The Bayesian Additive Regression Trees Formula for Safe Machine Learning-Based Intraocular Lens Predictions - Clarke, Gerald P.; Kapelner, Adam (Frontiers in Big Data, 2020)
Purpose: Our work introduces a highly accurate, safe and sufficiently explicable machine-learning (artificial intelligence) model of intraocular lens power (IOL) translating into better post-surgical outcomes for patients with cataracts. We also demonstrate its improved predictive accuracy over previous formulas. Methods: We collected retrospective eye measurement data on 5,331 eyes from 3,276 patients across multiple centers who received a lens implantation during cataract surgery. The dependent measure is the postoperative manifest spherical equivalent error from intended and the independent variables are the patient- and eye-specific characteristics. This dataset was split so that one subset was for formula construction and the other for validating our new formula. Data excluded fellow eyes, so as not to confound the prediction with bilateral eyes. Results: Our formula is three times more precise than reported studies with a median absolute IOL error of 0.204 diopters (D). When converted to absolute predictive refraction errors on the cornea, the median error is 0.137 D which is close to IOL manufacturer tolerance. These estimates are validated out-of-sample and thus are expected to reflect future performance of our prediction formula, especially since our data were collected from a wide variety of patients, clinics, and manufacturers. Conclusion: The increased precision of IOL power calculations has the potential to optimize patient positive refractive outcomes. Our model also provides uncertainty plots that can be used in tandem with the clinician’s expertise and previous formula output, further enhancing the safety. Translational Relevance: Our new machine learning process has the potential to significantly improve patient IOL refractive outcomes safely.