ReviewDer Ophthalmologe : Zeitschrift der Deutschen Ophthalmologischen Gesellschaft2018
[Screening and management of retinal diseases using digital medicine].
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 2 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
2 citing papers in PubMed, 4 citations in OpenAlex.
- Visual acuity prediction on real-life patient data using a machine learning based multistage system.Scientific reports · 2024Article
- Clinical Perspectives on the Use of Computer Vision in Glaucoma Screening.Medicina (Kaunas, Lithuania) · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundModern retinal imaging creates gigantic amounts of data (big data) of anatomic information. At the same time patient numbers and interventions are increasing exponentially.
objectiveIntroduction of artificial intelligence (AI) for optimization of personalized therapy and diagnosis. MATERIAL AND
methodsDeep learning was introduced for automated segmentation and recognition of risk factors and activity levels in retinal diseases.
resultsAutomated algorithms enable the precise identification and quantification of retinal fluid in all compartments. Early detection of retinopathy in diabetes or glaucoma or risk determination for the development of age-related macular degeneration (AMD) are possible as well as an individual visual prognosis and evaluation of the need for retreatment in intravitreal injection therapy.
conclusionMethods using AI constitute a breakthrough perspective for the introduction of individualized medicine and optimization of diagnosis and therapy, screening and prognosis.
Indexed as
Identifiers
What 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.