Evidence map›Paper›PMID 32638030›Full record

ReviewDer Radiologe2020

[Artificial intelligence-based algorithms : Decision-making support for computed tomography of the chest].

Panagiota Manava, Marco Galster, Henrik Heinen, Alexander Stebner, Michael Lell

Abstract readReview
PubMed Publisher
In one paragraph

Review in Der Radiologe, 2020. 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. Review
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

5 authors.

Panagiota ManavaInstitut für Radiologie und Nuklearmedizin, Klinikum Nürnberg, Paracelsus Medizinische Privatuniversität, Prof. Ernst-Nathan-Str. 1, 90419, Nürnberg, Deutschland.
Marco GalsterInstitut für Radiologie und Nuklearmedizin, Klinikum Nürnberg, Paracelsus Medizinische Privatuniversität, Prof. Ernst-Nathan-Str. 1, 90419, Nürnberg, Deutschland. marco.galster@klinikum-nuernberg.de.
Henrik HeinenInstitut für Radiologie und Nuklearmedizin, Klinikum Nürnberg, Paracelsus Medizinische Privatuniversität, Prof. Ernst-Nathan-Str. 1, 90419, Nürnberg, Deutschland.
Alexander StebnerInstitut für Radiologie und Nuklearmedizin, Klinikum Nürnberg, Paracelsus Medizinische Privatuniversität, Prof. Ernst-Nathan-Str. 1, 90419, Nürnberg, Deutschland.
Michael LellInstitut für Radiologie und Nuklearmedizin, Klinikum Nürnberg, Paracelsus Medizinische Privatuniversität, Prof. Ernst-Nathan-Str. 1, 90419, Nürnberg, Deutschland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) algorithms are increasingly used in radiology. The main areas of application are, for example, the detection of lung lesions and the diagnosis of chronic obstructive and interstitial lung diseases. The aim of our study was to train and evaluate a package of algorithms that analyze data from computed tomographic (CT) images of the chest and provide quantitative measurements to the radiologist. The following algorithms were trained: lung lesion detection and measurement, lung lobe segmentation, vessel segmentation and measurement, coronary calcium scoring, measurement and density analysis of vertebral bodies. AI-supported algorithms will become part of daily routine of the radiologist in the future. Tasks that do not require medical expertise can be performed by AI. However, our results show that, based on the current accuracy, verification by an experienced radiologist is necessary.

Indexed as

Artificial IntelligenceLung Diseases, InterstitialTomography, X-Ray ComputedAlgorithmsDecision Support Systems, ClinicalHumansThoraxAnalysis of vertebra bodiesCoronary calcium scoreLung lesion detectionLung lobe segmentationVessel segmentation

Identifiers

PMID32638030

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.