ArticleBMC ophthalmology2025
Differentiation of optic disc edema and pseudopapilledema with deep learning on near-infrared reflectance images.
Article in BMC ophthalmology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThis study aimed to develop an artificial intelligence-based deep learning (DL) algorithm using near-infrared reflectance (NIR) images to differentiate between optic disc edema and pseudopapilledema, and to evaluate the diagnostic performance of the developed model.
methodsNIR images were divided into 2 groups for training and testing of the model. 85% (714 images) were used for training the model and 15% (126 images) for testing the trained model. Sensitivity, specificity and accuracy of the model were calculated in detecting optic disc edema, pseudopapilledema and normal optic discs. Receiver operating characteristic curve and area under the curve (AUC) values were also analyzed.
resultsThe developed model was tested with 24 optic disc edema, 52 pseudopapilledema, and 50 normal optic disc images not used in training. Sensitivities were 100%, 98%, and 96%; specificities were 99%, 97%, and 100%; and accuracy rates were 99%, 98%, and 98%, respectively. In addition, the AUC values of the groups were 0.995 (95% Confidence interval [CI]: 0.98-1); 0.983 (95% CI: 0.96-1); and 0.973 (95% CI: 0.94-1).
conclusionsThe developed DL model demonstrated high diagnostic performance in distinguishing optic disc edema and pseudopapilledema using NIR images and may serve as a reliable clinical decision support tool.
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.