ReviewRespiratory research2024
Using machine learning for early detection of chronic obstructive pulmonary disease: a narrative review.
Review in Respiratory research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Economic evaluations of screening and case-finding for Chronic Obstructive Pulmonary Disease (COPD): a systematic review.NPJ primary care respiratory medicine · 2026Pooled it
- Characteristics of Respiratory Microbiome in COPD-A Literature Review.Advances in respiratory medicine · 2026Review
- Hybrid deep learning approach for early emphysema diagnosis combining fuzzy C-means, TransUNet, and faster mask R-CNN.Scientific reports · 2026Article
- Interpretable machine learning methods based on oscillometry and electric modeling for the diagnostic of respiratory dysfunction in silicosis.BMC medical informatics and decision making · 2026Article
- Are Real-World Mobility Patterns Early Indicators of COPD Onset? Insights from Wrist-Worn Sensors.International journal of chronic obstructive pulmonary disease · 2026Article
- Development and Validation of an Electronic Health Record Algorithm to Predict the Presence of Chronic Obstructive Pulmonary Disease.International journal of chronic obstructive pulmonary disease · 2026Article
- Machine learning models using dual-phase CT radiomics for early detection of PRISm.Scientific reports · 2025Article
- Explainable machine learning-driven models for predicting Parkinson's disease and its prognosis: obesity patterns associations and models development using NHANES 1999-2018 data.Lipids in health and disease · 2025Article
- Vowel segmentation impact on machine learning classification for chronic obstructive pulmonary disease.Scientific reports · 2025Observational
- Developing an interpretable machine learning predictive model of chronic obstructive pulmonary disease by serum PFAS concentration.Frontiers in public health · 2025Article
- Machine Learning for Enhanced COPD Diagnosis: A Comparative Analysis of Classification Algorithms.Diagnostics (Basel, Switzerland) · 2024Article
- Demographic and Clinical Characteristics of Mild, Young and Early COPD: A Cross-Sectional Analysis of 5468 Patients.Journal of clinical medicine · 2024Article
- Acoustic and Clinical Data Analysis of Vocal Recordings: Pandemic Insights and Lessons.Diagnostics (Basel, Switzerland) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Chronic obstructive pulmonary disease (COPD) is a prevalent respiratory disease and ranks third in global mortality rates, imposing a significant burden on patients and society. This review looks at recent research, both domestically and abroad, on the application of machine learning (ML) for early COPD screening. The review discusses the practical application, key optimization points, and prospects of ML techniques in early COPD screening. The aim is to establish a scientific foundation and reference framework for future research and the development of screening strategies.
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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.