Evidence map›Paper›PMID 37938386›Full record

ArticleEuropean radiology2024

Population-wide evaluation of artificial intelligence and radiologist assessment of screening mammograms.

Johanne Kühl, Mohammad Talal Elhakim, Sarah Wordenskjold Stougaard, Benjamin Schnack Brandt Rasmussen, Mads Nielsen, Oke Gerke, Lisbet Brønsro Larsen, Ole Graumann

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In one paragraph

Article in European radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 2 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

9 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Artificial intelligence in mammography: a systematic review of the external validation.Revista brasileira de ginecologia e obstetricia : revista da Federacao Brasileira das Sociedades de Ginecologia e Obstetricia · 2024
    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

8 authors.

Johanne KühlDepartment of Clinical Research, University of Southern Denmark, Kløvervænget 10, 2ndfloor, 5000, Odense C, Denmark.
Mohammad Talal ElhakimDepartment of Clinical Research, University of Southern Denmark, Kløvervænget 10, 2ndfloor, 5000, Odense C, Denmark. mte@rsyd.dk.ORCID http://orcid.org/0000-0002-2673-2352
Sarah Wordenskjold StougaardDepartment of Clinical Research, University of Southern Denmark, Kløvervænget 10, 2ndfloor, 5000, Odense C, Denmark.
Benjamin Schnack Brandt RasmussenDepartment of Clinical Research, University of Southern Denmark, Kløvervænget 10, 2ndfloor, 5000, Odense C, Denmark.
Mads NielsenDepartment of Computer Science, University of Copenhagen, Universitetsparken 1, 2100, Copenhagen, Denmark.
Oke GerkeDepartment of Clinical Research, University of Southern Denmark, Kløvervænget 10, 2ndfloor, 5000, Odense C, Denmark.
Lisbet Brønsro LarsenDepartment of Radiology, Odense University Hospital, Kløvervænget 47, Ground Floor, 5000, Odense C, Denmark.
Ole GraumannDepartment of Clinical Research, University of Southern Denmark, Kløvervænget 10, 2ndfloor, 5000, Odense C, Denmark.

Funding

Region Syddanmark 10240300
6 · The paper itself

Abstract

objectivesTo validate an AI system for standalone breast cancer detection on an entire screening population in comparison to first-reading breast radiologists. MATERIALS AND

methodsAll mammography screenings performed between August 4, 2014, and August 15, 2018, in the Region of Southern Denmark with follow-up within 24 months were eligible. Screenings were assessed as normal or abnormal by breast radiologists through double reading with arbitration. For an AI decision of normal or abnormal, two AI-score cut-off points were applied by matching at mean sensitivity (AI

resultsThe sample included 249,402 screenings (149,495 women) and 2033 breast cancers (72.6% screen-detected cancers, 27.4% interval cancers). AI

conclusionStandalone AI can detect breast cancer at an accuracy level equivalent to the standard of first readers when the AI threshold point was matched at first reader specificity. However, AI and first readers detected a different composition of cancers. CLINICAL RELEVANCE STATEMENT: Replacing first readers with AI with an appropriate cut-off score could be feasible. AI-detected cancers not detected by radiologists suggest a potential increase in the number of cancers detected if AI is implemented to support double reading within screening, although the clinicopathological characteristics of detected cancers would not change significantly. KEY POINTS: • Standalone AI cancer detection was compared to first readers in a double-read mammography screening population. • Standalone AI matched at first reader specificity showed no statistically significant difference in overall accuracy but detected different cancers. • With an appropriate threshold, AI-integrated screening can increase the number of detected cancers with similar clinicopathological characteristics.

Indexed as

Artificial IntelligenceBreast NeoplasmsEarly Detection of CancerMammographyRadiologistsSensitivity and SpecificityAgedDenmarkFemaleHumansMass ScreeningMiddle AgedArtificial intelligenceBreast cancerMammographyScreening

Identifiers

PMID37938386
PMCPMC11166831

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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.