ReviewDer Ophthalmologe : Zeitschrift der Deutschen Ophthalmologischen Gesellschaft2018
[Deep learning and neuronal networks in ophthalmology : Applications in the field of optical coherence tomography].
Review in Der Ophthalmologe : Zeitschrift der Deutschen Ophthalmologischen Gesellschaft, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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.
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.
Who cites it
8 citing papers in PubMed.
- Discriminating Healthy Optic Discs and Visible Optic Disc Drusen on Fundus Autofluorescence and Color Fundus Photography Using Deep Learning-A Pilot Study.Journal of clinical medicine · 2023Article
- [Employee survey after introduction of the FIDUS electronic patient file at the Saarland University Eye Hospital].Der Ophthalmologe : Zeitschrift der Deutschen Ophthalmologischen Gesellschaft · 2022Article
- [Diagnostics of diseases of the optic nerve head in times of artificial intelligence and big data].Der Ophthalmologe : Zeitschrift der Deutschen Ophthalmologischen Gesellschaft · 2021Review
- Magnetic Resonance Imaging Images under Deep Learning in the Identification of Tuberculosis and Pneumonia.Journal of healthcare engineering · 2021Article
- Extraction of Retinal Layers Through Convolution Neural Network (CNN) in an OCT Image for Glaucoma Diagnosis.Journal of digital imaging · 2020Article
- [Artificial intelligence in management of macular edema : Opportunities and challenges].Der Ophthalmologe : Zeitschrift der Deutschen Ophthalmologischen Gesellschaft · 2020Review
- [FIDUSweb version 2.0 : An electronic platform to establish a cooperative university eye network].Der Ophthalmologe : Zeitschrift der Deutschen Ophthalmologischen Gesellschaft · 2020Article
- Automated OCT angiography image quality assessment using a deep learning algorithm.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep learning is increasingly becoming the focus of various imaging methods in medicine. Due to the large number of different imaging modalities, ophthalmology is particularly suitable for this field of application. This article gives a general overview on the topic of deep learning and its current applications in the field of optical coherence tomography. For the benefit of the reader it focuses on the clinical rather than the technical aspects.
Indexed as
Identifiers
29675699What OpenQuestion holds
Registered trials
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.