Su, Tai YuanTai YuanSuTing, Peng JenPeng JenTingSHU-WEN CHANGChen, Duan YuDuan YuChen2024-01-222024-01-222020-02-011530437Xhttps://scholars.lib.ntu.edu.tw/handle/123456789/638749Ocular surface damage is a major characteristic of dry eye syndrome. Ocular surface damage caused from dry eye refers to that there is superficial punctate keratitis (SPK), or also called the punctate dots, on the ocular surface. In the current diagnostic methods such as the ocular surface fluorescein-staining test, ophthalmologists dye the ocular surface to visualize punctate dots and then identify as well as count them for grading. Based on the grading results, ophthalmologists conduct a further diagnosis. For an expert, it is hard to achieve consistent results. Method: This study proposed to train a deep CNN (convolutional neural network) model to automatically detect punctate dots. Then we obtain a value, called the CNN-SPK value, which represents the coverage of punctate dots. Standard fluorescein-staining images from 101 participants were collected. Results: The correlation between the estimated CNN-SPK values of the rest 81 participants and the clinical grades were significant (r = 0.85; p < 0.05). Based on this observation, we suggest a statistical approach for the final grading. Using CNN-SPK values from 81 participants, as well as the corresponding clinical grades, we find CNN-SPK thresholds between any two adjacent grades. Also, we obtain the threshold between with- and without- SPK-symptoms, leading to 0.94 in sensitivity, and 0.79 in specificity. Conclusion: Our automatic method may be used to reliably grade the severity of punctate dots, to improve the efficiency of the dry diagnosis.Artificial intelligence | medical diagnostic imaging | medical information systems | multi-layer neural network | superficial punctate keratitisjournal article10.1109/JSEN.2019.29485762-s2.0-85078531414https://api.elsevier.com/content/abstract/scopus_id/85078531414