AI-Powered Acoustic Monitoring for Digital Health Applications

Dear Colleagues,

AI-powered acoustic monitoring represents a revolutionary approach to healthcare that harnesses the diagnostic power of sound through advanced artificial intelligence technologies. This emerging field recognizes that human bodies naturally produce acoustic signatures—from heartbeats and breathing patterns to coughs and vocal characteristics—that contain rich information about health status and disease progression.

Traditional healthcare relies heavily on episodic clinical encounters and invasive diagnostic procedures. In contrast, acoustic monitoring enables continuous, non-invasive health assessment using readily available devices like smartphones and wearables. Machine learning algorithms can now detect subtle acoustic patterns invisible to human perception, identifying early signs of respiratory infections, cardiovascular abnormalities, neurological conditions, and mental health changes.

This technology's transformative potential lies in its accessibility and scalability. Unlike expensive medical equipment requiring specialized facilities, acoustic monitoring can reach underserved populations and remote communities through ubiquitous consumer devices. This democratization of healthcare monitoring is particularly valuable for chronic disease management, epidemic surveillance, and preventive care.

Current applications span diverse medical domains, from tuberculosis screening in developing countries to COVID-19 detection and elderly care monitoring. As AI algorithms become more sophisticated and datasets expand, acoustic monitoring promises to shift healthcare from reactive treatment to proactive prevention, enabling early intervention when treatments are most effective and cost-efficient, ultimately transforming how we approach global health challenges.

We welcome original research with potential topics including, but not limited to, the following:

• acoustic biomarker identification, where respiratory sounds, coughs, and speech patterns serve as health indicators;
• novel AI architectures for continuous acoustic monitoring;
• tuberculosis screening via cough pattern analysis;
• speech analysis for diagnostics;
• Parkinson’s disease screening via speech pattern analysis;
• COVID-19 detection through breath, speech, and cough sound classification;
• cardiovascular assessment using automated heart sound interpretation;
• healthcare accessibility through low-cost device (e.g., smartphones) acoustic analysis;
• remote diagnostic capabilities for underserved populations.
• machine learning algorithm development for
  • acoustic pattern recognition;
  • signal processing innovations;
  • real-world deployment challenges;
• predictive analytics applications;
• interdisciplinary contributions addressing
  • regulatory frameworks;
  • privacy protection mechanisms;
  • ethical considerations in acoustic health monitoring;
• clinical validation studies;
• cost-effectiveness analyses;

This Special Issue aims to establish acoustic monitoring as a cornerstone of next-generation digital health infrastructure. We invite researchers from computer science, biomedical engineering, clinical medicine, and public health to contribute to this rapidly evolving field that promises more accessible, proactive, and personalized healthcare delivery.

We look forward to receiving your contributions.

Dr. George P. Kafentzis
Prof. Dr. Manolis Tsiknakis
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• acoustics
• healthcare
• AI
• machine learning
• monitoring
• disease classification
• digital health
• speech
• signals
• respiratory system