A system (10) and method of assessing vision quality of an eye (E) is presented, with a controller (C) having a processor (P) and tangible, non-transitory memory (M) on which instructions are recorded. The controller (C) is configured to selectively execute at least one machine learning model (35, 36, 38). Execution of the instructions by the processor (P) causes the controller (C) to: receive wavefront aberration data of the eye and express the wavefront aberration data as a collection of Zernike polynomials. The controller (C) is configured to obtain (120) a plurality of input factors based on the collection of Zernike polynomials. The plurality of input factors is fed (120) into the at least one machine learning model (35, 36, 38), which is trained to analyze the plurality of input factors. The machine learning model (35, 36, 38) generates (130) at least one vision correction factor based in part on the plurality of input factors.
VISION QUALITY ASSESSMENT BASED ON MACHINE LEARNING MODEL AND WAVEFRONT ANALYSIS