ArticleScientific reports2022
Deep learning to distinguish Best vitelliform macular dystrophy (BVMD) from adult-onset vitelliform macular degeneration (AVMD).
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed, 19 citations in OpenAlex.
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- Artificial Intelligence (AI) for Early Diagnosis of Retinal Diseases.Medicina (Kaunas, Lithuania) · 2024Review
- RobOCTNet: Robotics and Deep Learning for Referable Posterior Segment Pathology Detection in an Emergency Department Population.Translational vision science & technology · 2024Article
- Deep learning model for automatic differentiation of EMAP from AMD in macular atrophy.Scientific reports · 2023Article
- Artificial intelligence in retinal disease: clinical application, challenges, and future directions.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2023Review
- Correlation between AI-measured lacquer cracks extension and development of myopic choroidal neovascularization.Eye (London, England) · 2023Article
- Advanced ImageJ Analysis in Degenerative Acquired Vitelliform Lesions Using Techniques Based on Optical Coherence Tomography.Biomedicines · 2023Article
- Adult-onset foveomacular vitelliform dystrophy: epidemiology, pathophysiology, imaging, and prognosis.Frontiers in ophthalmology · 2023Review
- Comparison of Multimodal Imaging for the Characterization of Geographic Atrophy.Translational vision science & technology · 2022Article
- Glutamate Permeability of Chicken Best1.Experimental neurobiology · 2022Article
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Authors and funding
12 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Initial stages of Best vitelliform macular dystrophy (BVMD) and adult vitelliform macular dystrophy (AVMD) harbor similar blue autofluorescence (BAF) and optical coherence tomography (OCT) features. Nevertheless, BVMD is characterized by a worse final stage visual acuity (VA) and an earlier onset of critical VA loss. Currently, differential diagnosis requires an invasive and time-consuming process including genetic testing, electrooculography (EOG), full field electroretinogram (ERG), and visual field testing. The aim of our study was to automatically classify OCT and BAF images from stage II BVMD and AVMD eyes using a deep learning algorithm and to identify an image processing method to facilitate human-based clinical diagnosis based on non-invasive tests like BAF and OCT without the use of machine-learning technology. After the application of a customized image processing method, OCT images were characterized by a dark appearance of the vitelliform deposit in the case of BVMD and a lighter inhomogeneous appearance in the case of AVMD. By contrast, a customized method for processing of BAF images revealed that BVMD and AVMD were characterized respectively by the presence or absence of a hypo-autofluorescent region of retina encircling the central hyperautofluorescent foveal lesion. The human-based evaluation of both BAF and OCT images showed significantly higher correspondence to ground truth reference when performed on processed images. The deep learning classifiers based on BAF and OCT images showed around 90% accuracy of classification with both processed and unprocessed images, which was significantly higher than human performance on both processed and unprocessed images. The ability to differentiate between the two entities without recurring to invasive and expensive tests may offer a valuable clinical tool in the management of the two diseases.
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