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In Journal of cataract and refractive surgery

Differences between target and implanted intraocular lens (IOL) power in Ethiopian cataract outreach campaigns were evaluated and machine learning (ML) applied to optimize IOL inventory and minimize avoidable refractive error. Patients from Ethiopian cataract campaigns with available target and implanted IOL records were identified and the diopter difference between the two measured. A gradient descent (an ML algorithm) was used to generate an optimal IOL inventory and measured the model's performance across varying surplus levels.Only 45.6% of patients received their target IOL power and 23.6% received underpowered IOLs with current inventory (50% surplus). The ML-generated IOL inventory ensured that >99.5% of patients received their target IOL when using only 39% IOL surplus.In Ethiopian cataract campaigns, the majority of patients have avoidable postoperative refractive error secondary to suboptimal IOL inventory. Optimizing IOL inventory using our ML model might eliminate refractive error from insufficient inventory and reduce costs.

Brant Arthur R, Hinkle John, Shi Siyu, Hess Olivia, Zubair Talhah, Pershing Suzann, Tabin Geoffrey C

2020-Sep-03