Dataset Information

Predicting Keratoconus Progression and Need for Corneal Crosslinking Using Deep Learning.

ABSTRACT: We aimed to predict keratoconus progression and the need for corneal crosslinking (CXL) using deep learning (DL). Two hundred and seventy-four corneal tomography images taken by Pentacam HR^® (Oculus, Wetzlar, Germany) of 158 keratoconus patients were examined. All patients were examined two times or more, and divided into two groups; the progression group and the non-progression group. An axial map of the frontal corneal plane, a pachymetry map, and a combination of these two maps at the initial examination were assessed according to the patients' age. Training with a convolutional neural network on these learning data objects was conducted. Ninety eyes showed progression and 184 eyes showed no progression. The axial map, the pachymetry map, and their combination combined with patients' age showed mean AUC values of 0.783, 0.784, and 0.814 (95% confidence interval (0.721-0.845) (0.722-0.846), and (0.755-0.872), respectively), with sensitivities of 87.8%, 77.8%, and 77.8% ((79.2-93.7), (67.8-85.9), and (67.8-85.9)) and specificities of 59.8%, 65.8%, and 69.6% ((52.3-66.9), (58.4-72.6), and (62.4-76.1)), respectively. Using the proposed DL neural network model, keratoconus progression can be predicted on corneal tomography maps combined with patients' age.

SUBMITTER: Kato N

PROVIDER: S-EPMC7923054 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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Predicting Keratoconus Progression and Need for Corneal Crosslinking Using Deep Learning.

Kato Naoko N Masumoto Hiroki H Tanabe Mao M Sakai Chikako C Negishi Kazuno K Torii Hidemasa H Tabuchi Hitoshi H Tsubota Kazuo K

Journal of clinical medicine 20210218 4

We aimed to predict keratoconus progression and the need for corneal crosslinking (CXL) using deep learning (DL). Two hundred and seventy-four corneal tomography images taken by Pentacam HR<sup>®</sup> (Oculus, Wetzlar, Germany) of 158 keratoconus patients were examined. All patients were examined two times or more, and divided into two groups; the progression group and the non-progression group. An axial map of the frontal corneal plane, a pachymetry map, and a combination of these two maps at ...[more]

PMID: 33670732

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Project description:IntroductionThe primary objective of this study was to develop an end-to-end model that can accurately identify corneal endothelial cells and diagnose keratoconus based on corneal endothelial images acquired from a non-contact specular microscope.MethodsThis was a retrospective case-control study performed at the Refractive Surgery Center of West China Hospital. A total of 403 keratoconus eyes (221 patients) and 370 myopic eyes (185 normal controls) were consecutively recruited from January 2021 to September 2022. Specular microscopy was used to image and measure the morphometric parameters of the corneal endothelial cells. A Fully Convolutional Network model with a ResNet50 (FCN_ResNet50) was established to perform the endothelial segmentation. The images were then classified using an ensemble machine learning system consisting of four pre-trained deep learning networks: DenseNet121, ResNet50, Inception_v3, and MobileNet_v2. The performance of the models was evaluated based on different metrics, such as accuracy, intersection over union (IoU), and mean IoU.ResultsWe established a fully end-to-end deep-learning model for the segmentation of endothelial and diagnosis of keratoconus. For endothelial segmentation, the accuracy of the FCN_ResNet50 model achieved near 90% with mean IoU converging to about 80%. The ensemble machine learning system can achieve over 92% accuracy, and > 98% area under curve (AUC) values to diagnose keratoconus with endothelial cell images. In addition, we constructed a diagnostic model based on deep-learning features and developed an associated nomogram which manifested an excellent performance for diagnosis and monitoring the progression of keratoconus.ConclusionsOur research developed an end-to-end model to automatically identify and assess corneal endothelial morphological changes in keratoconus eyes. Moreover, we also constructed a novel nomogram, which can provide valuable information for the diagnosis, monitoring, and management of the disease.

Dataset Information

Predicting Keratoconus Progression and Need for Corneal Crosslinking Using Deep Learning.

Publications

Predicting Keratoconus Progression and Need for Corneal Crosslinking Using Deep Learning.

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