PDF(Pubmed)
Abstract：
Corneal infection is a major public health concern worldwide and the most common cause of unilateral corneal blindness. Toxic effects of different microorganisms, such as bacteria and fungi, worsen keratitis leading to corneal perforation even with optimal drug treatment. The cornea forms the main refractive surface of the eye. Diseases affecting the cornea can cause severe visual impairment. Therefore, it is crucial to analyze the risk of corneal perforation and visual impairment in corneal ulcer patients for making early treatment strategies. The modeling of a fully automated prognostic model system was performed in two parts. In the first part, the dataset contained 4973 slit lamp images of corneal ulcer patients in three centers. A deep learning model was developed and tested for segmenting and classifying five lesions (corneal ulcer, corneal scar, hypopyon, corneal descementocele, and corneal neovascularization) in the eyes of corneal ulcer patients. Further, hierarchical quantification was carried out based on policy rules. In the second part, the dataset included clinical data (name, gender, age, best corrected visual acuity, and type of corneal ulcer) of 240 patients with corneal ulcers and respective 1010 slit lamp images under two light sources (natural light and cobalt blue light). The slit lamp images were then quantified hierarchically according to the policy rules developed in the first part of the modeling. Combining the above clinical data, the features were used to build the final prognostic model system for corneal ulcer perforation outcome and visual impairment using machine learning algorithms such as XGBoost, LightGBM. The ROC curve area (AUC value) evaluated the model\'s performance. For segmentation of the five lesions, the accuracy rates of hypopyon, descemetocele, corneal ulcer under blue light, and corneal neovascularization were 96.86, 91.64, 90.51, and 93.97, respectively. For the corneal scar lesion classification, the accuracy rate of the final model was 69.76. The XGBoost model performed the best in predicting the 1-month prognosis of patients, with an AUC of 0.81 (95% CI 0.63-1.00) for ulcer perforation and an AUC of 0.77 (95% CI 0.63-0.91) for visual impairment. In predicting the 3-month prognosis of patients, the XGBoost model received the best AUC of 0.97 (95% CI 0.92-1.00) for ulcer perforation, while the LightGBM model achieved the best performance with an AUC of 0.98 (95% CI 0.94-1.00) for visual impairment.
摘要：
角膜感染是全球范围内的主要公共卫生问题，也是单侧角膜盲的最常见原因。不同微生物的毒性作用，比如细菌和真菌，即使使用最佳药物治疗，也会加重角膜炎，导致角膜穿孔。角膜形成眼睛的主要折射表面。影响角膜的疾病可导致严重的视力损害。因此,分析角膜溃疡患者角膜穿孔和视力损害的风险对制定早期治疗策略至关重要。全自动预后模型系统的建模分两部分进行。在第一部分,数据集包含三个中心的4973张角膜溃疡患者的裂隙灯图像.开发并测试了一种深度学习模型，用于分割和分类五种病变（角膜溃疡，角膜瘢痕,hypopyon,角膜先天性膨出，和角膜新生血管形成）在角膜溃疡患者的眼中。Further,基于政策规则进行分层量化。在第二部分,数据集包括临床数据(名称，性别,年龄,最佳矫正视力，和角膜溃疡类型）的240例角膜溃疡患者以及在两种光源（自然光和钴蓝光）下的1010个裂隙灯图像。然后根据在建模的第一部分中开发的策略规则分层地量化裂隙灯图像。结合以上临床资料，利用XGBoost等机器学习算法,构建角膜溃疡穿孔结局和视力损害的最终预后模型系统,LightGBM.ROC曲线面积(AUC值)评估模型的性能。对于五个病变的分割，hypyopyon的准确率，后代囊肿,蓝光下的角膜溃疡，角膜新生血管分别为96.86、91.64、90.51和93.97。对于角膜瘢痕病变分类，最终模型的准确率为69.76.XGBoost模型在预测患者1个月预后方面表现最好。溃疡穿孔的AUC为0.81（95％CI0.63-1.00），视力障碍的AUC为0.77（95％CI0.63-0.91）。在预测患者3个月的预后时，XGBoost模型对溃疡穿孔的最佳AUC为0.97（95％CI0.92-1.00），而LightGBM模型在视力障碍方面取得了最佳表现，AUC为0.98(95%CI0.94-1.00)。
