Objective

To apply artificial intelligence (AI) for automated identification of corneal condition and prediction of the likelihood of need for future keratoplasty intervention from optical coherence tomography (OCT)-based corneal parameters.

Design

Cohort study.

Participants

We collected 12,242 corneal OCT images from 3162 subjects using CASIA OCT Imaging Systems (Tomey, Japan). We included 3318 measurements collected at the baseline visit of each patient. A total of 333 eyes had post-operative penetrating keratoplasty (PKP), lamellar keratoplasty (LKP), deep anterior keratoplasty (DALK), descemet's stripping automated endothelial keratoplasty (DSAEK) or descemet's membrane endothelial keratoplasty (DMEK) intervention.

Method

We developed a pipeline including linear and nonlinear data transformations followed by unsupervised machine learning and applied on corneal parameters from the baseline visit of each patient. Five non-overlapping clusters of eyes were identified. Post hoc analyses revealed that clusters corresponded to different likelihoods of need for future keratoplasty. These clusters on a 2-dimensional map can be used by clinicians and surgeons to identify patients with higher risk of need for future keratoplasty intervention.

Main outcome measures

The likelihood of the need for future surgery.

Results

The mean age of participants was 69.7 (standard deviation; SD = 16.1) and 59% were female. The normalized likelihood of need for future corneal keratoplasty intervention for eyes mapped onto clusters one to five were 2.2%, 1.0%, 33.1%, 32.7%, and 31.0%, respectively.

Conclusions

The AI system can assist the (cornea) surgeon in identifying those patients who may be at higher risk for future keratoplasty using comprehensive corneal shape, thickness, and elevation parameters. Future research utilizing independent datasets is necessary to validate the proposed system.

中文翻译：

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使用人工智能预测将来需要进行角膜移植手术的可能性。

目的

要应用人工智能（AI）来自动识别角膜状况，并从基于光学相干断层扫描（OCT）的角膜参数预测未来角膜移植术的需求可能性。

设计

队列研究。

参加者

我们使用CASIA OCT Imaging Systems（日本，托米）从3162名受试者中收集了12,242张角膜OCT图像。我们纳入了每位患者基线访视时收集的3318个测量值。共有333眼接受了术后穿透性角膜移植术（PKP），层状角膜移植术（LKP），深部前角膜移植术（DALK），descemet的剥离自动内皮角膜移植术（DSAEK）或descemet的膜内皮内皮角膜移植术（DMEK）干预。

方法

我们开发了一个包括线性和非线性数据转换，然后进行无监督机器学习的流水线，并从每个患者的基线访视中将其应用于角膜参数。确定了五个不重叠的眼睛簇。事后分析显示，簇对应于将来进行角膜移植术的不同可能性。临床医生和外科医生可以使用二维图上的这些聚类来确定需要将来进行角膜移植手术干预的较高风险的患者。

主要观察指标

将来需要手术的可能性。

结果

参与者的平均年龄为69.7（标准差； SD = 16.1），女性为59％。映射到群集1到5上的眼睛将来需要进行角膜角膜移植手术的标准化可能性分别为2.2％，1.0％，33.1％，32.7％和31.0％。

结论

AI系统可以使用综合的角膜形状，厚度和高度参数，帮助（角膜）外科医生识别出将来可能面临较高角膜移植术风险的患者。未来需要利用独立的数据集进行研究，以验证所提出的系统。