An AI Physical Exam Can Predict Lung Function

Abstract

Abstract Rationale Multimodal clinical audio data, including speech and breath sounds, play a critical role in assessing respiratory function in disorders affecting the thoracic cage, including neurodegenerative, infectious, and respiratory disorders. Machine learning (ML) has shown promise in identifying patterns to enhance predictive accuracy in these disorders due to its data processing power with chronic respiratory conditions that disrupt airflow, such as Chronic Obstructive Pulmonary Disease (COPD) and Interstitial Lung Disease (ILD), being notable examples. This study investigates the feasibility of using an ML model to predict clinical parameters including pulmonary function, smoking status, and disease presence by analyzing breath and vocal characteristics. Methods Two-hundred patients with various respiratory and thoracic pathologies were recruited via convenience sampling from a university medical center. Data were collected using digital stethoscopes and microphones to record breath sounds at eight anatomical locations along with vocal samples during a variety of vocal tasks (Figure 1). Audio data were converted into Mel-spectrograms and used to train a convolutional neural network (CNN) model to predict patient outcomes, including gender, smoking status, COPD, and ILD. The dataset was divided into a 90:10 training-to-validation ratio. Fig. 1. Representation of study workflow showing data collection, data transformation, and subsequent model training. Results In predicting smoking status, breath recordings achieved 80% accuracy, 95% specificity, 63% sensitivity, and an AUC of 0.82 compared to 65%, 69%, 57%, and 0.58 for the voice-based model. In COPD classification, breath data yielded 75% accuracy, 62% specificity, and 80% sensitivity in contrast to 62.5%, 80%, and 57% for the vocal model. In ILD classification, breath data yielded 65% accuracy, 58% specificity, and 75% sensitivity in contrast to 65%, 77%, and 55% for the vocal model. Additionally, in predicting pulmonary function via Forced Vital Capacity (FVC) and the FEV1/FVC ratio, breath sounds had lower mean squared error (MSE) values relative to voice models (0.7 vs. 10.2, and 111.6 vs. 2308.6), demonstrating superior performance. Conclusion Breath sounds offer significant predictive value in identifying pulmonary conditions and estimating functional metrics, outperforming vocal and cough data in most tasks. Given the low cost of obtaining clinical audio data, uncovering reliable biomarkers in audio patterns via ML methods may assist in diagnosis, risk stratification, and identification of patients in whom preventative care may be beneficial. Future work will require larger datasets to improve model robustness and explore more complex biomarker integration, such as combining breath and voice data, for enhanced clinical prediction.

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