JMIR Publications

ABSTRACT

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:

To objectively review the literature in AI-assisted lung auscultation for pediatric asthma and provide a balanced assessment of its Strengths, Weaknesses, Opportunities and Threats (SWOT).

Methods:

A scoping review of AI-assisted lung sound analysis in children with asthma was conducted across major scientific databases 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 checklist for the assessment of medical AI (ChAMAI).

Results:

Of 82 records retrieved, 7 (8.5%) articles were eligible and included for review. All had poor to medium ChAMAI scores, mostly due to the absence of external validation. Strengths identified were the promise for improved diagnostic accuracy, 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 the use case, where clinicians would 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.