Evidence map›Paper›PMID 32524789›Full record

ArticleKorean journal of radiology2020

Assessment of a Deep Learning Algorithm for the Detection of Rib Fractures on Whole-Body Trauma Computed Tomography.

Thomas Weikert, Luca Andre Noordtzij, Jens Bremerich, Bram Stieltjes, Victor Parmar, Joshy Cyriac, Gregor Sommer, Alexander Walter Sauter

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In one paragraph

Article in Korean journal of radiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers, 7 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
49citing papers in PubMed, 7 pooled it
–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

49 citing papers in PubMed, 7 syntheses or guidelines pooled it.

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  9. Pneumothorax and Hemothorax: Practical Approaches for Radiologists.Journal of the Korean Society of Radiology · 2026
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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

8 authors.

Thomas Weikert *Clinic of Radiology and Nuclear Medicine, University Hospital Basel, University of Basel, Basel, Switzerland. thomas.weikert@usb.ch.ORCID 0000-0001-9274-053X
Luca Andre Noordtzij *Clinic of Radiology and Nuclear Medicine, University Hospital Basel, University of Basel, Basel, Switzerland.ORCID 0000-0002-2104-127X
Jens BremerichClinic of Radiology and Nuclear Medicine, University Hospital Basel, University of Basel, Basel, Switzerland.ORCID 0000-0002-1002-8483
Bram StieltjesClinic of Radiology and Nuclear Medicine, University Hospital Basel, University of Basel, Basel, Switzerland.ORCID 0000-0002-5961-802X
Victor ParmarClinic of Radiology and Nuclear Medicine, University Hospital Basel, University of Basel, Basel, Switzerland.ORCID 0000-0003-4802-5402
Joshy CyriacClinic of Radiology and Nuclear Medicine, University Hospital Basel, University of Basel, Basel, Switzerland.ORCID 0000-0002-4584-0623
Gregor SommerClinic of Radiology and Nuclear Medicine, University Hospital Basel, University of Basel, Basel, Switzerland.ORCID 0000-0002-8952-0808
Alexander Walter SauterClinic of Radiology and Nuclear Medicine, University Hospital Basel, University of Basel, Basel, Switzerland.ORCID 0000-0002-6707-2258

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo assess the diagnostic performance of a deep learning-based algorithm for automated detection of acute and chronic rib fractures on whole-body trauma CT. MATERIALS AND

methodsWe retrospectively identified all whole-body trauma CT scans referred from the emergency department of our hospital from January to December 2018 (n = 511). Scans were categorized as positive (n = 159) or negative (n = 352) for rib fractures according to the clinically approved written CT reports, which served as the index test. The bone kernel series (1.5-mm slice thickness) served as an input for a detection prototype algorithm trained to detect both acute and chronic rib fractures based on a deep convolutional neural network. It had previously been trained on an independent sample from eight other institutions (n = 11455).

resultsAll CTs except one were successfully processed (510/511). The algorithm achieved a sensitivity of 87.4% and specificity of 91.5% on a per-examination level [per CT scan: rib fracture(s): yes/no]. There were 0.16 false-positives per examination (= 81/510). On a per-finding level, there were 587 true-positive findings (sensitivity: 65.7%) and 307 false-negatives. Furthermore, 97 true rib fractures were detected that were not mentioned in the written CT reports. A major factor associated with correct detection was displacement.

conclusionWe found good performance of a deep learning-based prototype algorithm detecting rib fractures on trauma CT on a per-examination level at a low rate of false-positives per case. A potential area for clinical application is its use as a screening tool to avoid false-negative radiology reports.

Indexed as

Deep LearningAdultAgedFemaleHumansImage Interpretation, Computer-AssistedMaleMiddle AgedRetrospective StudiesRib FracturesTomography, X-Ray ComputedWhole Body ImagingWounds and InjuriesComputed tomographyComputer-assisted image interpretationDeep learningRib fracturesTrauma

Identifiers

PMID32524789
PMCPMC7289702

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LicenceCC BY-NC
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Registered trials

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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.