| Summary: | 碩士 === 元智大學 === 電機工程學系甲組 === 107 === Superficial punctate keratitis is a common disease in ophthalmology. However, the traditional diagnosis method is to use the doctor's reference to the relevant scale to diagnose the fluoroscopic tear film. This method has a big problem and will be subjectively interpreted by the doctor. The size of the SPK in the fluorescent image is determined to cause the level of diagnosis by each professional physician to be different. This study proposes to use the Convolutional Neural Network (CNN) model to detect the SPK area of the fluorescent tear film and then classify its SPK level, and define it as CNN shallow keratitis grading (Convolutional Neural) Network Superficial Punctate Keratitis, CNN-SPK). The slit lamp recorded a standard fluorescent tear film image of 102 subjects. Twenty of them were used to train the CNN model to identify the features on the ocular surface, the remaining 82 were used to verify the effect of CNN-SPK, and 82 subjects extracted the fluorescence images of their left and right eyes, so there were a total of 164 images. To verify, 109 images were diagnosed by the doctor as having SPK symptoms, and the remaining 56 were subjects without SPK symptoms. The results showed that CNN-SPK was significantly lower in SPK patients than in normal subjects (P < 0.05). The correlation between CNN-SPK and the SPK grade judged by the doctor was significant (r = 0.8; P < 0.05). When the CNN-BUT uses 1020Pixel as a threshold to grade the SPK level, the threshold sensitivity and specificity of the grading are 0.84 and 0.79, respectively, which means that the CNN-SPK can be used to evaluate the SPK and accurately and automatically assess the state of the surface of the eye.
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