ReviewERJ open research2023
Exploring computer-based imaging analysis in interstitial lung disease: opportunities and challenges.
Review in ERJ open research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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
14 citing papers in PubMed, 23 citations in OpenAlex.
- e-Lung computed tomography biomarkers are associated with outcomes in fibrotic interstitial lung diseases.ERJ open research · 2026Article
- Quantitative spectral computed tomography biomarkers for idiopathic inflammatory myopathy-associated interstitial lung disease: a dual-layer detector computed tomography study.Quantitative imaging in medicine and surgery · 2026Article
- AI-based chest CT quantification of interstitial lung disease in idiopathic inflammatory myopathies: agreement with expert visual assessment in 107 patients.BMC pulmonary medicine · 2026Article
- A transfer learning-based multimodal model for early prediction of 90-day respiratory failure in dermatomyositis-associated interstitial lung disease.Frontiers in immunology · 2026Article
- Artificial Intelligence Enabled Lung Sound Auscultation in the Early Diagnosis and Subtyping of Interstitial Lung Disease.Journal of clinical medicine · 2025Review
- A narrative review of the early diagnosis and treatment of idiopathic pulmonary fibrosis with lung cancer.Journal of thoracic disease · 2025Review
- Prognostic value of pulmonary vessel-related structures in rapid progression of idiopathic inflammatory myopathy-associated interstitial lung disease: a retrospective study from two centres.BMJ open respiratory research · 2025Article
- Radiomics and Artificial Intelligence in Pulmonary Fibrosis.Journal of imaging informatics in medicine · 2025Review
- Integrating Radiomics Signature into Clinical Pathway for Patients with Progressive Pulmonary Fibrosis.Diagnostics (Basel, Switzerland) · 2025Review
- Review
- High resolution computed tomography in systemic sclerosis: From diagnosis to follow-up.Rheumatology and immunology research · 2024Article
- [Interstitial lung abnormalities : What the radiologist needs to know].Radiologie (Heidelberg, Germany) · 2024Article
- Biomarkers in idiopathic pulmonary fibrosis: Current insight and future direction.Chinese medical journal pulmonary and critical care medicine · 2024Article
- Advancing Drug Development in Idiopathic Pulmonary Fibrosis: Tomorrow Is Now.American journal of respiratory and critical care medicine · 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 at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The advent of quantitative computed tomography (QCT) and artificial intelligence (AI) using high-resolution computed tomography data has revolutionised the way interstitial diseases are studied. These quantitative methods provide more accurate and precise results compared to prior semiquantitative methods, which were limited by human error such as interobserver disagreement or low reproducibility. The integration of QCT and AI and the development of digital biomarkers has facilitated not only diagnosis but also prognostication and prediction of disease behaviour, not just in idiopathic pulmonary fibrosis in which they were initially studied, but also in other fibrotic lung diseases. These tools provide reproducible, objective prognostic information which may facilitate clinical decision-making. However, despite the benefits of QCT and AI, there are still obstacles that need to be addressed. Important issues include optimal data management, data sharing and maintenance of data privacy. In addition, the development of explainable AI will be essential to develop trust within the medical community and facilitate implementation in routine clinical practice.
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.