Evidence map›Paper›PMID 32095839›Full record

ReviewDer Ophthalmologe : Zeitschrift der Deutschen Ophthalmologischen Gesellschaft2020

[Potential of methods of artificial intelligence for quality assurance].

Philipp Berens, Sebastian M Waldstein, Murat Seckin Ayhan, Louis Kümmerle, Hansjürgen Agostini, Andreas Stahl, Focke Ziemssen

Abstract readReview
PubMed Publisher
In one paragraph

Review in Der Ophthalmologe : Zeitschrift der Deutschen Ophthalmologischen Gesellschaft, 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
0.3field-weighted citation impact, top 38% 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

1 citing paper in PubMed, 5 citations in OpenAlex.

  1. [Artificial intelligence in ophthalmology : Guidelines for physicians for the critical evaluation of studies].Der Ophthalmologe : Zeitschrift der Deutschen Ophthalmologischen Gesellschaft · 2020
    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

7 authors at 5 institutions in 4 countries.

Philipp BerensForschungsinstitut für Augenheilkunde, Universität Tübingen, Otfried-Müller-Str. 25, 72076, Tübingen, Deutschland. philipp.berens@uni-tuebingen.de.
Sebastian M WaldsteinChristian Doppler Laboratory for Ophthalmic Image Analysis, Univ. Klinik für Augenheilkunde und Optometrie, Medizinische Universität Wien, Wien, Österreich.
Murat Seckin AyhanForschungsinstitut für Augenheilkunde, Universität Tübingen, Otfried-Müller-Str. 25, 72076, Tübingen, Deutschland.
Louis KümmerleForschungsinstitut für Augenheilkunde, Universität Tübingen, Otfried-Müller-Str. 25, 72076, Tübingen, Deutschland.
Hansjürgen AgostiniKlinik für Augenheilkunde, Universitätsklinikum Freiburg, Freiburg, Deutschland.
Andreas StahlUniversitätsaugenklinik Greifswald, Universitätsmedizin Greifswald, Greifswald, Deutschland.
Focke ZiemssenUniversitäts-Augenklinik Tübingen, Universität Tübingen, Tübingen, Deutschland.
Research Institute for Work, Technology and Culture · DEUniversitätsmedizin Greifswald · DEUniversity Medical Center Freiburg · DEUniversity of Tübingen · DEWestmead Hospital · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundProcedures with artificial intelligence (AI), such as deep neural networks, show promising results in automatic analysis of ophthalmological imaging data.

objectiveThis article discusses to what extent the application of AI algorithms can contribute to quality assurance in the field of ophthalmology.

methodsRelevant aspects from the literature are discussed.

findingsSystems based on artificial deep neural networks achieve remarkable results in the diagnostics of eye diseases, such as diabetic retinopathy and are very helpful, for example by segmenting optical coherence tomographic (OCT) images and detecting lesion components with high fidelity. To train these algorithms large data sets are required. The quality and availability of such data sets determine the continuous improvement of the algorithms. The comparison between the AI algorithms and physicians for image interpretation has also enabled insights into the diagnostic concordance between physicians. Current challenges include the development of methods for modelling decision uncertainty and improved interpretability of automated diagnostic decisions.

conclusionSystems based on AI can support decision making for physicians and thereby contribute to a more efficient quality assurance.

Indexed as

Artificial IntelligenceAlgorithmsEye DiseasesHumansNeural Networks, ComputerOphthalmologyQuality Assurance, Health CareArtificial neuronal networksAutomatic analysisBenchmarkingData transferStatement certainty

Identifiers

PMID32095839
OpenAlexW3008305333

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.