Improving clinical refractive results of cataract surgery by machine learning
Improving clinical refractive results of cataract surgery by machine learning
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DOI：
10.7717/peerj.7202
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发表时间：
2019-07-02
期刊：
影响因子：
2.7
通讯作者：
Stodulka, Pavel
中科院分区：
文献类型：
--
作者：
Sramka, Martin;Slovak, Martin;Stodulka, Pavel
目的：为了评估支持向量机回归模型（SVM-RM）和多层神经网络嵌入模型（MLNN-EM）的潜力，以提高人工透镜（IOL）的功率计算的临床workflow.Background：目前的IOL功率计算方法是有限的，在其准确性的可能性降低，特别是在眼睛与一个不寻常的眼睛尺寸。在白内障或屈光性透镜置换手术中，如果IOL屈光度计算不正确，则存在再次手术或进一步屈光矫正的风险。这可能会产生潜在的并发症和不适的patient.Methods：一个数据集包含的信息约2,194只眼睛，使用数据挖掘过程中获得的电子健康记录（EHR）系统数据库的双子座眼科诊所。对数据集进行了优化，并将其分为选择集（用于模型设计和训练）和验证集（用于评估）。对两种模型和临床结果（CR）的平均预测误差（PE）和预测屈光不正的分布进行了评价。结果：两种模型的大多数评价参数均优于CR。两种评价模型之间无显著差异。在+/- 0.50 D PE类别中，SVM-RM和MLNN-EM均略优于Barrett Universal II公式，后者通常被认为是最准确的计算公式。结论：与当前的临床方法相比，SVM-RM和MLNN-EM在IOL计算中均取得了显著更好的结果，因此具有很强的潜力来改善临床白内障屈光结局。
Aim: To evaluate the potential of the Support Vector Machine Regression model (SVM-RM) and Multilayer Neural Network Ensemble model (MLNN-EM) to improve the intraocular lens (IOL) power calculation for clinical workflow.Background: Current IOL power calculation methods are limited in their accuracy with the possibility of decreased accuracy especially in eyes with an unusual ocular dimension. In case of an improperly calculated power of the IOL in cataract or refractive lens replacement surgery there is a risk of re-operation or further refractive correction. This may create potential complications and discomfort for the patient.Methods: A dataset containing information about 2,194 eyes was obtained using data mining process from the Electronic Health Record (EHR) system database of the Gemini Eye Clinic. The dataset was optimized and split into the selection set (used in the design for models and training), and the verification set (used in the evaluation). The set of mean prediction errors (PEs) and the distribution of predicted refractive errors were evaluated for both models and clinical results (CR).Results: Both models performed significantly better for the majority of the evaluated parameters compared with the CR. There was no significant difference between both evaluated models. In the +/- 0.50 D PE category both SVM-RM and MLNN-EM were slightly better than the Barrett Universal II formula, which is often presented as the most accurate calculation formula.Conclusion: In comparison to the current clinical method, both SVM-RM an d MLNN-EM have achieved significantly better results in IOL calculations and therefore have a strong potential to improve clinical cataract refractive outcomes.