Evidence map›Paper›PMID 34209844›Full record

ArticleDiagnostics (Basel, Switzerland)2021

Automated Radiology Alert System for Pneumothorax Detection on Chest Radiographs Improves Efficiency and Diagnostic Performance.

Cheng-Yi Kao, Chiao-Yun Lin, Cheng-Chen Chao, Han-Sheng Huang, Hsing-Yu Lee, Chia-Ming Chang, Kang Sung, Ting-Rong Chen, Po-Chang Chiang, Li-Ting Huang and 5 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
1.5field-weighted citation impact, top 18% of its field
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

5 citing papers in PubMed, 1 synthesis or guideline pooled it, 13 citations in OpenAlex.

  1. Deep learning for pneumothorax diagnosis: a systematic review and meta-analysis.European respiratory review : an official journal of the European Respiratory Society · 2023
    Pooled it
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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

15 authors at 3 institutions in 1 country.

Cheng-Yi KaoDepartment of Medical Imaging, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, No. 1 University Road, Tainan 704, Taiwan.
Chiao-Yun LinDepartment of Medical Imaging, E-DA Hospital, I-Shou University, No. 1 Yida Road, Jiaosu Village, Yanchao District, Kaohsiung 824, Taiwan.
Cheng-Chen ChaoDepartment of Medical Imaging, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, No. 1 University Road, Tainan 704, Taiwan.
Han-Sheng HuangDepartment of Medical Imaging, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, No. 1 University Road, Tainan 704, Taiwan.
Hsing-Yu LeeDepartment of Medical Imaging, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, No. 1 University Road, Tainan 704, Taiwan.
Chia-Ming ChangDepartment of Medical Imaging, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, No. 1 University Road, Tainan 704, Taiwan.
Kang SungDepartment of Medical Imaging, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, No. 1 University Road, Tainan 704, Taiwan.
Ting-Rong ChenDepartment of Medical Imaging, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, No. 1 University Road, Tainan 704, Taiwan.
Po-Chang ChiangDepartment of Medical Imaging, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, No. 1 University Road, Tainan 704, Taiwan.
Li-Ting HuangDepartment of Medical Imaging, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, No. 1 University Road, Tainan 704, Taiwan.
Bow WangDepartment of Medical Imaging, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, No. 1 University Road, Tainan 704, Taiwan.
Yi-Sheng LiuDepartment of Medical Imaging, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, No. 1 University Road, Tainan 704, Taiwan.
Jung-Hsien ChiangDepartment of Computer Science and Information Engineering, College of Electrical Engineering and Computer Science, National Cheng Kung University, No. 1 University Road, Tainan 704, Taiwan.
Chien-Kuo WangDepartment of Medical Imaging, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, No. 1 University Road, Tainan 704, Taiwan.ORCID 0000-0001-8887-2935
Yi-Shan TsaiDepartment of Medical Imaging, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, No. 1 University Road, Tainan 704, Taiwan.ORCID 0000-0002-0898-9250
National Cheng Kung University Hospital · TWI-Shou University · TWNational Cheng Kung University · TW

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We aimed to set up an Automated Radiology Alert System (ARAS) for the detection of pneumothorax in chest radiographs by a deep learning model, and to compare its efficiency and diagnostic performance with the existing Manual Radiology Alert System (MRAS) at the tertiary medical center. This study retrospectively collected 1235 chest radiographs with pneumothorax labeling from 2013 to 2019, and 337 chest radiographs with negative findings in 2019 were separated into training and validation datasets for the deep learning model of ARAS. The efficiency before and after using the model was compared in terms of alert time and report time. During parallel running of the two systems from September to October 2020, chest radiographs prospectively acquired in the emergency department with age more than 6 years served as the testing dataset for comparison of diagnostic performance. The efficiency was improved after using the model, with mean alert time improving from 8.45 min to 0.69 min and the mean report time from 2.81 days to 1.59 days. The comparison of the diagnostic performance of both systems using 3739 chest radiographs acquired during parallel running showed that the ARAS was better than the MRAS as assessed in terms of sensitivity (recall), area under receiver operating characteristic curve, and F1 score (0.837 vs. 0.256, 0.914 vs. 0.628, and 0.754 vs. 0.407, respectively), but worse in terms of positive predictive value (PPV) (precision) (0.686 vs. 1.000). This study had successfully designed a deep learning model for pneumothorax detection on chest radiographs and set up an ARAS with improved efficiency and overall diagnostic performance.

Indexed as

artificial intelligencedeep learningpneumothoraxRadiology Alert System

Identifiers

PMID34209844
PMCPMC8307391
OpenAlexW3175890950

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.