Research Progress in Artificial Intelligence for Central Serous Chorioretinopathy: A Systematic Review
Abstract Introduction This 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. Methods A systematic literat...
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| Format: | Article |
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Adis, Springer Healthcare
2025-07-01
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| Series: | Ophthalmology and Therapy |
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| Online Access: | https://doi.org/10.1007/s40123-025-01209-9 |
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| author | Ping Zhang Qing Zhang Xinya Hu Wei Chi Weihua Yang |
| author_facet | Ping Zhang Qing Zhang Xinya Hu Wei Chi Weihua Yang |
| author_sort | Ping Zhang |
| collection | DOAJ |
| description | Abstract Introduction This 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. Methods A 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. Results The 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. Conclusion AI 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. |
| format | Article |
| id | doaj-art-18dd8e883546455fad3b07d027ec6dce |
| institution | Kabale University |
| issn | 2193-8245 2193-6528 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Adis, Springer Healthcare |
| record_format | Article |
| series | Ophthalmology and Therapy |
| spelling | doaj-art-18dd8e883546455fad3b07d027ec6dce2025-08-24T11:11:42ZengAdis, Springer HealthcareOphthalmology and Therapy2193-82452193-65282025-07-011492083210710.1007/s40123-025-01209-9Research Progress in Artificial Intelligence for Central Serous Chorioretinopathy: A Systematic ReviewPing Zhang0Qing Zhang1Xinya Hu2Wei Chi3Weihua Yang4The Second Clinical Medical College of Jinan UniversityShenzhen Eye Hospital, Shenzhen Eye Medical Center, Southern Medical UniversityShenzhen Eye Hospital, Shenzhen Eye Medical Center, Southern Medical UniversityShenzhen Eye Hospital, Shenzhen Eye Medical Center, Southern Medical UniversityShenzhen Eye Hospital, Shenzhen Eye Medical Center, Southern Medical UniversityAbstract Introduction This 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. Methods A 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. Results The 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. Conclusion AI 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.https://doi.org/10.1007/s40123-025-01209-9Central serous chorioretinopathy (CSCR)Artificial intelligence (AI)Deep learning (DL)Machine learning (ML)Optical coherence tomography (OCT)Color fundus photography (CFP) |
| spellingShingle | Ping Zhang Qing Zhang Xinya Hu Wei Chi Weihua Yang Research Progress in Artificial Intelligence for Central Serous Chorioretinopathy: A Systematic Review Ophthalmology and Therapy Central serous chorioretinopathy (CSCR) Artificial intelligence (AI) Deep learning (DL) Machine learning (ML) Optical coherence tomography (OCT) Color fundus photography (CFP) |
| title | Research Progress in Artificial Intelligence for Central Serous Chorioretinopathy: A Systematic Review |
| title_full | Research Progress in Artificial Intelligence for Central Serous Chorioretinopathy: A Systematic Review |
| title_fullStr | Research Progress in Artificial Intelligence for Central Serous Chorioretinopathy: A Systematic Review |
| title_full_unstemmed | Research Progress in Artificial Intelligence for Central Serous Chorioretinopathy: A Systematic Review |
| title_short | Research Progress in Artificial Intelligence for Central Serous Chorioretinopathy: A Systematic Review |
| title_sort | research progress in artificial intelligence for central serous chorioretinopathy a systematic review |
| topic | Central serous chorioretinopathy (CSCR) Artificial intelligence (AI) Deep learning (DL) Machine learning (ML) Optical coherence tomography (OCT) Color fundus photography (CFP) |
| url | https://doi.org/10.1007/s40123-025-01209-9 |
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