In International ophthalmology
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.
Tang Ningning, Huang Guangyi, Lei Daizai, Jiang Li, Chen Qi, He Wenjing, Tang Fen, Hong Yiyi, Lv Jian, Qin Yuanjun, Lin Yunru, Lan Qianqian, Qin Yikun, Lan Rushi, Pan Xipeng, Li Min, Xu Fan, Lu Peng
2023-Jan-03
Artificial intelligence (AI), Deep learning (DL), Fungal genera, Fungal keratitis (FK), In vivo confocal microscopy (IVCM)
