ReviewJournal of clinical medicine2024
Lung Imaging and Artificial Intelligence in ARDS.
Review in Journal of clinical medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07328997 (Exploration of Diagnosis and Treatment Strategies and Prognostic Prediction Models for Acute Respiratory Distress Syndrome Based on Radiographic Evaluations Assessed by Artificial Intelligence), which is not on this map. Cited by 7 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.
Exploration of Diagnosis and Treatment Strategies and Prognostic Prediction Models for Acute Respiratory Distress Syndrome Based on Radiographic Evaluations Assessed by Artificial Intelligence
Who cites it
7 citing papers in PubMed, 10 citations in OpenAlex.
- Quantitative assessment of lung mechanical properties in ARDS using X-ray computed tomography.Intensive care medicine experimental · 2026Review
- Exploratory association between multimodal AI-derived digital biomarkers and in-hospital mortality in adult patients with pneumonia: A proof-of-concept study.PLOS digital health · 2026Article
- Bedside ventilatory settings guided by respiratory mechanics in acute respiratory distress syndrome.Annals of intensive care · 2025Review
- Artificial intelligence-driven decision support for patients with acute respiratory failure: a scoping review.Intensive care medicine experimental · 2025Review
- Imaging and pulmonary function techniques in ARDS diagnosis and management: current insights and challenges.Critical care (London, England) · 2025Review
- Chest radiographs in acute respiratory distress syndrome: an Achilles' heel of the Berlin criteria?Frontiers in medicine · 2025Article
- Enhancing Lung Ultrasound Diagnostics: A Clinical Study on an Artificial Intelligence Tool for the Detection and Quantification of A-Lines and B-Lines.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
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) can make intelligent decisions in a manner akin to that of the human mind. AI has the potential to improve clinical workflow, diagnosis, and prognosis, especially in radiology. Acute respiratory distress syndrome (ARDS) is a very diverse illness that is characterized by interstitial opacities, mostly in the dependent areas, decreased lung aeration with alveolar collapse, and inflammatory lung edema resulting in elevated lung weight. As a result, lung imaging is a crucial tool for evaluating the mechanical and morphological traits of ARDS patients. Compared to traditional chest radiography, sensitivity and specificity of lung computed tomography (CT) and ultrasound are higher. The state of the art in the application of AI is summarized in this narrative review which focuses on CT and ultrasound techniques in patients with ARDS. A total of eighteen items were retrieved. The primary goals of using AI for lung imaging were to evaluate the risk of developing ARDS, the measurement of alveolar recruitment, potential alternative diagnoses, and outcome. While the physician must still be present to guarantee a high standard of examination, AI could help the clinical team provide the best care possible.
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.