ReviewCureus2025
Radiomics-Based Artificial Intelligence and Machine Learning Approach for the Diagnosis and Prognosis of Idiopathic Pulmonary Fibrosis: A Systematic Review.
Review in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed.
- A systematic review of artificial intelligence-based diagnosis models for idiopathic pulmonary fibrosis.Journal of thoracic disease · 2026Review
- A transfer learning-based multimodal model for early prediction of 90-day respiratory failure in dermatomyositis-associated interstitial lung disease.Frontiers in immunology · 2026Article
- A biomarker-guided framework for corticosteroid treatment stratification in acute exacerbation of idiopathic pulmonary fibrosis.Frontiers in pharmacology · 2026Article
- Research on the role and mechanisms of Cystatin 6 in disease pathosis and development.Frontiers in molecular biosciences · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Idiopathic pulmonary fibrosis (IPF) is a devastating interstitial lung disease (ILD) characterized by progressive fibrosis and poor survival outcomes. Accurate diagnosis and prognosis remain challenging due to overlapping features with other ILDs and variability in imaging interpretation. This systematic review evaluates the current evidence on artificial intelligence (AI) and machine learning (ML) applications for the diagnosis and prognosis of IPF using computed tomography (CT) imaging. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, eight studies published between 2017 and 2024 were included, demonstrating promising results across various methodologies, including deep learning (DL) models, support vector machines (SVMs), and ensemble approaches. AI-derived parameters, particularly measures of fibrotic burden and pulmonary vascular volume, consistently outperformed conventional visual CT scores for prognostication. Strong correlations between AI-quantified CT features and pulmonary function (PF) tests suggest potential surrogate markers for physiological parameters. Novel prognostic biomarkers identified through AI analysis expand understanding beyond traditional parenchymal assessment. Despite these advances, limitations include retrospective designs, sample size constraints, male-predominant cohorts, and limited external validation. Future research should prioritize large, prospective, multi-center studies with diverse populations, standardized protocols, explainable AI (XAI) techniques, and integration into clinical workflows to realize the transformative potential of AI for improving IPF management.
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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.