Home Management of Childhood Asthma Exacerbations

Automated Interpretation of Lung Sounds by Deep Learning in Children With Asthma: Scoping Review and Strengths, Weaknesses, Opportunities, and Threats Analysis

BACKGROUND

The interpretation of lung sounds plays a crucial role in the appropriate diagnosis and management of pediatric asthma. Applying artificial intelligence (AI) to this task has the potential to better standardize assessment and may even improve its predictive potential.

OBJECTIVE

This study aims to objectively review the literature on AI-assisted lung auscultation for pediatric asthma and provide a balanced assessment of its strengths, weaknesses, opportunities, and threats.

METHODS

A scoping review on AI-assisted lung sound analysis in children with asthma was conducted across 4 major scientific databases (PubMed, MEDLINE Ovid, Embase, and Web of Science), supplemented by a gray literature search on Google Scholar, to identify relevant studies published from January 1, 2000, until May 23, 2023. The search strategy incorporated a combination of keywords related to AI, pulmonary auscultation, children, and asthma. The quality of eligible studies was assessed using the ChAMAI (Checklist for the Assessment of Medical Artificial Intelligence).

RESULTS

The search identified 7 relevant studies out of 82 (9%) to be included through an academic literature search, while 11 of 250 (4.4%) studies from the gray literature search were considered but not included in the subsequent review and quality assessment. All had poor to medium ChAMAI scores, mostly due to the absence of external validation. Identified strengths were improved predictive accuracy of AI to allow for prompt and early diagnosis, personalized management strategies, and remote monitoring capabilities. Weaknesses were the heterogeneity between studies and the lack of standardization in data collection and interpretation. Opportunities were the potential of coordinated surveillance, growing data sets, and new ways of collaboratively learning from distributed data. Threats were both generic for the field of medical AI (loss of interpretability) but also specific to the use case, as clinicians might lose the skill of auscultation.

CONCLUSIONS

To achieve the opportunities of automated lung auscultation, there is a need to address weaknesses and threats with large-scale coordinated data collection in globally representative populations and leveraging new approaches to collaborative learning.

Protective effect of inhaled corticosteroid on children with asthma with Mycoplasma pneumoniae pneumonia

BACKGROUND

The aim of this study was to determine the differences in the characteristics of Mycoplasma pneumoniae pneumonia (MPP) in children with and without asthma and in children with asthma with and without inhaled corticosteroid (ICS) therapy in order to determine the risk factors for asthma exacerbation and the effect of regular ICS therapy on children with asthma with MPP.

MATERIALS AND METHODS

Children with MPP were divided into two groups according to whether they had a history of asthma. Children with asthma were further divided into an ICS therapy group and a group without ICS therapy. The clinical characteristics, laboratory test results, and pulmonary images were compared between the children with and without asthma. Differences in the severity of acute exacerbation were compared between the children with asthma in the ICS therapy and without ICS therapy groups. Multivariable logistic regression was used to determine the risk factors for exacerbation of MPP in children with asthma.

RESULTS

In children with MPP, the differences in the eosinophil counts; total immunoglobulin E (IgE), C-reactive protein, procalcitonin (PCT), and lactate dehydrogenase (LDH) levels; and fever duration, wheezing, extrapulmonary complications, oxygen saturation < 92%, severe pneumonia, pleural effusion, co-infection with other pathogens, and lobar pneumonia between children with and without asthma were statistically significant. Among children with asthma with MPP, those in the ICS therapy group were less likely to experience an exacerbation, and exacerbations were less severe than those in the without ICS therapy group. The multivariable logistic regression analysis showed that the ICS therapy was an independent protective factor against exacerbation.

CONCLUSION

Among children with MPP, the chance of wheezing was higher in children with asthma than in children without asthma. The ICS therapy was a protective factor against exacerbation in children with asthma with MPP.

Child and caregiver experiences and perceptions of asthma self-management

Asthma is the most common chronic condition of childhood. Self-management is integral to good asthma control. This qualitative paper explores how children with asthma and their parents perceive asthma, their experience with asthma, and how they manage symptoms, preventions and medications within and outside the home. We undertook 15 focus groups with 41 school-aged (6-11 years) children with asthma and 38 parents. Parents and their children attended the same focus groups. We used thematic analysis to analyse the transcripts. Our findings show the impact asthma can have on children's social and emotional wellbeing and highlight how reliant school-aged children are on their parents to effectively manage their asthma. Parents reported being unsure when their child's symptoms warranted visiting their doctor or hospital. Schools were identified as a source of difficulty regarding asthma management; families reported that children may be self-conscious about their asthma and using their inhaler at school. School policies and teachers' lack of asthma knowledge were reported to exacerbate children's reluctance to use their inhaler at school. Our results have implications for the design and implementation of children's self-management interventions for their asthma, particularly when they are at school and away from their parents.