Background: The aim of this study was to develop an intelligent system based on a deep learning algorithm for automatically diagnosing fungal keratitis (FK) in in vivo confocal microscopy (IVCM) images.Methods: A total of 2,088 IVCM images were included in the training dataset. The positive group consisted of 688 images with fungal hyphae, and the negative group included 1,400 images without fungal hyphae. A total of 535 images in the testing dataset were not included in the training dataset. Deep Residual Learning for Image Recognition (ResNet) was used to build the intelligent system for diagnosing FK automatically. The system was verified by external validation in the testing dataset using the area under the receiver operating characteristic curve (AUC), accuracy, specificity and sensitivity.Results: In the testing dataset, 515 images were diagnosed correctly and 20 were misdiagnosed (including 6 with fungal hyphae and 14 without). The system achieved an AUC of 0.9875 with an accuracy of 0.9626 in detecting fungal hyphae. The sensitivity of the system was 0.9186, with a specificity of 0.9834. When 349 diabetic patients were included in the training dataset, 501 images were diagnosed correctly and thirtyfour were misdiagnosed (including 4 with fungal hyphae and 30 without). The AUC of the system was 0.9769.The accuracy, specificity and sensitivity were 0.9364, 0.9889 and 0.8256, respectively. Conclusions:The intelligent system based on a deep learning algorithm exhibited satisfactory diagnostic performance and effectively classified FK in various IVCM images. The context of this deep learning automated diagnostic system can be extended to other types of keratitis.

Background To evaluate the subfoveal choroidal thickness (SFCT) in eyes with macular edema (ME) secondary to retinal vein occlusion(RVO), and to investigate the short term response after a single intravitreal ranibizumab (IVR) injection. What is more, to compare SFCT and SFCT change between central RVO (CRVO) and branch RVO (BRVO). Methods In the retrospective study, we had collected 36-six treatment-naïve patients with unilateral ME secondary to RVO (including 19 CRVO and 17 BRVO). All patients had received IVR injection after newly diagnosed. The SFCT was measured at the onset and after 2 weeks of IVR injection. Paired t test was performed to compare the SFCT of RVO eyes and fellow eyes, as well as the SFCT of pre-injection and post-injection. In further, independent t test was used to compare SFCT and SFCT change between CRVO eyes and BRVO eyes. Results The mean SFCT at the onset was 326.03 ± 30.86 μm in CRVO eyes, which was significantly thicker than that in contralateral fellow eyes ( p < 0.01, paired t test), and reduced to 294.15 ± 30.83 μm rapidly after 2 weeks of IVR injection ( p < 0.01, paired t test). Similarly, the SFCT in BRVO eyes was significantly thicker than that in contralateral eyes at the onset, and decreased significantly after IVR injection. However, our findings showed that there was no statistically significant difference in SFCT and SFCT reduction after IVR injection between CRVO eyes and BRVO eyes. Conclusions The SFCT in eyes with ME secondary to CRVO and BRVO was significantly thicker than that in fellow eyes, and decreased significantly within a short time in response to a single IVR injection. In further, the study showed that SFCT and SFCT change had no correlation with RVO subtypes.

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Background: Artificial intelligence (AI) has great potential to detect fungal keratitis using in vivo confocal microscopy images, but its clinical value remains unclarified. A major limitation of its clinical utility is the lack of explainability and interpretability.Methods: An explainable AI (XAI) system based on Gradient-weighted Class Activation Mapping (Grad-CAM) and Guided Grad-CAM was established. In this randomized controlled trial, nine ophthalmologists (three expert ophthalmologists, three competent ophthalmologists, and three novice ophthalmologists) read images in each of the conditions: unassisted, AI-assisted, or XAI-assisted. In unassisted condition, only the original IVCM images were shown to the readers. AI assistance comprised a histogram of model prediction probability. For XAI assistance, explanatory maps were additionally shown. The accuracy, sensitivity, and specificity were calculated against an adjudicated reference standard. Moreover, the time spent was measured.Results: Both forms of algorithmic assistance increased the accuracy and sensitivity of competent and novice ophthalmologists significantly without reducing specificity. The improvement was more pronounced in XAI-assisted condition than that in AI-assisted condition. Time spent with XAI assistance was not significantly different from that without assistance.Conclusion: AI has shown great promise in improving the accuracy of ophthalmologists. The inexperienced readers are more likely to benefit from the XAI system. With better interpretability and explainability, XAI-assistance can boost ophthalmologist performance beyond what is achievable by the reader alone or with black-box AI assistance.

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