Evidence map›Paper›PMID 32076660›Full record

ArticleRadiology. Artificial intelligence2019

Improving Accuracy and Efficiency with Concurrent Use of Artificial Intelligence for Digital Breast Tomosynthesis.

Emily F Conant, Alicia Y Toledano, Senthil Periaswamy, Sergei V Fotin, Jonathan Go, Justin E Boatsman, Jeffrey W Hoffmeister

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

Article in Radiology. Artificial intelligence, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 88 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
88citing papers in PubMed, 4 pooled it
18.3field-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

88 citing papers in PubMed, 4 syntheses or guidelines pooled it, 228 citations in OpenAlex.

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

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors at 1 institution in 1 country.

Emily F ConantDepartment of Radiology, Perelman School of Medicine at the University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104 (E.F.C.); Biostatistics Consulting, Kensington, Md (A.Y.T.); iCAD, Nashua, NH (S.P., S.V.F., J.G., J.W.H.); and Intrinsic Imaging, Bolton, Mass (J.E.B.).ORCID 0000-0001-9331-644X
Alicia Y ToledanoDepartment of Radiology, Perelman School of Medicine at the University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104 (E.F.C.); Biostatistics Consulting, Kensington, Md (A.Y.T.); iCAD, Nashua, NH (S.P., S.V.F., J.G., J.W.H.); and Intrinsic Imaging, Bolton, Mass (J.E.B.).ORCID 0000-0003-3153-2002
Senthil PeriaswamyDepartment of Radiology, Perelman School of Medicine at the University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104 (E.F.C.); Biostatistics Consulting, Kensington, Md (A.Y.T.); iCAD, Nashua, NH (S.P., S.V.F., J.G., J.W.H.); and Intrinsic Imaging, Bolton, Mass (J.E.B.).ORCID 0000-0001-5064-6174
Sergei V FotinDepartment of Radiology, Perelman School of Medicine at the University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104 (E.F.C.); Biostatistics Consulting, Kensington, Md (A.Y.T.); iCAD, Nashua, NH (S.P., S.V.F., J.G., J.W.H.); and Intrinsic Imaging, Bolton, Mass (J.E.B.).ORCID 0000-0002-0419-8777
Jonathan GoDepartment of Radiology, Perelman School of Medicine at the University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104 (E.F.C.); Biostatistics Consulting, Kensington, Md (A.Y.T.); iCAD, Nashua, NH (S.P., S.V.F., J.G., J.W.H.); and Intrinsic Imaging, Bolton, Mass (J.E.B.).
Justin E BoatsmanDepartment of Radiology, Perelman School of Medicine at the University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104 (E.F.C.); Biostatistics Consulting, Kensington, Md (A.Y.T.); iCAD, Nashua, NH (S.P., S.V.F., J.G., J.W.H.); and Intrinsic Imaging, Bolton, Mass (J.E.B.).
Jeffrey W HoffmeisterDepartment of Radiology, Perelman School of Medicine at the University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104 (E.F.C.); Biostatistics Consulting, Kensington, Md (A.Y.T.); iCAD, Nashua, NH (S.P., S.V.F., J.G., J.W.H.); and Intrinsic Imaging, Bolton, Mass (J.E.B.).ORCID 0000-0002-8203-6589
University of Pennsylvania · US

Funding

Penn Center for Innovation in Personalized Breast ScreeningU54CA163313 · NCI · UNIVERSITY OF PENNSYLVANIA · PI ARMSTRONG, KATRINA, SCHNALL, MITCHELL D. · 2011 to 2017
$7.4M
NCI NIH HHS U54 CA163313
6 · The paper itself

Abstract

purposeTo evaluate the use of artificial intelligence (AI) to shorten digital breast tomosynthesis (DBT) reading time while maintaining or improving accuracy. MATERIALS AND

methodsA deep learning AI system was developed to identify suspicious soft-tissue and calcified lesions in DBT images. A reader study compared the performance of 24 radiologists (13 of whom were breast subspecialists) reading 260 DBT examinations (including 65 cancer cases) both with and without AI. Readings occurred in two sessions separated by at least 4 weeks. Area under the receiver operating characteristic curve (AUC), reading time, sensitivity, specificity, and recall rate were evaluated with statistical methods for multireader, multicase studies.

resultsRadiologist performance for the detection of malignant lesions, measured by mean AUC, increased 0.057 with the use of AI (95% confidence interval [CI]: 0.028, 0.087;

conclusionThe concurrent use of an accurate DBT AI system was found to improve cancer detection efficacy in a reader study that demonstrated increases in AUC, sensitivity, and specificity and a reduction in recall rate and reading time.© RSNA, 2019See also the commentary by Hsu and Hoyt in this issue.

Identifiers

PMID32076660
PMCPMC6677281
OpenAlexW2966665347

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.