Evidence map›Paper›PMID 39017722›Full record

ArticleRadiologie (Heidelberg, Germany)2024

[The possible benefit of artificial intelligence in an organized population-related screening program : Initial results and perspective].

R Morant, A Gräwingholt, J Subelack, D Kuklinski, J Vogel, M Blum, A Eichenberger, A Geissler

Abstract readEnglish Abstract
In one paragraph

Article in Radiologie (Heidelberg, Germany), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–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

1 citing paper in PubMed.

  1. Article
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.

R MorantKrebsliga Ostschweiz, Flurhofstrasse 7, 9000, St. Gallen, Schweiz.
A GräwingholtRadiologie am Theater, 33098, Paderborn, Deutschland.
J SubelackSchool of Medicine, Lehrstuhl für Gesundheitsökonomie, -Politik und -Management, Universität St. Gallen, 9000, St. Gallen, Schweiz.
D KuklinskiSchool of Medicine, Lehrstuhl für Gesundheitsökonomie, -Politik und -Management, Universität St. Gallen, 9000, St. Gallen, Schweiz. david.kuklinski@unisg.ch.
J VogelSchool of Medicine, Lehrstuhl für Gesundheitsökonomie, -Politik und -Management, Universität St. Gallen, 9000, St. Gallen, Schweiz.
M BlumKrebsliga Ostschweiz, Flurhofstrasse 7, 9000, St. Gallen, Schweiz.
A EichenbergerKrebsliga Ostschweiz, Flurhofstrasse 7, 9000, St. Gallen, Schweiz.
A GeisslerSchool of Medicine, Lehrstuhl für Gesundheitsökonomie, -Politik und -Management, Universität St. Gallen, 9000, St. Gallen, Schweiz.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMammography screening programs (MSP) have shown that breast cancer can be detected at an earlier stage enabling less invasive treatment and leading to a better survival rate. The considerable numbers of interval breast cancer (IBC) and the additional examinations required, the majority of which turn out not to be cancer, are critically assessed.

objectiveIn recent years companies and universities have used machine learning (ML) to develop powerful algorithms that demonstrate astonishing abilities to read mammograms. Can such algorithms be used to improve the quality of MSP?

methodThe original screening mammographies of 251 cases with IBC were retrospectively analyzed using the software ProFound AI® (iCAD) and the results were compared (case score, risk score) with a control group. The relevant current literature was also studied.

resultsThe distributions of the case scores and the risk scores were markedly shifted to higher risks compared to the control group, comparable to the results of other studies.

conclusionRetrospective studies as well as our own data show that artificial intelligence (AI) could change our approach to MSP in the future in the direction of personalized screening and could enable a significant reduction in the workload of radiologists, fewer additional examinations and a reduced number of IBCs; however, the results of prospective studies are needed before implementation.

Indexed as

Artificial IntelligenceBreast NeoplasmsEarly Detection of CancerMammographyFemaleHumansMass ScreeningRetrospective StudiesBreast cancerInterval cancerMachine learningMammography screeningSwitzerland

Identifiers

PMID39017722
PMCPMC11422457

What OpenQuestion holds

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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.