ReviewDiagnostics (Basel, Switzerland)2023
Acoustic-Based Deep Learning Architectures for Lung Disease Diagnosis: A Comprehensive Overview.
Review in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed.
- Respiratory Disease Detection: A Systematic Review of AI-Based Approaches, from Audio and Visual Unimodal Methods to Multimodal Integration.Diagnostics (Basel, Switzerland) · 2026Review
- Differential diagnosis model for tuberculous and malignant pleural effusion combining U-Net automatic segmentation and deep learning.Frontiers in medicine · 2026Article
- Noise-Resilient Bioacoustics Feature Extraction Methods and Their Implications on Audio Classification Performance: Systematic Review.JMIR biomedical engineering · 2025Review
- A Multimodal AI Framework for Automated Multiclass Lung Disease Diagnosis from Respiratory Sounds with Simulated Biomarker Fusion and Personalized Medication Recommendation.International journal of molecular sciences · 2025Article
- Early Diagnosis of Pneumonia and Chronic Obstructive Pulmonary Disease with a Smart Stethoscope with Cloud Server-Embedded Machine Learning in the Post-COVID-19 Era.Biomedicines · 2025Article
- Machines Are Learning Chest Auscultation. Will They Also Become Our Teachers?CHEST pulmonary · 2024Review
- A Comparative Study of Metaheuristic Feature Selection Algorithms for Respiratory Disease Classification.Diagnostics (Basel, Switzerland) · 2024Article
- Automated Interpretation of Lung Sounds by Deep Learning in Children With Asthma: Scoping Review and Strengths, Weaknesses, Opportunities, and Threats Analysis.Journal of medical Internet research · 2024Article
- A New Shapley-Based Feature Selection Method in a Clinical Decision Support System for the Identification of Lung Diseases.Diagnostics (Basel, Switzerland) · 2023Article
- Multimodal Diagnostics of Changes in Rat Lungs after Vaping.Diagnostics (Basel, Switzerland) · 2023Article
- Article
- ELW-CNN: An extremely lightweight convolutional neural network for enhancing interoperability in colon and lung cancer identification using explainable AI.Healthcare technology lettersArticle
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Lung auscultation has long been used as a valuable medical tool to assess respiratory health and has gotten a lot of attention in recent years, notably following the coronavirus epidemic. Lung auscultation is used to assess a patient's respiratory role. Modern technological progress has guided the growth of computer-based respiratory speech investigation, a valuable tool for detecting lung abnormalities and diseases. Several recent studies have reviewed this important area, but none are specific to lung sound-based analysis with deep-learning architectures from one side and the provided information was not sufficient for a good understanding of these techniques. This paper gives a complete review of prior deep-learning-based architecture lung sound analysis. Deep-learning-based respiratory sound analysis articles are found in different databases including the Plos, ACM Digital Libraries, Elsevier, PubMed, MDPI, Springer, and IEEE. More than 160 publications were extracted and submitted for assessment. This paper discusses different trends in pathology/lung sound, the common features for classifying lung sounds, several considered datasets, classification methods, signal processing techniques, and some statistical information based on previous study findings. Finally, the assessment concludes with a discussion of potential future improvements and recommendations.
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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.