SynthesisSensors (Basel, Switzerland)2024
Respiratory Diseases Diagnosis Using Audio Analysis and Artificial Intelligence: A Systematic Review.
Synthesis in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic accuracy of artificial intelligence for tuberculosis detection from cough sounds: a systematic review and meta-analysis.Frontiers in artificial intelligence · 2026Pooled it
- Applications of artificial intelligence in dyspnoea management for patients with advanced lung cancer: a scoping review protocol.BMJ open · 2026Article
- Beyond fingerprint and voice print: cough sequence sound for identity verification.npj health systems · 2026Article
- Explainable Multi-Modal Deep Learning for Recording-Level Classification of Respiratory Audio Signals Under Internal and Domain-Shift Evaluation.Life (Basel, Switzerland) · 2026Article
- Respiratory Disease Detection: A Systematic Review of AI-Based Approaches, from Audio and Visual Unimodal Methods to Multimodal Integration.Diagnostics (Basel, Switzerland) · 2026Review
- External validation of cough-based algorithms for pulmonary tuberculosis screening from the CODA TB DREAM challenge using cough data from Peru.Scientific reports · 2026Article
- Use of machine learning and voice for multiclass classification of Parkinson's disease, chronic obstructive pulmonary disease, and healthy controls.Scientific reports · 2026Article
- Wheeze detection in real-world pediatric care: AI applied to smartphone lung auscultation.European journal of pediatrics · 2026Observational
- Multi-Stage Respiratory Sound Analysis: Confidence-Driven Wheeze and Crackle Detection.IEEE transactions on bio-medical engineering · 2026Article
- The rise of artificial intelligence in respiratory primary care and pulmonology: a scoping review.NPJ primary care respiratory medicine · 2026Article
- Article
- Artificial Intelligence for the Diagnosis of Respiratory Diseases in Dogs and Cats: A Systematic Review.Veterinary sciences · 2026Review
- ATF-MGIAM: Medically-guided interpretable attention mapping for robust pertussis cough sound recognition.PloS one · 2026Article
- Deep Learning-Driven Early Diagnosis of Respiratory Diseases using CNN-RNN Fusion on Lung Sound Data.Scientific reports · 2025Article
- Review
- Automated Cough Analysis with Convolutional Recurrent Neural Network.Bioengineering (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Respiratory diseases represent a significant global burden, necessitating efficient diagnostic methods for timely intervention. Digital biomarkers based on audio, acoustics, and sound from the upper and lower respiratory system, as well as the voice, have emerged as valuable indicators of respiratory functionality. Recent advancements in machine learning (ML) algorithms offer promising avenues for the identification and diagnosis of respiratory diseases through the analysis and processing of such audio-based biomarkers. An ever-increasing number of studies employ ML techniques to extract meaningful information from audio biomarkers. Beyond disease identification, these studies explore diverse aspects such as the recognition of cough sounds amidst environmental noise, the analysis of respiratory sounds to detect respiratory symptoms like wheezes and crackles, as well as the analysis of the voice/speech for the evaluation of human voice abnormalities. To provide a more in-depth analysis, this review examines 75 relevant audio analysis studies across three distinct areas of concern based on respiratory diseases' symptoms: (a) cough detection, (b) lower respiratory symptoms identification, and (c) diagnostics from the voice and speech. Furthermore, publicly available datasets commonly utilized in this domain are presented. It is observed that research trends are influenced by the pandemic, with a surge in studies on COVID-19 diagnosis, mobile data acquisition, and remote diagnosis systems.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.