Evidence map›Paper›PMID 42471848›Full record

ArticleAcute medicine & surgery

Impact of Artificial Intelligence Assistance on Diagnosing Traumatic Pneumothorax: A Comparison of Specialist and Non-Specialist Emergency Physicians.

Keisuke Suzuki, Kaede Hiruma, Kazuyuki Miyamoto, Miho Saeki, Takahiro Kanai, Yoshitaka Tomita, Kazuki Kikuchi, Gen Inoue, Yuki Kaki, Masaharu Yagi and 2 more

Abstract read
In one paragraph

Article in Acute medicine & surgery. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Keisuke SuzukiDepartment of Emergency, Critical Care and Disaster Medicine Showa Medical University School of Medicine Tokyo Japan.ORCID https://orcid.org/0000-0002-2099-1345
Kaede HirumaDepartment of Emergency, Critical Care and Disaster Medicine Showa Medical University School of Medicine Tokyo Japan.
Kazuyuki MiyamotoDepartment of Emergency, Critical Care and Disaster Medicine Showa Medical University School of Medicine Tokyo Japan.ORCID https://orcid.org/0000-0002-9558-2570
Miho SaekiDivision of Radiology, Department of Radiology Showa Medical University School of Medicine Tokyo Japan.
Takahiro KanaiDivision of Radiology, Department of Radiology Showa Medical University School of Medicine Tokyo Japan.
Yoshitaka TomitaDepartment of Emergency, Critical Care and Disaster Medicine Showa Medical University School of Medicine Tokyo Japan.ORCID https://orcid.org/0009-0008-8262-965X
Kazuki KikuchiDepartment of Emergency, Critical Care and Disaster Medicine Showa Medical University School of Medicine Tokyo Japan.ORCID https://orcid.org/0009-0001-2419-1193
Gen InoueDepartment of Emergency, Critical Care and Disaster Medicine Showa Medical University School of Medicine Tokyo Japan.
Yuki KakiDepartment of Emergency, Critical Care and Disaster Medicine Showa Medical University School of Medicine Tokyo Japan.
Masaharu YagiDepartment of Emergency, Critical Care and Disaster Medicine Showa Medical University School of Medicine Tokyo Japan.ORCID https://orcid.org/0009-0000-4525-9225
Jun SasakiDepartment of Emergency, Critical Care and Disaster Medicine Showa Medical University School of Medicine Tokyo Japan.ORCID https://orcid.org/0009-0003-6188-8568
Kenji DohiDepartment of Emergency, Critical Care and Disaster Medicine Showa Medical University School of Medicine Tokyo Japan.ORCID https://orcid.org/0000-0002-4451-0382

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Supine chest radiography is routinely used in trauma care; however, its sensitivity is limited in pneumothorax detection. Although artificial intelligence (AI) has recently been introduced as a diagnostic support tool for chest radiograph interpretation, its use in trauma radiographs and its interaction with physician experience remain unclear. We aimed to evaluate the diagnostic performance of AI-assisted image interpretation in traumatic pneumothorax detection among emergency medicine specialists and non-specialists. Methods: In this retrospective single-center study, 34 supine chest radiographs (17 pneumothorax and 17 non-pneumothorax cases confirmed with computed tomography) were interpreted by 20 emergency medicine physicians (10 specialists and 10 non-specialists). Each participant reviewed all radiographs twice: first without AI assistance and then with AI assistance after a 2-week washout period. Sensitivity, specificity, accuracy, and precision were calculated. To account for clustering of observations within readers and cases, mixed-effects logistic regression analysis was performed. Results: AI assistance significantly improved sensitivity and diagnostic accuracy across participants, whereas specificity and precision were not significantly affected. Specialists demonstrated higher baseline sensitivity and accuracy than non-specialists. In the mixed-effects logistic regression analysis, AI assistance was independently associated with improved diagnostic accuracy (odds ratio 2.37, Conclusion: AI-assisted interpretation of supine chest radiographs is a potentially useful decision-support tool for detecting traumatic pneumothorax in emergency settings.

Indexed as

artificial intelligence (AI)clinical decision supportemergency medicinepneumothoraxtrauma imaging

Identifiers

PMID42471848
PMCPMC13380316

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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.