Feasibility of detecting aortic stenosis with mobile phone auscultation data: a pilot study

Document Type

Article

Publication Date

3-2026

Institution/Department

Cardiology; Internal Medicine

Journal Title

Frontiers in cardiovascular medicine

Abstract

BACKGROUND: The prevalence of valvular heart disease is increasing. Early detection remains poor as screening relies on front line detection of audible or symptomatic disease and confirmation requires specialized echocardiography. METHODS: We conducted a single center, observational pilot study. Eligible subjects were stratified into groups based on echocardiographic findings. In addition to chart extraction of demographics, medical history, and echocardiographic parameters, each subject underwent three auscultation recordings that were analyzed via computational nonlinear dynamics to extract features and construct predictors without fitting or weighting. Predictors were used to create logistic regression binary classification models. Training and test set performance was reported for each model with a focus on area-under-the-curve and sensitivity as the primary benchmarks. RESULTS: We analyzed the recordings of 248 subjects, median age 73 years, 43.6% female, 99% White. All recordings were chaotic and of low dimensionality. Personnel and subject collected recordings had a normalized mutual information entropy of 1.0, indicating they shared the same information and could be interchangeable for model development. Three models for aortic stenosis met predetermined metrics, with the best performing model reporting an AUC of 0.872 and a sensitivity of 0.923. Mitral regurgitation models were explored but limited by sample size. CONCLUSIONS: This study established the feasibility of two innovative approaches, by combining the sound recordings collected from unmodified mobile phones with analysis via nonlinear dynamics software. This work has the potential to improve valvular heart disease detection by overcoming barriers that remain for current standards of care and emerging artificial intelligence solutions.

ISSN

2297-055X

Comments

Kailey Kowalski- Resident

First Page

1768473

Link to Full Text

Share

COinS