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Predicting Post-operative Visual Acuity for LASIK Surgeries

机译：预测LASIK手术的术后视力

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LASIK (Laser-Assisted in SItu Keratomileusis) surgeries have been quite popular for treatment of myopia (nearsightedness), hyperopia (farsightedness) and astigmatism over the past two decades. In the past decade, over 10 million LASIK procedures had been performed in the United States alone with an average cost of approximately $2000 USD per surgery. While 99 % of such surgeries are successful, the commonest side effect is a residual refractive error and poor uncorrected visual acuity (UCVA). In this work, we aim at predicting the UCVA post LASIK surgery. We model the task as a regression problem and use the patient demography and pre-operative examination details as features. To the best of our knowledge, this is the first work to systematically explore this critical problem using machine learning methods. Further, LASIK surgery settings are often determined by practitioners using manually designed rules. We explore the possibility of determining such settings automatically to optimize for the best post-operative UCVA by including such settings as features in our regression model. Our experiments on a dataset of 791 surgeries provides an RMSE (root mean square error) of 0.102, 0.094 and 0.074 for the predicted post-operative UCVA after one day, one week and one month of the surgery respectively.
机译：在过去的二十年中，LASIK（激光辅助性角膜磨镶术）手术在治疗近视（近视），远视（远视）和散光方面非常受欢迎。在过去的十年中，仅在美国就进行了超过一千万次LASIK手术，平均每次手术费用约为2000美元。尽管99％的此类手术成功了，但最常见的副作用是残余屈光不正和较差的未矫正视敏度（UCVA）。在这项工作中，我们旨在预测LASIK手术后的UCVA。我们将任务建模为回归问题，并使用患者人口统计学和术前检查细节作为特征。据我们所知，这是使用机器学习方法系统地探索这一关键问题的第一项工作。此外，LASIK手术设置通常由从业人员使用手动设计的规则确定。我们探索了通过在回归模型中包含诸如特征之类的设置来自动确定此类设置以优化最佳术后UCVA的可能性。我们在791个手术的数据集上进行的实验分别为术后一天，一周和一个月的预计术后UCVA提供了0.102、0.094和0.074的RMSE（均方根误差）。

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