In the field of ophthalmology, the accurate classification of different types of keratoconus (KCN) is vital for effective surgical planning and the successful implantation of intracorneal ring segments (ICRS). During the diagnostic process, ophthalmologists are required to review demographic and clinical ophthalmic examinations to make an accurate diagnosis. This process can be time-consuming and prone to errors. This research conducted a comprehensive study on the diagnosis and treatment of different types of KCN using a novel approach that employed a few-shot learning (FSL) technique with deep learning models based on corneal topography images and the Keraring nomogram. The retrospective cross-sectional study included 268 corneal images from 175 patients who underwent keraring segments implantation and were enrolled between May 2020 and September 2022. We developed multiple transfer learning techniques and a prototypical network to identify and classify corneal disorders. The study achieved high accuracy rates ranging from 88% for AlexNet to 98% for MobileNet-V3 and GoogLeNet, and AUC values ranging from 0.96 for VGG16 to 0.99 for MNASNet, EfficientNet-V2, and GoogLeNet to classify different corneal types of KCN. The results demonstrated the potential of FSL in addressing the challenge of limited medical image datasets, providing reliable performance in accurately categorizing different types of KCN and improving surgical decision-making. Our application provided the detection of KCN patterns and proposed personalized, fully automated surgical planning for each patient, thus supplanting the former manual calculations.

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