SynthesisEuropean respiratory review : an official journal of the European Respiratory Society2023
Deep learning for pneumothorax diagnosis: a systematic review and meta-analysis.
Synthesis in European respiratory review : an official journal of the European Respiratory Society, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 38 citations in OpenAlex.
- Comparing the Diagnostic Performance of Lung Ultrasonography and Chest Radiography for Detecting Pneumothorax in Patients with Trauma: A Meta-Analysis.Respiration; international review of thoracic diseases · 2025Pooled it
- Development and evaluation of a deep learning model for computer-aided diagnosis of neonatal pneumothorax on chest radiographs.Pediatric radiology · 2026Article
- Large-scale evaluation of multimodal large language models for pneumothorax detection.Diagnostic and interventional radiology (Ankara, Turkey) · 2026Article
- Pneumothorax and Hemothorax: Practical Approaches for Radiologists.Journal of the Korean Society of Radiology · 2026Review
- Recent advances in artificial intelligence across interventional pulmonology: a narrative review.Journal of thoracic disease · 2026Review
- Severe trauma care: advances and future directions in diagnostic and therapeutic techniques and information technology support.Medical review (2021) · 2026Article
- Explainable transfer learning ensemble AI model for lung ultrasound pneumothorax detection with expert benchmark.Scandinavian journal of trauma, resuscitation and emergency medicine · 2026Article
- Artificial Intelligence in Pleural Diseases: Current Applications and Next Steps.Thoracic research and practice · 2026Article
- Diagnostic Performance of ChatGPT-5 for Detecting Pediatric Pneumothorax on Chest Radiographs: A Multi-Prompt Evaluation.Diagnostics (Basel, Switzerland) · 2026Article
- Evaluation of the effectiveness of the ChatGPT artificial intelligence application in the diagnosis of spontaneous pneumothorax on chest radiograph interpretation.BMC pulmonary medicine · 2025Article
- Impact of AI Assistance in Pneumothorax Detection on Chest Radiographs Among Readers of Varying Experience.Diagnostics (Basel, Switzerland) · 2025Article
- External Validation of an Upgraded AI Model for Screening Ileocolic Intussusception Using Pediatric Abdominal Radiographs: Multicenter Retrospective Study.Journal of medical Internet research · 2025Article
- Multidisciplinary Evaluation of an AI-Based Pneumothorax Detection Model: Clinical Comparison with Physicians in Edge and Cloud Environments.Journal of multidisciplinary healthcare · 2025Article
- Factors for increasing positive predictive value of pneumothorax detection on chest radiographs using artificial intelligence.Scientific reports · 2024Article
- Automatic detection and visualization of temporomandibular joint effusion with deep neural network.Scientific reports · 2024Article
- Digital health delivery in respiratory medicine: adjunct, replacement or cause for division?European respiratory review : an official journal of the European Respiratory Society · 2024Review
- Radiographic chest wall abnormalities in primary spontaneous pneumothorax identified by artificial intelligence.Heliyon · 2024Article
- AI tools in Emergency Radiology reading room: a new era of Radiology.Emergency radiology · 2023Review
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 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDeep learning (DL), a subset of artificial intelligence (AI), has been applied to pneumothorax diagnosis to aid physician diagnosis, but no meta-analysis has been performed.
methodsA search of multiple electronic databases through September 2022 was performed to identify studies that applied DL for pneumothorax diagnosis using imaging. Meta-analysis
resultsIn 56 of the 63 primary studies, pneumothorax was identified from chest radiography. The total AUC was 0.97 (95% CI 0.96-0.98) for both DL and physicians. The total pooled sensitivity was 84% (95% CI 79-89%) for DL and 85% (95% CI 73-92%) for physicians and the pooled specificity was 96% (95% CI 94-98%) for DL and 98% (95% CI 95-99%) for physicians. More than half of the original studies (57%) had a high risk of bias.
conclusionsOur review found the diagnostic performance of DL models was similar to that of physicians, although the majority of studies had a high risk of bias. Further pneumothorax AI research is needed.
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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.