Simeng Zhu et al.

INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS (2022)

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A proposal for developing a platform that evaluates algorithmic equity and accuracy

Paul Cerrato et al.

Summary: We are currently at a crucial point in the development of healthcare artificial intelligence. The proposal in this article examines algorithmic biases and accuracy issues in diagnostic and therapeutic algorithms, and suggests solutions to these problems. Additionally, the article describes a project that aims to evaluate biases in a large number of healthcare records, as well as proposes a labeling system and AI evaluation/testing system to aid healthcare professionals in assessing the value of products and services with algorithms.

BMJ HEALTH & CARE INFORMATICS (2022)

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Article Computer Science, Information Systems

Predictive modelling and analytics for diabetes using a machine learning approach

Harleen Kaur et al.

Summary: Diabetes is a global metabolic disorder that can be diagnosed early using machine learning techniques and predictive models to analyze potential risk factors.

APPLIED COMPUTING AND INFORMATICS (2022)

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Article Allergy

Detecting asthma exacerbations using daily home monitoring and machine learning

Olivier Zhang et al.

Summary: Machine learning algorithms applied to daily self-monitoring data of asthma patients can effectively detect severe asthma exacerbations, leading to improved outcomes.

JOURNAL OF ASTHMA (2021)

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Article Biochemistry & Molecular Biology

Robust breast cancer detection in mammography and digital breast tomosynthesis using an annotation-efficient deep learning approach

William Lotter et al.

Summary: Breast cancer poses a global challenge and early detection through screening mammography is crucial. Recent advancements in applying deep learning to mammography have addressed key difficulties, enhancing accuracy and accessibility of screening mammography.

NATURE MEDICINE (2021)

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A Comprehensive Survey on Transfer Learning

Fuzhen Zhuang et al.

Summary: Transfer learning aims to improve the performance of target learners by transferring knowledge from related source domains, reducing the reliance on target-domain data. This survey aims to systematize and summarize existing research studies in order to help readers understand the current status and ideas in the area of transfer learning.

PROCEEDINGS OF THE IEEE (2021)

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Article Critical Care Medicine

Chromosome 17q12-21 Variants Are Associated with Multiple Wheezing Phenotypes in Childhood

Brian Hallmark et al.

Summary: This study explored the association between wheezing phenotypes and 17q12-21 genetic variants in children, revealing four latent classes of wheezing and highlighting the genetic locus as relevant to wheezing. Differences in genetic associations between European American and African American children were also observed, suggesting potential racial/ancestral influences on wheezing phenotypes.

AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE (2021)

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Article Dermatology

Four childhood atopic dermatitis subtypes identified from trajectory and severity of disease and internally validated in a large UK birth cohort

A. R. Mulick et al.

Summary: This study identified four distinct and recognizable subtypes of childhood AD based on symptom severity and trajectories, with differential associations of genetic risk factors, comorbidities, and demographic and environmental variables among and between subtypes. Further research is needed to explore the implications of these findings.

BRITISH JOURNAL OF DERMATOLOGY (2021)

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Ziaurrehman Tanoli et al.

Summary: Drug repurposing is a cost-effective strategy for drug development, and machine learning and artificial intelligence methods can help identify potential reuses systematically. The use of comprehensive target activity profiles in anticancer drug therapy can facilitate a systematic repurposing process.

EXPERT OPINION ON DRUG DISCOVERY (2021)

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Article Multidisciplinary Sciences

Personalized prediction of early childhood asthma persistence: A machine learning approach

Saurav Bose et al.

Summary: The study aimed to develop machine learning models to predict which children diagnosed with asthma before age 5 will continue to have asthma-related visits. Feature importance analysis highlighted factors such as age and number of visits as important predictors of persistent asthma.

PLOS ONE (2021)

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A Survey of the Usages of Deep Learning for Natural Language Processing

Daniel W. Otter et al.

Summary: This article provides a brief introduction to the recent developments in natural language processing driven by deep learning models, discusses their applications and research findings, and offers recommendations for future research directions.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2021)

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Review Respiratory System

Machine learning in asthma research: moving toward a more integrated approach

Sara Fontanella et al.

Summary: Big data is reshaping the future of medicine, with a shift towards data-driven hypothesis-generating methods in asthma research. However, despite advancements, few findings have been translated into clinically actionable solutions. Embracing collaborative science and cross-disciplinary teams is necessary to fully capitalize on the potential of big data.

EXPERT REVIEW OF RESPIRATORY MEDICINE (2021)

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Article Allergy

Characterization of Asthma Trajectories from Infancy to Young Adulthood

Maria Odling et al.

Summary: Asthma development trajectories were identified as never/infrequent, early-onset transient, adolescent-onset, and persistent asthma. The adolescent-onset and persistent asthma groups had equal burdens of asthma control in adolescence and young adulthood, but the persistent asthma group showed more signs of type 2 inflammation.

JOURNAL OF ALLERGY AND CLINICAL IMMUNOLOGY-IN PRACTICE (2021)

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Article Computer Science, Artificial Intelligence

A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI

Erico Tjoa et al.

Summary: Artificial intelligence and machine learning have shown remarkable performances in various fields, but the challenge of interpretability remains. The medical sector requires higher levels of interpretability to ensure the reliability of machine decisions, and a deeper understanding of the mechanisms behind machine algorithms is needed to advance medical practices.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2021)

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Article Critical Care Medicine

Recurrent Severe Preschool Wheeze: From Prespecified Diagnostic Labels to Underlying Endotypes

Polly F. M. Robinson et al.

Summary: The study identified four clusters of severe preschool wheeze, distinguished by sensitization, peripheral eosinophilia, lower airway neutrophilia, and bacteriology. Blood tests and pathogen distribution showed significant differences among different pathological clusters.

AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE (2021)

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Article Allergy

Development of childhood asthma prediction models using machine learning approaches

Dilini M. Kothalawala et al.

Summary: This study applied machine learning approaches to predict asthma in early childhood and preschool age, showing that ML methods can improve upon existing predictive models with good generalisability.

CLINICAL AND TRANSLATIONAL ALLERGY (2021)

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Digital medicine and the curse of dimensionality

Visar Berisha et al.

Summary: Digital health data are multimodal and high-dimensional, enabling a multitude of signals to characterize a patient's health state. While AI models show promise in diagnosis, prognosis, and tracking, challenges such as unpredictable performance after deployment exist, partly due to the high-dimensional nature of the data.

NPJ DIGITAL MEDICINE (2021)

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Merged Affinity Network Association Clustering: Joint multi-omic/clinical clustering to identify disease endotypes

Scott R. Tyler et al.

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CELL REPORTS (2021)

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Predicting Treatment Outcomes Using Explainable Machine Learning in Children with Asthma

Mario Lovric et al.

Summary: This study utilized machine learning algorithms to predict treatment outcomes in pediatric asthma patients, finding that changes in asthma control and FENO values after controller medication use had higher predictive power for treatment outcomes.

CHILDREN-BASEL (2021)

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A Survey on Bias and Fairness in Machine Learning

Ninareh Mehrabi et al.

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ACM COMPUTING SURVEYS (2021)

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Article Allergy

Atopic dermatitis trajectories to age 8 years in the GUSTO cohort

Noor H. A. Suaini et al.

Summary: Three different trajectories of atopic dermatitis (AD) were identified in a birth cohort study in Singapore, each associated with different risk factors and prognostic implications. Early-onset transient AD was linked to male gender, family history of atopy, house dust mite sensitization and wheezing; early-onset persistent AD was associated with antenatal/intrapartum antibiotic use, food sensitization and wheezing; late-onset persistent AD was related to family history of atopy, house dust mite sensitization, allergic rhinitis and wheezing. Further research is needed to understand the molecular and immunological origins of these phenotypes.

CLINICAL AND EXPERIMENTAL ALLERGY (2021)

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Onat Kadioglu et al.

Summary: The study utilized a variety of methods to identify new drug candidates against COVID-19, including existing approved drugs and natural compounds. Several candidate compounds were predicted as potential SARS-CoV-2 inhibitors, including drugs that target other viruses.

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Hee Yun Seol et al.

Summary: This study evaluated the effectiveness and efficiency of an AI-assisted CDS tool, A-GPS, in optimizing asthma management through a randomized clinical trial. While both the A-GPS intervention group and the control group showed decreased asthma exacerbation proportions, there was no significant difference in exacerbation frequency between the two groups during the study period. However, the A-GPS intervention significantly reduced the time required for clinicians to review electronic health records for asthma management, leading to more efficient care.

PLOS ONE (2021)

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Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes

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Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): Explanation and Elaboration

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ANNALS OF INTERNAL MEDICINE (2015)

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Geographical variation in the prevalence of sensitization to common aeroallergens in adults: the GA2LEN survey

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Clinical and Epidemiologic Phenotypes of Childhood Asthma

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AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE (2014)

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Unsupervised phenotyping of Severe Asthma Research Program participants using expanded lung data

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JOURNAL OF ALLERGY AND CLINICAL IMMUNOLOGY (2014)

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Developmental Profiles of Eczema, Wheeze, and Rhinitis: Two Population-Based Birth Cohort Studies

Danielle C. M. Belgrave et al.

PLOS MEDICINE (2014)

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Joint modeling of parentally reported and physician-confirmed wheeze identifies children with persistent troublesome wheezing

Danielle C. M. Belgrave et al.

JOURNAL OF ALLERGY AND CLINICAL IMMUNOLOGY (2013)

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Comparison of childhood wheezing phenotypes in 2 birth cohorts: ALSPAC and PIAMA

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JOURNAL OF ALLERGY AND CLINICAL IMMUNOLOGY (2011)

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Associations of wheezing phenotypes in the first 6 years of life with atopy, lung function and airway responsiveness in mid-childhood

J. Henderson et al.

THORAX (2008)

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The National Asthma Campaign Manchester Asthma and Allergy Study

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