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Multimodal artificial intelligence in retinopathy of prematurity: A comprehensive narrative review
Xinyu Zhao1, Zhenquan Wu1, Shirou Wu1
1Shenzhen Eye Hospital, Shenzhen Eye Medical Center, Southern Medical University, Shenzhen, China.
Insights
Artificial intelligence (AI) offers promising solutions for managing retinopathy of prematurity (ROP), a major cause of childhood blindness. This review explores AI applications in ROP diagnosis and treatment, addressing current challenges for wider implementation.
Area of Science:
- Ophthalmology
- Medical Imaging
- Artificial Intelligence
Background:
- Retinopathy of prematurity (ROP) is a significant cause of preventable childhood blindness globally.
- Current diagnostic methods for ROP, based on subjective fundus image interpretation, suffer from inter-observer variability and manual limitations.
- Limited screening resources in certain regions exacerbate the challenge of ROP management.
Purpose of the Study:
- To comprehensively review contemporary advances in artificial intelligence (AI)-driven management of retinopathy of prematurity (ROP).
- To synthesize AI technologies utilizing retinal imaging, clinical text, and smartphone images for ROP diagnosis, risk prediction, and treatment suggestions.
- To illustrate AI applications in intelligent diagnosis and treatment of ROP through multimodal imaging.
Main Methods:
- Systematic review of recent literature on AI applications in retinopathy of prematurity.
- Analysis of AI technologies applied to retinal imaging, clinical text data, and smartphone-captured images.
- Evaluation of AI's role in multimodal imaging for ROP diagnosis and treatment.
Main Results:
- AI demonstrates substantial potential for intelligent ROP management, transforming diagnosis, risk prediction, and treatment suggestions.
- AI technologies leverage diverse data sources, including retinal images, clinical text, and smartphone images, for enhanced ROP assessment.
- Multimodal imaging combined with AI shows promise for intelligent diagnosis and treatment of ROP.
Conclusions:
- Despite progress, challenges in data heterogeneity, model generalizability, and real-world integration need to be addressed for AI in ROP.
- Actionable insights are provided to accelerate the development of equitable and clinically deployable AI solutions for ROP.
- AI holds significant potential to reduce ROP-related vision loss globally, particularly in resource-limited settings.
Abstract:
Retinopathy of prematurity (ROP) remains a leading cause of preventable childhood blindness globally, particularly in regions with limited screening resources. Traditional diagnosis relying on subjective interpretation of fundus images faces challenges of inter-observer variability and manual analytical limitations. Recent advances in artificial intelligence (AI) have demonstrated substantial potential to achieve intelligent ROP management. We present a comprehensive review that synthesizes contemporary advances in AI-driven ROP management and reviewed the AI technologies based on retinal imaging, clinical text data, and smartphone-captured images transform ROP diagnosis, risk prediction, and treatment suggestion. We also illustrate the applications of AI in intelligent diagnosis and treatment of ROP through multimodal imaging. Despite remarkable progress, challenges persist in data heterogeneity, model generalizability, and real-world integration. By mapping technical breakthroughs to unmet clinical needs, we provide actionable insights to accelerate the development of equitable, clinically deployable AI solutions for preventing ROP-related vision loss.