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    How new ‘evolving’ IOL formula maximizes lens accuracy

     

    We track data from these groups, and use it to adjust and optimize the Ladas Super Surface accordingly. Figures 4A and 4B provide an idea of the progress made by highlighting the number of eyes we already have for optimization purposes within each grid box of the Ladas Super Surface.

    This “real-world” data helps the Ladas Super Formula evolve and gives it a dynamic edge, which no previous formula had before, to further refine IOL calculations.

    Figure 4A: The Manhattan Graph. A graphical representation of the optimization efforts so far where the Z-axis represents number of eyes that have been collected thanks to the participation of user groups. Figure 4B: A bird's eye view of the Manhattan graph

    Hypothetical user group

    In Figures 4A and 4B, we have provided a distribution of the data already collected over the given ranges of corneal power and axial range from various user groups. We can see there exists a certain preponderance of eyes in a particular region of the surface.

    As we cover every unit of the Ladas Super Surface, we will continue to optimize and perfect the Ladas Super Formula. The more eyes we are able to gather, the more refined the Ladas Super Formula gets. We are crowd-sourcing additional data so we can move up to a 100,000-plus eyes and then, eventually a 1 million or more eyes. Surgeons who wish to participate in this beta testing should contact the authors at the contact information at the end of this article.

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