Observational studyScandinavian journal of trauma, resuscitation and emergency medicine2026
Chest X-Ray evaluation using GPT for tube thoracostomy or conservative care in non-tension spontaneous pneumothorax.
Observational study in Scandinavian journal of trauma, resuscitation and emergency medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Artificial intelligence as a second reader for pneumothorax detection: redirecting rather than reducing chest CT use.BMC emergency medicine · 2026Article
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5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND/
objectiveSpontaneous pneumothorax (SP) commonly presents to the emergency department (ED), and clinicians must rapidly decide between conservative care and tube thoracostomy. Most artificial intelligence tools focus on pneumothorax detection rather than quantitative size estimation and actionable management recommendations. We aimed to evaluate whether a vision-enabled generative pre-trained transformer (GPT) model could estimate apical SP depth on chest radiographs (CXRs) and predict initial ED management.
methodsWe conducted a single-center retrospective observational study of adult patients (≥ 18 years) with confirmed SP on pre-intervention posteroanterior CXR (January 1, 2023-December 31, 2025). GPT received only the CXR plus age and sex and returned laterality, estimated apical depth (Depth_cm), and a binary management recommendation (tube thoracostomy vs conservative). The reference outcome was tube thoracostomy within 24 h. We calculated diagnostic performance metrics and area under the receiver operating characteristic curve (AUC). We assessed agreement between GPT depth estimates and blinded radiologist measurements using intraclass correlation coefficient (ICC), Bland-Altman analysis, and mean absolute error (MAE).
resultsAmong 101 patients, mean age was 33.1 ± 14.6 years and 89 (88.1%) were male; 53 (52.5%) underwent tube thoracostomy within 24 h. GPT showed sensitivity 86.8% (95% CI, 75.2%-93.5%), specificity 93.8% (95% CI, 83.2%-97.9%), and accuracy 90.1% (95% CI, 82.7%-94.5%), with Cohen's κ = 0.80 and AUC 0.90 (95% CI, 0.84-0.96). Depth agreement was strong (ICC, 0.893), with MAE 0.69 cm and mean bias -0.51 cm (95% limits of agreement, - 2.30 to 1.27 cm).
conclusionsIn confirmed SP, a vision-enabled GPT model produced apical depth estimates that closely agreed with radiologist measurements and generated management recommendations that substantially matched real-world ED decisions, supporting its potential role as adjunct imaging decision support.
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