Nambi Nallasamy, M.D.
Using Artificial Intelligence to Improve IOL Formulas
The resulting Nallasamy Formula was then tested against seven of the most commonly used formulas, using retrospective patient data. It outperformed them all. Dr. Nallasamy and fellow cataract surgeons at Kellogg now employ it side-by-side with other formulas. Having demonstrated that ML can improve IOL calculations for his patients, Dr. Nallasamy is now applying With more than 23 million procedures performed each it to challenges specific to other populations. year, cataract surgery is the world’s most common First, he hopes ML can help make cataract surgery surgical procedure. To achieve the best possible visual outcomes around the world more equitable by overoutcome for the patient, it’s crucial to implant an intracoming data biases inherent in IOL formulas. “Eye size ocular lens (IOL) with the optimal power. To select the and shape vary throughout the world,” he explains. “But IOL’s power, the surgeon relies on a formula to calculate our data sets reflect our local population. We need an which power will achieve a postoperative refraction that ML tool that can easily adjust to best matches the patient’s goals. different populations and IOL types A number of such formulas are IN ML, COMPUTERS ‘LEARN’ with minimal additional informaavailable, using different methodoloFROM PROCESSING DATA tion—a challenge called domain gies to weigh variables like the eye’s SETS FAR LARGER AND MORE adaptation.” length, corneal shape and lens thickMULTIFACETED THAN A HUMAN Dr. Nallasamy is also putting ness. While the accuracy of IOL power his ML approach to work to help calculation has risen over the years, MIND CAN ABSORB. surgeons navigate more complex so have patient expectations for their — Nambi Nallasamy, M.D. conditions, including patients rerefractive outcomes, creating demand quiring cataract surgery along with for still more powerful formulas. corneal transplantation, and those with corneal thinning Cornea and cataract surgeon Nambi Nallasamy, disorders like keratoconus. M.D., saw this as an ideal application for an artificial inSelecting the right lens to meet these patients’ telligence (AI) technology called machine learning (ML). needs is especially challenging, because the cornea “In ML, computers ‘learn’ from processing data sets continues to change as they recover from surgery. far larger and more multifaceted than a human mind “Again, ML can generate a more customized lens calcucan absorb,” he explains. “In this case, we taught the lation formula when we fine tune the dataset we use,” computer to make IOL power decisions by processing he explains, “emphasizing measurements from optical data from nearly 10,000 patients who underwent biometry and tomography to better model the postopcataract surgery at Kellogg.” That data set included erative evolution of the cornea.” key patient demographics plus eight different eye measurements. 14
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