Wei Shanshan, Wang Yuexin, Shi Faqiang, Sun Siman, Li Xuemin
Beijing Keynote Laboratory of Ophthalmology and Visual Science, Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing 100069, China.
Beijing Key Laboratory of Restoration of Damaged Ocular Nerve, Department of Ophthalmology, Peking University Third Hospital, Beijing 100191, China.
J Clin Med. 2023 Jan 16;12(2):715. doi: 10.3390/jcm12020715.
The present research aims to evaluate the feasibility of a deep-learning model in identifying bulbar conjunctival injection grading. Methods: We collected 1401 color anterior segment photographs demonstrating the cornea and bulbar conjunctival. The ground truth was bulbar conjunctival injection scores labeled by human ophthalmologists. Two convolutional neural network-based models were constructed and trained. Accuracy, precision, recall, F1-score, Kappa, and the area under the curve (AUC) were calculated to evaluate the efficiency of the deep learning models. The micro-average and macro-average AUC values for model grading bulbar conjunctival injection were 0.98 and 0.98, respectively. The deep learning model achieved a high accuracy of 87.12%, a precision of 87.13%, a recall of 87.12%, an F1-score of 87.07%, and Cohen's Kappa of 0.8153. The deep learning model demonstrated excellent performance in evaluating the severity of bulbar conjunctival injection, and it has the potential to help evaluate ocular surface diseases and determine disease progression and recovery.
本研究旨在评估深度学习模型在识别球结膜充血分级方面的可行性。方法：我们收集了1401张显示角膜和球结膜的彩色眼前节照片。真实情况是由眼科医生标注的球结膜充血评分。构建并训练了两个基于卷积神经网络的模型。计算准确率、精确率、召回率、F1分数、卡帕值和曲线下面积（AUC）以评估深度学习模型的效率。模型对球结膜充血分级的微平均AUC值和宏平均AUC值分别为0.98和0.98。深度学习模型的准确率高达87.12%，精确率为87.13%，召回率为87.12%，F1分数为87.07%，科恩卡帕值为0.8153。深度学习模型在评估球结膜充血严重程度方面表现出色，并且有潜力帮助评估眼表疾病以及确定疾病进展和恢复情况。
