Journal of refractive surgery (Thorofare, N.J. : 1995) · 2025
Using 3D Convolutional Neural Network and Corvis ST Corneal Dynamic Video for Detecting Forme Fruste Keratoconus.
СтатьяНазвание и аннотация: Перевод в очереди
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Rong Hua, Liu Guihua, Wang Yanling, Hu Jiamei, Sun Ziwen, Gao Nan, Kee Chea-Su, Du Bei, Wei Ruihua
Аннотация
PURPOSE: To evaluate the performance of a three-dimensional convolutional neural network (3D CNN) in detecting forme fruste keratoconus (FFKC).
METHODS: A total of 415 anonymized corneal dynamic videos were collected for this study. The video dataset consisted of 150 patients with FFKC (150 videos) and 265 normal patients (265 videos). These patients underwent comprehensive ocular examinations, including slit lamp, Pentacam (Oculus Optikgeräte GmbH), and Corvis ST (Oculus Optikgeräte GmbH), and were classified by corneal experts. A 3D CNN-based algorithm was developed to establish a FFKC detection model. The performance of the model was evaluated using metrics such as accuracy, area under the receiver operating characteristic curve (AUC), confusion matrices, and F1 score. Gradient-weighted class activation mapping (Grad-CAM) was used to observe the regions that the model attended to.
RESULTS: In the test dataset, the model achieved an accuracy of 87.95% in identifying FFKC. The ResNet3D-AUC was 0.95 with a cut-off value of 0.49, and the F1 value was 0.85. The sensitivity was 83.33% and the specificity was 90.57%.
CONCLUSIONS: Combining 3D CNN with Corvis ST corneal dynamic videos provides a new method for distinguishing between FFKC and normal corneas. This could offer valuable clinical insights and recommendations for detecting FFKC. Nevertheless, the generalizability of the model is still a concern, and external validation is required prior to its clinical implementation. [J Refract Surg. 2025;41(4):e356-e364.].
METHODS: A total of 415 anonymized corneal dynamic videos were collected for this study. The video dataset consisted of 150 patients with FFKC (150 videos) and 265 normal patients (265 videos). These patients underwent comprehensive ocular examinations, including slit lamp, Pentacam (Oculus Optikgeräte GmbH), and Corvis ST (Oculus Optikgeräte GmbH), and were classified by corneal experts. A 3D CNN-based algorithm was developed to establish a FFKC detection model. The performance of the model was evaluated using metrics such as accuracy, area under the receiver operating characteristic curve (AUC), confusion matrices, and F1 score. Gradient-weighted class activation mapping (Grad-CAM) was used to observe the regions that the model attended to.
RESULTS: In the test dataset, the model achieved an accuracy of 87.95% in identifying FFKC. The ResNet3D-AUC was 0.95 with a cut-off value of 0.49, and the F1 value was 0.85. The sensitivity was 83.33% and the specificity was 90.57%.
CONCLUSIONS: Combining 3D CNN with Corvis ST corneal dynamic videos provides a new method for distinguishing between FFKC and normal corneas. This could offer valuable clinical insights and recommendations for detecting FFKC. Nevertheless, the generalizability of the model is still a concern, and external validation is required prior to its clinical implementation. [J Refract Surg. 2025;41(4):e356-e364.].