Non-invasive Detection of Adenoid Hypertrophy Using Deep Learning Based on Heart-Lung Sounds

Adenoid hypertrophy is one of the most common upper respiratory tract disorders during childhood, leading to a range of symptoms such as nasal congestion, mouth breathing and obstructive sleep apnea. Current diagnostic methods, including computerized tomography scans and nasal endoscopy, are invasive or involve ionizing radiation, rendering them unsuitable for long-term assessments. To address these clinical challenges, this paper proposes a novel deep learning approach for the non-invasive detection of adenoid hypertrophy using heartlung sounds. Firstly, we established a heart-lung sound database with corresponding labels indicating adenoid size. Subsequently, we employed three different deep learning tasks to explore the association between heart-lung sounds and adenoid size. In particular, it includes binary classification to distinguish between normal and abnormal cases, four-grade classification to assess the severity of adenoid hypertrophy, and regression models to predict the actual size of the adenoids. The experimental results demonstrate that the deep learning models can effectively predict the condition of adenoid hypertrophy based on heart-lung sounds. In resource-constrained clinical environments, the proposed methods for adenoid hypertrophy automatic detection provide a simple and non-invasive approach, which can reduce healthcare costs and facilitate remote self-screening.