Peng Lu

Précis: Biometric parameters, including binocular difference of anterior chamber depth (ACD), lens vault (LV) in affected eyes, and binocular difference of the LV, had high efficiency in diagnosing acute angle closure (AAC) with zonular laxity. Purpose: To investigate the ocular biometric characteristics of eyes with AAC with zonular laxity to further explore the sensitive parameters for preoperative diagnosis. Methods: This study included 50 patients with AAC with zonular laxity and 54 patients with AAC without zonular laxity. Demographic data, ocular examination results, and biometric parameters on ultrasound biomicroscopy images were compared between the affected and fellow eyes in 2 groups. Parameters significant in the multiple linear regression model were included in a regression equation and the diagnostic efficiency was evaluated by area under the curve. Results: In patients with AAC with zonular laxity, the binocular difference of central ACD, LV in affected eyes, and binocular difference of the LV were significantly larger than those in patients without zonular laxity respectively and these three parameters were all significant in multiple linear regression analysis (all PP/(1−P))=−4.322 + 1.222 [LV in affected eyes (mm)] + 3.657 [binocular difference of LV (mm)] + 6.542 [binocular difference of ACD (mm)], with the accuracy of prediction reaching 94.05%. Conclusion: Binocular difference of ACD, LV in affected eyes, and binocular difference of LV had high efficiency in diagnosing AAC with zonular laxity.

Purpose: Accurate identification of corneal layers with in vivo confocal microscopy (IVCM) is essential for the correct assessment of corneal lesions. This project aims to obtain a reliable automated identification of corneal layers from IVCM images. Methods: A total of 7957 IVCM images were included for model training and testing. Scanning depth information and pixel information of IVCM images were used to build the classification system. Firstly, two base classifiers based on convolutional neural networks and K-nearest neighbors were constructed. Second, two hybrid strategies, namely weighted voting method and light gradient boosting machine (LightGBM) algorithm were used to fuse the results from the two base classifiers and obtain the final classification. Finally, the confidence of prediction results was stratified to help find out model errors. Results: Both two hybrid systems outperformed the two base classifiers. The weighted area under the curve, weighted precision, weighted recall, and weighted F1 score were 0.9841, 0.9096, 0.9145, and 0.9111 for weighted voting hybrid system, and were 0.9794, 0.9039, 0.9055, and 0.9034 for the light gradient boosting machine stacking hybrid system, respectively. More than one-half of the misclassified samples were found using the confidence stratification method. Conclusions: The proposed hybrid approach could effectively integrate the scanning depth and pixel information of IVCM images, allowing for the accurate identification of corneal layers for grossly normal IVCM images. The confidence stratification approach was useful to find out misclassification of the system. Translational Relevance: The proposed hybrid approach lays important groundwork for the automatic identification of the corneal layer for IVCM images.

Purpose Fungal keratitis is a common cause of blindness worldwide. Timely identification of the causative fungal genera is essential for clinical management. In vivo confocal microscopy (IVCM) provides useful information on pathogenic genera. This study attempted to apply deep learning (DL) to establish an automated method to identify pathogenic fungal genera using IVCM images. Methods Deep learning networks were trained, validated, and tested using a data set of 3364 IVCM images that collected from 100 eyes of 100 patients with culture-proven filamentous fungal keratitis. Two transfer learning approaches were investigated: one was a combined framework that extracted features by a DL network and adopted decision tree (DT) as a classifier; another was a complete supervised DL model which used DL-based fully connected layers to implement the classification. Results The DL classifier model revealed better performance compared with the DT classifier model in an independent testing set. The DL classifier model showed an area under the receiver operating characteristic curves (AUC) of 0.887 with an accuracy of 0.817, sensitivity of 0.791, specificity of 0.831, G-mean of 0.811, and F1 score of 0.749 in identifying Fusarium, and achieved an AUC of 0.827 with an accuracy of 0.757, sensitivity of 0.756, specificity of 0.759, G-mean of 0.757, and F1 score of 0.716 in identifying Aspergillus. Conclusion The DL model can classify Fusarium and Aspergillus by learning effective features in IVCM images automatically. The automated IVCM image analysis suggests a noninvasive identification of Fusarium and Aspergillus with clear potential application in early diagnosis and management of fungal keratitis.