Evidence map›Paper›PMID 32621243›Full record

ArticleEuropean radiology2020

Automated detection of pulmonary embolism in CT pulmonary angiograms using an AI-powered algorithm.

Thomas Weikert, David J Winkel, Jens Bremerich, Bram Stieltjes, Victor Parmar, Alexander W Sauter, Gregor Sommer

Abstract read
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In one paragraph

Article in European radiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 75 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
75citing papers in PubMed, 2 pooled it
9.4field-weighted citation impact, top 1% 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

75 citing papers in PubMed, 2 syntheses or guidelines pooled it, 148 citations in OpenAlex.

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15 more citing papers are in PubMed but not listed here.

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

7 authors at 1 institution in 2 countries.

Thomas WeikertClinic of Radiology & Nuclear Medicine, University Hospital Basel, University of Basel, Petersgraben 4, 4031, Basel, Switzerland. thomas.weikert@usb.ch.ORCID http://orcid.org/0000-0001-9274-053X
David J WinkelClinic of Radiology & Nuclear Medicine, University Hospital Basel, University of Basel, Petersgraben 4, 4031, Basel, Switzerland.
Jens BremerichClinic of Radiology & Nuclear Medicine, University Hospital Basel, University of Basel, Petersgraben 4, 4031, Basel, Switzerland.
Bram StieltjesClinic of Radiology & Nuclear Medicine, University Hospital Basel, University of Basel, Petersgraben 4, 4031, Basel, Switzerland.
Victor ParmarClinic of Radiology & Nuclear Medicine, University Hospital Basel, University of Basel, Petersgraben 4, 4031, Basel, Switzerland.
Alexander W SauterClinic of Radiology & Nuclear Medicine, University Hospital Basel, University of Basel, Petersgraben 4, 4031, Basel, Switzerland.
Gregor SommerClinic of Radiology & Nuclear Medicine, University Hospital Basel, University of Basel, Petersgraben 4, 4031, Basel, Switzerland.
University of Basel · CH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo evaluate the performance of an AI-powered algorithm for the automatic detection of pulmonary embolism (PE) on chest computed tomography pulmonary angiograms (CTPAs) on a large dataset.

methodsWe retrospectively identified all CTPAs conducted at our institution in 2017 (n = 1499). Exams with clinical questions other than PE were excluded from the analysis (n = 34). The remaining exams were classified into positive (n = 232) and negative (n = 1233) for PE based on the final written reports, which defined the reference standard. The fully anonymized 1-mm series in soft tissue reconstruction served as input for the PE detection prototype algorithm that was based on a deep convolutional neural network comprising a Resnet architecture. It was trained and validated on 28,000 CTPAs acquired at other institutions. The result series were reviewed using a web-based feedback platform. Measures of diagnostic performance were calculated on a per patient and a per finding level.

resultsThe algorithm correctly identified 215 of 232 exams positive for pulmonary embolism (sensitivity 92.7%; 95% confidence interval [CI] 88.3-95.5%) and 1178 of 1233 exams negative for pulmonary embolism (specificity 95.5%; 95% CI 94.2-96.6%). On a per finding level, 1174 of 1352 findings marked as embolus by the algorithm were true emboli. Most of the false positive findings were due to contrast agent-related flow artifacts, pulmonary veins, and lymph nodes.

conclusionThe AI prototype algorithm we tested has a high degree of diagnostic accuracy for the detection of PE on CTPAs. Sensitivity and specificity are balanced, which is a prerequisite for its clinical usefulness. KEY POINTS: • An AI-based prototype algorithm showed a high degree of diagnostic accuracy for the detection of pulmonary embolism on CTPAs. • It can therefore help clinicians to automatically prioritize exams with a high suspection of pulmonary embolism and serve as secondary reading tool. • By complementing traditional ways of worklist prioritization in radiology departments, this can speed up the diagnostic and therapeutic workup of patients with pulmonary embolism and help to avoid false negative calls.

Indexed as

Computed Tomography AngiographyDiagnosis, Computer-AssistedTomography, X-Ray ComputedAgedAlgorithmsArtificial IntelligenceContrast MediaFalse Positive ReactionsFemaleHumansImage Processing, Computer-AssistedLungMaleMiddle AgedNeural Networks, ComputerPattern Recognition, AutomatedContrast MediaArtificial intelligenceComputed tomography angiographyComputer-assisted image processingPulmonary embolism

Identifiers

PMID32621243
OpenAlexW3039113409

What OpenQuestion holds

Textmetadata
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.