Predicting patient-reported quality of vision through multifocal optics

Area of Science:

• Ophthalmology
• Optics
• Visual Science

Background:

• Assessing subjective image quality in multifocal soft contact lens (MF SCL) wear is crucial for product development.
• Computational modeling of the point-spread function (PSF) offers a potential objective measure of image quality.

Purpose of the Study:

• To evaluate the agreement between computationally modeled PSFs and subjective descriptions of image quality from MF SCL wearers.
• To determine if computational methods can accurately represent perceived visual performance.

Main Methods:

• Participants wore various MF SCLs and single vision lenses.
• Aberrometry data was collected to compute PSFs, which were then compared to participants' hand-drawn PSFs.
• Statistical analyses, including Yule's Q, Multidimensional Scaling (MDS), and Procrustes analysis, were used to quantify agreement.

Main Results:

• Significant qualitative and quantitative similarities were found between computed and hand-drawn PSFs.
• High inter-rater agreement (Yule's Q = 0.63) and excellent correspondence in MDS configurations (stress < 0.1) were observed.
• Strong concordance between hand-drawn and computed 3D configurations (congruence coefficient = 0.90) was confirmed.

Conclusions:

• There is good agreement between subjective PSF perception and computational predictions from aberrometry.
• Computational modeling of PSFs shows potential for use in clinical settings and contact lens R&D.
• Leveraging the link between subjective and computational image quality representations can enhance research and development processes.