ArticleBMC ophthalmology2014
Detection of leukocoria using a soft fusion of expert classifiers under non-clinical settings.
Article in BMC ophthalmology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02626416 (Self-monitoring of Visual Axis Opacification), which is not on this map. Cited by 5 papers.
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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.
Self-monitoring of Visual Axis Opacification (VAO) Under Non-clinical Settings in China
Who cites it
5 citing papers in PubMed.
- Smartphone applications for early screening of retinoblastoma: a scoping review.BMC ophthalmology · 2026Article
- Clinical Applications of Machine Learning in the Management of Intraocular Cancers: A Narrative Review.Investigative ophthalmology & visual science · 2023Review
- Autonomous early detection of eye disease in childhood photographs.Science advances · 2019Article
- Smartphone-based application improves the detection of retinoblastoma.Eye (London, England) · 2019Article
- The Implementation and Evaluation of Health Promotion Services and Programs to Improve Cultural Competency: A Systematic Scoping Review.Frontiers in public health · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLeukocoria is defined as a white reflection and its manifestation is symptomatic of several ocular pathologies, including retinoblastoma (Rb). Early detection of recurrent leukocoria is critical for improved patient outcomes and can be accomplished via the examination of recreational photography. To date, there exists a paucity of methods to automate leukocoria detection within such a dataset.
methodsThis research explores a novel classification scheme that uses fuzzy logic theory to combine a number of classifiers that are experts in performing multichannel detection of leukocoria from recreational photography. The proposed scheme extracts features aided by the discrete cosine transform and the Karhunen-Loeve transformation.
resultsThe soft fusion of classifiers is significantly better than other methods of combining classifiers with p = 1.12 × 10-5. The proposed methodology performs at a 92% accuracy rate, with an 89% true positive rate, and an 11% false positive rate. Furthermore, the results produced by our methodology exhibit the lowest average variance.
conclusionsThe proposed methodology overcomes non-ideal conditions of image acquisition, presenting a competent approach for the detection of leukocoria. Results suggest that recreational photography can be used in combination with the fusion of individual experts in multichannel classification and preprocessing tools such as the discrete cosine transform and the Karhunen-Loeve transformation.
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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.