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Background/Objective <p>Spontaneous 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.</p> Methods <p>We conducted a single-center retrospective observational study of adult patients (≥ 18&#xa0;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&#xa0;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).</p> Results <p>Among 101 patients, mean age was 33.1 ± 14.6&#xa0;years and 89 (88.1%) were male; 53 (52.5%) underwent tube thoracostomy within 24&#xa0;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&#xa0;cm and mean bias −0.51&#xa0;cm (95% limits of agreement, − 2.30 to 1.27&#xa0;cm).</p> Conclusions <p>In 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.</p>

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Chest X-Ray evaluation using GPT for tube thoracostomy or conservative care in non-tension spontaneous pneumothorax

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月17日

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  • Ertuğ Günsoy,
  • Ahmet Aykut,
  • Cem Yıldırım,
  • Mehmet Veysel Öncül,
  • Saim Türkoğlu

摘要

Background/Objective

Spontaneous 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.

Methods

We 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).

Results

Among 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).

Conclusions

In 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.