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    How new methodology improves accuracy for IOL power selection

    Radial basis function approach draws on concept of pattern recognition and data interpolation



    A new method being developed to improve the accuracy of IOL power calculations for cataract surgery uses pattern recognition as its basis.


    Dr. HillMesa, AZ—Radial basis functions—a method being developed to improve the accuracy of IOL power calculations for use during cataract surgery—uses a sophisticated method of pattern recognition and data interpolation as the basis for calculations.

    More in this issue: How technology aids real-time adjustment of toric IOLs

    This new method draws on technology outside of ophthalmology, said Warren E. Hill, MD, who is in private practice, Mesa, AZ.

    “We believe that it is possible to increase patient safety and physician confidence and reduce the costs associated with a refractive surprise after cataract surgery,” Dr. Hill said. “Our research team has developed a self-validating approach for improved refractive accuracy that combines enhanced axial metrics with two aspects of an already established calculation methodology.”

    Dr. Hill—along with his algorithm development “dream team” that includes Peter Maloney, SMME, senior principal consulting engineer at MathWorks, Novi, MI; Doug Koch, MD, and Li Wang, MD, PhD, of Baylor College of Medicine, Houston; Sheridan Lam, MD, of DuPage, IL, and Johnny Gayton, MD, of Warner-Robins, GA—adopted an engineering tool to the task IOL power selection that does not use formulas. The goal was to take the accuracy of this process to the next level by adopting a technological base from the world of artificial intelligence.

    The need for such an investigation came from insights that came out of optimizing physician cataract surgery outcomes databases for more than 200,000 cases and observing the typical accuracy.

    The majority of surgeons are within ±0.50 D only 78% of the time, Dr. Hill noted. Only 6% of databases reviewed were at this level of accuracy 84% of the time, and less than 1% of databases achieved this level of accuracy at a 92% level, he added.

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