Design of Progressive Addition Lens for Presbyopia: A Systematic Review
Abstract
Purpose: Presbyopia is a common age-related condition and progressive addition lenses (PALs) are a primary corrective method. This systematic review synthesised recent evidence (2015-2025) on the technical design of PALs for presbyopia, focusing on key developments in design methodologies, performance evaluation and emerging lens concepts.
Methods: A systematic search of six databases (PubMed, Embase, Web of Science, Cochrane Library, Scopus and SPIE Digital Library) was conducted following PRISMA guidelines. Eligible studies addressed PALs design principles, algorithms, methodologies or optical performance evaluation. Study quality was assessed using a customised 12-dimensional framework.
Results: The review included 17 eligible studies. Four major thematic areas of advancement were identified: the maturation of freeform surface design as the primary method for creating complex optical surfaces; the evolution of strategies for the control of surface astigmatism; the development of advanced optical performance quantification techniques enabling objective quantification of lens performance and the emergence of novel lens concepts expanding design possibilities beyond conventional refractive approaches. Key progress was noted in algorithmic optimisation for the control of surface astigmatism and the shift from subjective assessment to data-driven performance validation.
Conclusion: PAL design has evolved significantly toward customised, algorithm-driven solutions. However,…
How strong is the evidence?
Evidence Hub reads this as a systematic review, done in people. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid.
Read the paper
Related studies
- Knowledge Management Perspective of Generative Artificial Intelligence
- Navigating Generative Artificial Intelligence Promises and Perils for Knowledge and Creative Work
- Characterizing the evolving research on enterprise content management
- Large language models and multimodal foundation models for precision oncology
- From Generative AI to Extended Reality: Multidisciplinary Perspectives on the Challenges, Opportunities, and Future of Educational Computing
- Generative Artificial Intelligence: A New Engine for Advancing Environmental Science and Engineering
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.