Evidence map›Paper›PMID 41807842›Full record

ArticleEmergency radiology2026

Performance of an artificial intelligence-based software in detecting pneumothorax on supine chest radiographs: a retrospective study.

Hitomi Nakamura, Tomoki Wada, Ryota Inokuchi, Shouhei Hanaoka, Naoya Sakamoto, Kent Doi

Abstract read
In one paragraph

Article in Emergency radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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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.

2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Hitomi NakamuraDepartment of Emergency and Critical Care Medicine, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-0033, Japan.
Tomoki WadaDepartment of Emergency and Critical Care Medicine, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-0033, Japan. towada-tky@umin.ac.jp.
Ryota InokuchiDepartment of Emergency and Critical Care Medicine, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-0033, Japan.
Shouhei HanaokaDepartment of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-0033, Japan.
Naoya SakamotoDepartment of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-0033, Japan.
Kent DoiDepartment of Emergency and Critical Care Medicine, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-0033, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo evaluate the diagnostic performance of artificial intelligence (AI)-based software for pneumothorax detection on supine radiographs and its impact on physicians’ interpretation.

methodsThis single-center retrospective study analyzed 114 hemithoraces with pneumothorax and 340 without, using computed tomography as the reference standard. We evaluated the performance of CXR-AID, an AI-based software, for pneumothorax detection on supine chest radiographs, and conducted a reader study to assess the utility of AI assistance. Sensitivities and specificities were adjusted for clustering within patients.

resultsSensitivity and specificity of the AI in detecting overall pneumothorax on a supine chest radiograph were 61.0% (95% confidence interval [CI], 50.6%–70.4%) and 94.3% (95% CI, 90.8%–96.5%), respectively. Sensitivity of the AI in detecting a large pneumothorax with a maximum radial interpleural distance > 35 mm was 97.4% (95% CI, 83.6%–99.6%). Sensitivity was significantly higher in the upper lung zone than in the lower lung zone (69.5% [95% CI, 59.3%–78.1%] vs. 37.5% [95% CI, 27.3%–48.8%]). In the reader study, the AI significantly improved resident sensitivity (46.8% to 57.3%, P < 0.001). For experts, the AI did not improve sensitivity significantly (P = 0.32) but significantly improved specificity (90.9% to 95.6%, P = 0.02).

conclusionsThe AI demonstrated high sensitivity for detecting large pneumothoraces on supine radiographs, helping identify patients requiring tube thoracotomy. It may serve as a diagnostic safety net for residents by increasing sensitivity and enhances experts’ diagnostic confidence by improving specificity. However, pneumothorax detection in the lower lung zone remains challenging.

Indexed as

Artificial IntelligencePneumothoraxRadiographic Image Interpretation, Computer-AssistedRadiography, ThoracicSoftwareAdultAgedAged, 80 and overFemaleHumansIntelligent SystemsMaleMiddle AgedRetrospective StudiesSensitivity and SpecificitySupine PositionArtificial intelligenceComputed tomographyPneumothoraxRadiograph

Identifiers

PMID41807842
PMCPMC13079510

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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.