A Lightweight Chronic Obstructive Pulmonary Disease Screening Framework for Individuals with Intellectual Disability

Chronic obstructive pulmonary disease (COPD) is frequently underdetected in individuals with intellectual disability (ID) because cooperation-dependent tests such as spirometry are challenging to perform. The main objective of this study is to develop and validate a lightweight screening model that classifies auscultated lung sounds (LSs) as COPD or healthy for point-of-care triage. Four retrospective LS datasets were used. Fivefold cross-validation (CV) was used to evaluate the model’s performance. Audio was band-pass filtered, denoised, segmented into complete respiratory cycles, and converted to 224 × 224 Mel-spectrograms. A hybrid convolutional neural network–vision transformer (CNN–ViT) extracted both local and global time–frequency features, followed by a fully connected layer that produced a binary probability. Class-weighted loss, balanced mini-batches, and early stopping mitigated imbalance and overfitting. The decision threshold fixed during CV was applied unchanged to the external test set. CV yielded an accuracy of 89.3%, a sensitivity of 89.6%, a specificity of 87.4%, a precision of 91.3%, and an F1-score of 90.4%, with a pooled out-of-fold area under the receiver operating characteristic curve of 0.96. External testing achieved an accuracy of 88.4%, a sensitivity of 89.6%, a specificity of 85.7%, a precision of 93.2%, and an F1-score of 91.4%. The hybrid model outperformed standalone CNN and ViT baselines, as well as re-implemented approaches, under identical preprocessing, splits, and thresholding conditions, while remaining compact and fast. In populations with ID, the proposed approach offers a non-invasive screening option for COPD. With its lightweight design, minimal memory footprint, and low latency, it can be deployed on digital stethoscopes and tablets for real-time triage in community and primary-care settings. This strategy promotes equitable respiratory healthcare for vulnerable groups by facilitating early identification and prompt referral, thereby addressing long-standing diagnostic gaps.