Screening Candidates for Refractive Surgery With Corneal Tomographic-Based Deep Learning
Screening Candidates for Refractive Surgery With Corneal Tomographic-Based Deep Learning
复制标题
利用基于角膜断层扫描的深度学习筛选屈光手术候选人
DOI：
10.1001/jamaophthalmol.2020.0507
复制
发表时间：
2020-05-01
影响因子：
8.1
通讯作者：
Liu, Quan
中科院分区：
文献类型：
--
作者：
Xie, Yi;Zhao, Lanqin;Liu, Quan
这项诊断性研究报告了一种用于筛查希望接受矫正屈光手术的个体的学习模型。问题：深度学习能否用于屈光手术候选人的角膜断层扫描筛查？在这项诊断性研究中，包括1385名患者在内，深度学习模型在验证数据集上获得了94.7%的总体检测准确率。在独立测试数据集上，该模型获得了与资深眼科医生进行屈光手术的识别率(92.8%)相当的识别率(95.0%)。使用深度学习算法的角膜断层扫描可以提供标准化的结果，以减少外科医生的工作量和错误分类的风险。重要的是，在屈光手术前使用角膜断层扫描评估角膜形态特征，以排除高危角膜和圆锥角膜的患者。在之前的研究中，研究人员基于特定的角膜参数使用机器学习方法进行筛查。到目前为止，深度学习算法还没有与角膜断层扫描结合使用。目的探讨深度学习模型在屈光手术候选人筛选中的应用。设计、地点和对象一项诊断性横断面研究在广州中山眼科中心进行，中国，检查时间为2016年7月18日至2019年3月29日。调查时间为2018年7月2日至2019年6月28日。参与者包括1385名患者；6465张角膜断层图像用于生成人工智能(AI)模型。数据收集使用的是Pentacam人力资源系统。干预眼科医生和人工智能模型对识别出的图像进行分析。主要结果和衡量人工智能分类系统的性能。结果建立了以人工智能模型Pentacam InceptionResNetV2筛查系统(PIRSS)为核心的屈光手术候选筛查分类系统。该模型在验证数据集上取得了94.7%(95%可信区间，93.3%~95.8%)的整体检测准确率。此外，在独立的测试数据集上，PIRSS模型的总体检测准确率达到95%(95%CI，88.8%-97.8%)，与资深眼科医生的检测准确率(92.8%；95%CI，91.2%-94.4%)相当(P=0.72)。在区分具有屈光手术禁忌症的角膜方面，PIRSS模型在亚洲患者数据库中的表现优于Pentacam HR系统中的分类器(95%比81%；P<；.001)。结论和相关性PIRSS在对图像进行分类以提供角膜信息和初步识别高危角膜方面似乎是有用的。PIRSS可能会为屈光外科医生筛选屈光手术候选者以及亚洲患者的广泛临床应用提供指导，但它的使用需要在其他人群中得到证实。
This diagnostic study reports on a learning model used in screening individuals who wish to undergo corrective refractive surgery.Question Can deep learning be used in corneal tomographic screening of candidates for refractive surgery? Findings In this diagnostic study including 1385 patients, a deep learning model achieved an overall detection accuracy of 94.7% on the validation data set. On the independent test data set, the model achieved a discrimination rate (95.0%) comparable to that of senior ophthalmologists who perform refractive surgery (92.8%). Meaning Corneal tomographic scanning with a deep learning algorithm may offer standardized results to reduce both the workload of surgeons and the risk of misclassification.Importance Evaluating corneal morphologic characteristics with corneal tomographic scans before refractive surgery is necessary to exclude patients with at-risk corneas and keratoconus. In previous studies, researchers performed screening with machine learning methods based on specific corneal parameters. To date, a deep learning algorithm has not been used in combination with corneal tomographic scans. Objective To examine the use of a deep learning model in the screening of candidates for refractive surgery. Design, Setting, and Participants A diagnostic, cross-sectional study was conducted at the Zhongshan Ophthalmic Center, Guangzhou, China, with examination dates extending from July 18, 2016, to March 29, 2019. The investigation was performed from July 2, 2018, to June 28, 2019. Participants included 1385 patients; 6465 corneal tomographic images were used to generate the artificial intelligence (AI) model. The Pentacam HR system was used for data collection. Interventions The deidentified images were analyzed by ophthalmologists and the AI model. Main Outcomes and Measures The performance of the AI classification system. Results A classification system centered on the AI model Pentacam InceptionResNetV2 Screening System (PIRSS) was developed for screening potential candidates for refractive surgery. The model achieved an overall detection accuracy of 94.7% (95% CI, 93.3%-95.8%) on the validation data set. Moreover, on the independent test data set, the PIRSS model achieved an overall detection accuracy of 95% (95% CI, 88.8%-97.8%), which was comparable with that of senior ophthalmologists who are refractive surgeons (92.8%; 95% CI, 91.2%-94.4%) (P = .72). In distinguishing corneas with contraindications for refractive surgery, the PIRSS model performed better than the classifiers (95% vs 81%; P < .001) in the Pentacam HR system on an Asian patient database. Conclusions and Relevance PIRSS appears to be useful in classifying images to provide corneal information and preliminarily identify at-risk corneas. PIRSS may provide guidance to refractive surgeons in screening candidates for refractive surgery as well as for generalized clinical application for Asian patients, but its use needs to be confirmed in other populations.