Bustamante-Arias, A.; Cheddad, A.; Jimenez-Perez, J.C.; Rodriguez-Garcia, A. Digital Image Processing and Development of Machine Learning Models for the Discrimination of Corneal Pathology: An Experimental Model. Photonics2021, 8, 118.
Bustamante-Arias, A.; Cheddad, A.; Jimenez-Perez, J.C.; Rodriguez-Garcia, A. Digital Image Processing and Development of Machine Learning Models for the Discrimination of Corneal Pathology: An Experimental Model. Photonics 2021, 8, 118.
Bustamante-Arias, A.; Cheddad, A.; Jimenez-Perez, J.C.; Rodriguez-Garcia, A. Digital Image Processing and Development of Machine Learning Models for the Discrimination of Corneal Pathology: An Experimental Model. Photonics2021, 8, 118.
Bustamante-Arias, A.; Cheddad, A.; Jimenez-Perez, J.C.; Rodriguez-Garcia, A. Digital Image Processing and Development of Machine Learning Models for the Discrimination of Corneal Pathology: An Experimental Model. Photonics 2021, 8, 118.
Abstract
Machine learning (ML) has a large capacity to learn and analyze a large volume of data. This study aimed to train different algorithms to discriminate between healthy and pathologic corneal images by evaluating digitally processed spectral-domain optical coherence tomography (SD-OCT) corneal images. A set of 22 SD-OCT images belonging to a random set of corneal pathologies was compared to 71 healthy corneas (control group). A binary classification method was applied; three approaches of ML were used. Once all images were analyzed, representative areas from every digital image were also processed and analyzed for a statistical feature comparison between healthy and pathologic corneas. The best performance was obtained from transfer learning - support vector machine (TL-SVM) (AUROC = 0.94, SPE 88%, SEN 100%) and transfer learning – random forest (TL- RF) method (AUROC = 0.92, SPE 84%, SEN 100%), followed by convolutional neural network (CNN) (AUROC = 0.84, SPE 77%, SEN 91%) and random forest (AUROC = 0.77, SPE 60%, SEN 95%). The highest diagnostic accuracy in classifying corneal images was achieved with the TL-SVM and the TL-RF models. In image classification, CNN was a strong predictor. This pilot experimental study developed a systematic mechanized system to discern pathologic from healthy corneas.
Keywords
Artificial intelligence; machine learning; cornea; SD-OCT; keratoconus; ectasia; keratitis; random forest, convolutional neural network; transfer learning.
Subject
Medicine and Pharmacology, Ophthalmology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
