Background <p>Seeking to develop technology for medical countermeasures in novel infectious pandemics, this funded project intended to use mobile phone auscultation (MPA) to enable frontline detection of respiratory disease for situations with limited diagnostic options. The overall objective of this research was to determine the feasibility of obtaining mobile phone recordings in Emergency Departments with the intention of creating accurate models of pneumonia diagnosis.</p> Methods <p>This prospective study enrolled participants in five predetermined cohorts: influenza A, pneumonia, acute bronchitis, other respiratory illness, and controls. Potential subjects were approached in one of three emergency departments (two urban and one rural). Recordings were obtained with mobile phones with no hardware modifications. Recordings sampled bilateral 5th intercostal sites during normal and deep respiration, along with one supraclavicular fossa egophonic recording. Recordings were analyzed with computational nonlinear dynamics which, when applied to auscultation data, represent biofluid dynamics. Maximal Lyapunov Exponent (MLE) and Correlation Dimension (Dcorr) were evaluated to confirm the presence of low dimensional chaos and applicability of Time Series Dynamics (TSD) modeling. Train and test sets were created by 80/20 random sampling of clustered records. TSD models were fitted separately to recordings and then combined into a single “composite” model. Binary classifiers were fitted from extracted features by using logistic regression.</p> Results <p>From Nov 21, 2023 to June 12, 2024, 292 subjects were enrolled (64 pneumonia, 59 influenza A, 38 acute bronchitis, 68 other respiratory, and 63 controls) with a mean age of 49 years (SD 17.2). Half of the subjects were male, 68.0% white and 29.2% black. Compared to all others, pneumonia subjects were older (58 years vs. 46 years), more likely male (56% vs. 48%), more likely white (77% vs. 66%), and more likely to have a tobacco history (77% vs. 68%). No completed recordings were excluded from the recording analysis. TSD modeling of egophonic, right normal breathing (RNB), and composite models all produced accurate results. In test analyses, the RNB (sens 85%, spec 81%) and egophonic (sens 85%, spec 86%) models performed best. The composite model achieved sensitivity 91%, specificity 87%, and area under the curve of 89%. The test models yielded one false negative and seven false positives.</p> Conclusion <p>This study found that modeling of mobile phone auscultation recordings yielded excellent sensitivity, specificity, and area under the curve to delineate pneumonia from other respiratory illness and controls. Similar models could increase access to accurate diagnosis of multiple medical conditions, particularly for medical counter measures during disease outbreaks. Phase 2 will further characterize models for deployment in larger populations. </p>