ReviewOphthalmology and therapy2025
Research Progress in Artificial Intelligence for Central Serous Chorioretinopathy: A Systematic Review.
Review in Ophthalmology and therapy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Vitreoretinal disease detection using artificial intelligence: a systematic review and meta-analysis.International ophthalmology · 2026Pooled it
- Radiomics-based fundus autofluorescence analysis in central serous chorioretinopathy-MICRoN report number twelve.Scientific reports · 2026Article
- A two-stage deep learning framework with progressive feature fusion and attention mechanisms for multi-class retinal disease classification.Frontiers in artificial intelligence · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
introductionThis review synthesizes advancements in artificial intelligence (AI) applications for central serous chorioretinopathy (CSCR), analyzing challenges and outlining future research directions to guide personalized diagnostic and therapeutic strategies.
methodsA systematic literature search was conducted in the Web of Science database using a comprehensive AI-related keyword set (e.g., "deep learning," "neural networks," "computer vision") combined with "central serous chorioretinopathy." The search yielded 698 records, with 73 original research studies selected after excluding reviews and non-empirical work based on predefined criteria.
resultsThe application of AI in CSCR has progressed from disease classification to dynamic prognostic prediction, leveraging multimodal data fusion (e.g., optical coherence tomography [OCT] and OCT angiography [OCTA] and fundus fluorescein angiography [FFA]) to enhance both qualitative and quantitative diagnostic accuracy. AI models outperform clinical experts in classifying retinal disease subtypes and segmenting lesions. However, clinical translation faces infrastructural barriers (e.g., incompatible PACS systems) and limited physician trust due to "black box" decision-making. New approaches, such as explainable AI (XAI), are being integrated to enhance the transparency and clinical applicability of AI models. Key limitations involve single-center data dependency, interobserver annotation variability, and the inability of static frameworks to capture dynamic lesion progression.
conclusionAI enhances CSCR diagnosis and subtyping efficiency. To optimize clinical translation, future research should focus on multicenter data integration, dynamic visualization frameworks, and standardized guidelines, promoting interdisciplinary collaboration and prospective trials for personalized treatment strategies. Incorporating federated learning for privacy-preserving data sharing and prioritizing explainability will be essential to overcoming barriers to physician adoption and improving trust in AI-driven clinical decision-making. Future efforts should focus on creating dynamic systems that provide real-time insights into lesion progression and integrating them with standardized protocols for wider clinical adoption.
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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.