ReviewJournal of digital imaging2018
Automated Segmentation and Quantification of Drusen in Fundus and Optical Coherence Tomography Images for Detection of ARMD.
Review in Journal of digital imaging, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
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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.
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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.
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Who cites it
19 citing papers in PubMed, 45 citations in OpenAlex.
- Indocyanine green angiography and machine learning analysis determine topographic distribution of peripheral hard drusen in a Chinese cohort.Communications medicine · 2026Article
- Optical Coherence Tomography Biomarkers Predicting Progression to Atrophy in Non-Exudative Age-Related Macular Degeneration.Diagnostics (Basel, Switzerland) · 2026Review
- AI-aided segmentation of four types of drusen in volumetric OCT.Biomedical optics express · 2025Article
- Preclinical Retinal Disease Models: Applications in Drug Development and Translational Research.Pharmaceuticals (Basel, Switzerland) · 2025Review
- Navigating ethical challenges in digital transformation: insights on climate adaptation, microbiology, healthcare, robotics, and AI under the EU AI act: an experts panel discussion.Global bioethics = Problemi di bioetica · 2025Article
- Artificial intelligence in stroke risk assessment and management via retinal imaging.Frontiers in computational neuroscience · 2025Review
- Automated Detection of Drusenoid Pigment Epithelial Detachments From Spectral-Domain Optical Coherence Tomography in Patients With AMD.Translational vision science & technology · 2024Article
- Pretraining of 3D image segmentation models for retinal OCT using denoising-based self-supervised learning.Biomedical optics express · 2024Article
- Artificial intelligence in chorioretinal pathology through fundoscopy: a comprehensive review.International journal of retina and vitreous · 2024Review
- Non-Neovascular Age-Related Macular Degeneration Assessment: Focus on Optical Coherence Tomography Biomarkers.Diagnostics (Basel, Switzerland) · 2024Review
- On Machine Learning in Clinical Interpretation of Retinal Diseases Using OCT Images.Bioengineering (Basel, Switzerland) · 2023Review
- OCT and OCT Angiography Update: Clinical Application to Age-Related Macular Degeneration, Central Serous Chorioretinopathy, Macular Telangiectasia, and Diabetic Retinopathy.Diagnostics (Basel, Switzerland) · 2023Review
- Machine learning in image analysis in ophthalmology.Einstein (Sao Paulo, Brazil) · 2022Article
- Real-world effectiveness of screening programs for age-related macular degeneration: amended Japanese specific health checkups and augmented screening programs with OCT or AI.Japanese journal of ophthalmology · 2022Article
- Automatic detection of leakage point in central serous chorioretinopathy of fundus fluorescein angiography based on time sequence deep learning.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2021Article
- Validation of a Novel Automated Algorithm to Measure Drusen Volume and Area Using Swept Source Optical Coherence Tomography Angiography.Translational vision science & technology · 2021Article
- [Screening and management of retinal diseases using digital medicine].Der Ophthalmologe : Zeitschrift der Deutschen Ophthalmologischen Gesellschaft · 2018Review
- Deep learning approach for the detection and quantification of intraretinal cystoid fluid in multivendor optical coherence tomography.Biomedical optics express · 2018Article
- Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Age-related macular degeneration (ARMD) is one of the most common retinal syndromes that occurs in elderly people. Different eye testing techniques such as fundus photography and optical coherence tomography (OCT) are used to clinically examine the ARMD-affected patients. Many researchers have worked on detecting ARMD from fundus images, few of them also worked on detecting ARMD from OCT images. However, there are only few systems that establish the correspondence between fundus and OCT images to give an accurate prediction of ARMD pathology. In this paper, we present fully automated decision support system that can automatically detect ARMD by establishing correspondence between OCT and fundus imagery. The proposed system also distinguishes between early, suspect and confirmed ARMD by correlating OCT B-scans with respective region of the fundus image. In first phase, proposed system uses different B-scan based features along with support vector machine (SVM) to detect the presence of drusens and classify it as ARMD or normal case. In case input OCT scan is classified as ARMD, region of interest from corresponding fundus image is considered for further evaluation. The analysis of fundus image is performed using contrast enhancement and adaptive thresholding to detect possible drusens from fundus image and proposed system finally classified it as early stage ARMD or advance stage ARMD. The proposed system is tested on local data set of 100 patients with100 fundus images and 6800 OCT B-scans. Proposed system detects ARMD with the accuracy, sensitivity, and specificity ratings of 98.0, 100, and 97.14%, respectively.
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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.