MODERN APPROACHES TO INTRAOCULAR LENS POWER CALCULATION IN PATIENTS AFTER RADIAL KERATOTOMY (LITERATURE REVIEW)

Abstract

Radial keratotomy (RK), once widely used for myopia correction, has left a large population of patients who later develop cataract and present significant challenges in intraocular lens (IOL) power calculation. The biomechanical and optical alterations caused by RK – such as corneal flattening, altered anterior-posterior ratio, and irregular astigmatism – compromise the accuracy of standard biometry and conventional IOL formulas. This review summarizes the current understanding of IOL power calculation in post-RK eyes, highlighting the main sources of error, modern formulas, and emerging technologies that improve prediction accuracy.

Classical methods such as SRK/T, Holladay 1, and Hoffer Q systematically lead to hyperopic outcomes, while newer formulas including Barrett True-K (Post-RK), Haigis-L, and Shammas-PL offer improved but still limited precision. Artificial intelligence – based algorithms (Hill-RBF 3.0, EVO 2.0) and ray-tracing techniques have further enhanced accuracy by integrating corneal geometry and individualized optical modelling. The incorporation of total keratometry (TK), 3D tomography, and self-learning AI systems marks a paradigm shift toward personalized ophthalmic surgery. Future developments in hybrid, cloud-based, and AI-assisted approaches are expected to achieve sub–0.35 D predictive accuracy and provide consistent refractive outcomes for this complex patient group.

Purpose – to analyze the accuracy of different IOL power calculation methods in patients who previously underwent RK and to identify approaches that minimize postoperative refractive errors.
The study included patients with a history of RK undergoing cataract surgery. Biometric data were obtained using the IOLMaster 700 (Carl Zeiss Meditec). IOL power was calculated using conventional formulas (SRK/T, Hoffer Q, Holladay 1), adjusted methods (Barrett True-K, Haigis-L, Shammas-PL), and artificial intelligence (AI)-based algorithms (Hill-RBF 3.0, EVO 2.0). The mean absolute error (MAE) between predicted and achieved refraction was evaluated.

Traditional formulas demonstrated a consistent hyperopic shift (+0.60 ± 0.25 D). Modern formulas, particularly Barrett True-K and Haigis-L, improved accuracy with MAE values of 0.38 ± 0.16 D. AI-based and ray-tracing methods achieved the highest precision (MAE 0.28–0.32 D), with 88% of cases within ±0.5 D of target refraction.

Conclusion
Intraocular lens power calculation after RK remains challenging due to corneal irregularity and unpredictable effective lens position (ELP). AI-integrated and ray-tracing–based algorithms offer superior accuracy and represent the future of personalized IOL prediction in post-RK eyes.