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Reliability of Prediction Models for the Functional Classification of a Sinusoidal Intraocular Lens Depending on
Diego Montagud-Martínez1,2, Walter D Furlan2, Vicente Ferrando1
1Centro de Tecnologías Físicas, Universitat Politècnica de València, 46022 Valencia, Spain.
Diagnostics (Basel, Switzerland)
|October 16, 2025
Summary
Different visual acuity prediction models show varied agreement when classifying intraocular lenses (IOLs), especially concerning pupil-dependent performance. Caution is advised when interpreting functional classifications due to variations in optical bench setups and models.
Area of Science:
- Ophthalmic optics
- Biomedical engineering
- Vision science
Background:
- Intraocular lenses (IOLs) require functional classification for clinical application.
- Evaluating pupil dependency is crucial for understanding IOL performance across different lighting conditions.
- Existing prediction models for IOL functional classification need assessment for agreement and limitations.
Purpose of the Study:
- To assess agreement among prediction models for intraocular lens (IOL) functional classification.
- To evaluate the limitations of these models in assessing pupil dependency.
- To analyze the performance of a sinusoidal IOL across varying pupil sizes.
Main Methods:
- Utilized an ISO-compliant optical bench with modified setup to measure modulation transfer function area (MTFa).
- Measured MTFa across pupil diameters from 1.5 to 5.5 mm for the Acriva Trinova Pro C Pupil Adaptive IOL.
- Applied six prediction models to estimate visual acuity defocus curves and functional classifications (depth-of-field and visual acuity increase).
Main Results:
- All models predicted a Full-depth-of-field (DOFi) response (>2.3 D at 0.2 logMAR).
- Differences in visual acuity increase (ΔVA) emerged across pupil diameters, with continuous decreases (<0.05 logMAR) below 2.5 mm.
- Model classifications (Continuous, Smooth, Steep) varied significantly above 3.5 mm pupil diameter, highlighting model-specific responses.
Conclusions:
- Visual acuity prediction models offer clinically relevant metrics from optical bench data.
- Functional classifications derived from these models can differ based on the optical bench setup and prediction model employed.
- Interpretation of IOL functional classification requires careful consideration of the methodology and prediction model used.
