ReviewArchives of computational methods in engineering : state of the art reviews2023
Artificial Intelligence Techniques to Predict the Airway Disorders Illness: A Systematic Review.
Review in Archives of computational methods in engineering : state of the art reviews, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed.
- Defining endotypes of bronchopulmonary dysplasia in preterm infants to improve precision-based therapies.JCI insight · 2025Review
- Deep learning-based algorithm for classifying high-resolution computed tomography features in coal workers' pneumoconiosis.Biomedical engineering online · 2025Article
- Lung Cancers Associated with Cystic Airspaces.Cancers · 2025Review
- Predicting Paediatric Brain Disorders from MRI Images Using Advanced Deep Learning Techniques.Neuroinformatics · 2025Article
- Artificial Intelligence in the Management of Asthma: A Review of a New Frontier in Patient Care.Journal of asthma and allergy · 2025Review
- Automating cancer diagnosis using advanced deep learning techniques for multi-cancer image classification.Scientific reports · 2024Article
- Application of Artificial Intelligence in Cone-Beam Computed Tomography for Airway Analysis: A Narrative Review.Diagnostics (Basel, Switzerland) · 2024Review
- Artificial Intelligence-Driven Prognosis of Respiratory Mechanics: Forecasting Tissue Hysteresivity Using Long Short-Term Memory and Continuous Sensor Data.Sensors (Basel, Switzerland) · 2024Article
- The role of pathologists in the diagnosis of occupational lung diseases: an expert opinion of the European Society of Pathology Pulmonary Pathology Working Group.Virchows Archiv : an international journal of pathology · 2024Review
- Enhancing parasitic organism detection in microscopy images through deep learning and fine-tuned optimizer.Scientific reports · 2024Article
- Artificial Intelligence in Chronic Obstructive Pulmonary Disease: Research Status, Trends, and Future Directions --A Bibliometric Analysis from 2009 to 2023.International journal of chronic obstructive pulmonary disease · 2024Article
- A Systematic Review of Artificial Intelligence Applications in the Management of Lung Disorders.Cureus · 2024Review
- A comprehensive retrospect on the current perspectives and future prospects of pneumoconiosis.Frontiers in public health · 2024Review
- Efficient clinical data analysis for prediction of coal workers' pneumoconiosis using machine learning algorithms.The clinical respiratory journal · 2023Article
- Distinguishing infectivity in patients with pulmonary tuberculosis using deep learning.Frontiers in public health · 2023Article
- Predicting Mesothelioma Using Artificial Intelligence: A Scoping Review of Common Models and Applications.Technology in cancer research & treatmentArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Airway disease is a major healthcare issue that causes at least 3 million fatalities every year. It is also considered one of the foremost causes of death all around the globe by 2030. Numerous studies have been undertaken to demonstrate the latest advances in artificial intelligence algorithms to assist in identifying and classifying these diseases. This comprehensive review aims to summarise the state-of-the-art machine and deep learning-based systems for detecting airway disorders, envisage the trends of the recent work in this domain, and analyze the difficulties and potential future paths. This systematic literature review includes the study of one hundred fifty-five articles on airway diseases such as cystic fibrosis, emphysema, lung cancer, Mesothelioma, covid-19, pneumoconiosis, asthma, pulmonary edema, tuberculosis, pulmonary embolism as well as highlights the automated learning techniques to predict them. The study concludes with a discussion and challenges about expanding the efficiency and machine and deep learning-assisted airway disease detection applications.
Identifiers
What OpenQuestion holds
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.