SynthesisEuropean journal of medical research2025
Diagnosis accuracy of machine learning for idiopathic pulmonary fibrosis: a systematic review and meta-analysis.
Synthesis in European journal of medical research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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
5 citing papers in PubMed.
- Artificial intelligence in respiratory medicine: From diagnosis to treatment and future directions.Chinese medical journal pulmonary and critical care medicine · 2026Review
- A systematic review of artificial intelligence-based diagnosis models for idiopathic pulmonary fibrosis.Journal of thoracic disease · 2026Review
- Advances in diagnosis of lung fibrosis: focus on present and future approaches.Frontiers in medicine · 2026Review
- Explainable pulmonary fibrosis detection using edge-strengthened dilated holistic edge detection-based lung segmentation and ResNet-V2 classification.Frontiers in artificial intelligence · 2026Article
- Predicting 30-day in-hospital mortality in ICU asthma patients: a retrospective machine learning study with external validation.BMC pulmonary medicine · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
backgroundThe diagnosis of idiopathic pulmonary fibrosis (IPF) is complex, which requires lung biopsy, if necessary, and multidisciplinary discussions with specialists. Clinical diagnosis of the two ailments is particularly challenging due to the impact of interobserver variability. Several studies have endeavored to utilize image-based machine learning to diagnose IPF and its subtype of usual interstitial pneumonia (UIP). However, the diagnostic accuracy of this approach lacks evidence-based support.
objectiveWe conducted a systematic review and meta-analysis to explore the diagnostic efficiency of image-based machine learning (ML) for IPF. DATA SOURCES AND
methodsWe comprehensively searched PubMed, Cochrane, Embase, and Web of Science databases up to August 24, 2024. During the meta-analysis, we carried out subgroup analyses by imaging source (computed radiography/computed tomography) and modeling type (deep learning/other) to evaluate its diagnostic performance for IPF.
resultsThe meta-analysis findings indicated that in the diagnosis of IPF, the C-index, sensitivity, and specificity of ML were 0.93 (95% CI 0.89-0.97), 0.79 (95% CI 0.73-0.83), and 0.84 (95% CI 0.79-0.88), respectively. The sensitivity of radiologists/clinicians in diagnosing IPF was 0.69 (95% CI 0.56-0.79), with a specificity of 0.93 (95% CI 0.74-0.98). For UIP diagnosis, the C-index of ML was 0.91 (95% CI 0.87-0.94), with a sensitivity of 0.92 (95% CI 0.80-0.97) and a specificity of 0.92 (95%CI 0.82-0.97). In contrast, the sensitivity of radiologists/clinicians in diagnosing UIP was 0.69 (95% CI 0.50-0.84), with a specificity of 0.90 (95% CI 0.82-0.94).
conclusionsImage-based machine learning techniques demonstrate robust data processing and recognition capabilities, providing strong support for accurate diagnosis of idiopathic pulmonary fibrosis and usual interstitial pneumonia. Future multicenter large-scale studies are warranted to develop more intelligent evaluation tools to further enhance clinical diagnostic efficiency. Trial registration This study protocol was registered with PROSPERO (CRD42022383162).
Indexed as
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.