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Purpose: To assess the performance of artificial intelligence in the automated classification of images taken with a tablet device of patients with blepharoptosis and subjects with normal eyelid. Methods: This is a prospective and observational study. A total of 1276 eyelid images (624 images from 347 blepharoptosis cases and 652 images from 367 normal controls) from 606 participants were analyzed. In order to obtain a sufficient number of images for analysis, 1 to 4 eyelid images were obtained from each participant. We developed a model by fully retraining the pre-trained MobileNetV2 convolutional neural network. Subsequently, we verified whether the automatic diagnosis of blepharoptosis was possible using the images. In addition, we visualized how the model captured the features of the test data with Score-CAM. k-fold cross-validation (k = 5) was adopted for splitting the training and validation. Sensitivity, specificity, and the area under the curve (AUC) of the receiver operating characteristic curve for detecting blepharoptosis were examined. Results: We found the model had a sensitivity of 83.0% (95% confidence interval [CI], 79.8–85.9) and a specificity of 82.5% (95% CI, 79.4–85.4). The accuracy of the validation data was 82.8%, and the AUC was 0.900 (95% CI, 0.882–0.917). Conclusion: Artificial intelligence was able to classify with high accuracy images of blepharoptosis and normal eyelids taken using a tablet device. Thus, the diagnosis of blepharoptosis with a tablet device is possible at a high level of accuracy. Trial registration: Date of registration: 2021–06-25. Trial registration number: UMIN000044660. Registration site: https://upload.umin.ac.jp/cgi-open-bin/ctr/ctr_view.cgi?recptno=R000051004 [ABSTRACT FROM AUTHOR]

Purpose: We assessed the ability of deep learning (DL) models to distinguish between tear meniscus of lacrimal duct obstruction (LDO) patients and normal subjects using anterior segment optical coherence tomography (ASOCT) images. Methods: The study included 117 ASOCT images (19 men and 98 women; mean age, 66.6 ± 13.6 years) from 101 LDO patients and 113 ASOCT images (29 men and 84 women; mean age, 38.3 ± 19.9 years) from 71 normal subjects. We trained to construct 9 single and 502 ensemble DL models with 9 different network structures, and calculated the area under the curve (AUC), sensitivity, and specificity to compare the distinguishing abilities of these single and ensemble DL models. Results: For the highest single DL model (DenseNet169), the AUC, sensitivity, and specificity for distinguishing LDO were 0.778, 64.6%, and 72.1%, respectively. For the highest ensemble DL model (VGG16, ResNet50, DenseNet121, DenseNet169, InceptionResNetV2, InceptionV3, and Xception), the AUC, sensitivity, and specificity for distinguishing LDO were 0.824, 84.8%, and 58.8%, respectively. The heat maps indicated that these DL models placed their focus on the tear meniscus region of the ASOCT images. Conclusion: The combination of DL and ASOCT images could distinguish between tear meniscus of LDO patients and normal subjects with a high level of accuracy. These results suggest that DL might be useful for automatic screening of patients for LDO. [ABSTRACT FROM AUTHOR]

Purpose: To determine whether water drinking test (WDT) alters choroidal structure using binarization of enhanced depth imaging optical coherence tomographic (EDI-OCT) images. Methods: We performed a prospective study of 57 eyes of 57 normal subjects. The intraocular pressure (IOP), EDI-OCT images, and laser speckle flowgraphic images were recorded at baseline, and at 15, 30, 45, and 120 min after the WDT. The EDI-OCT images were converted to binary images using ImageJ software, and we examined luminal area, interstitial area, whole choroidal area, the ratio of luminal area to whole choroidal area (L/W ratio), subfoveal choroidal thickness (SCT), and central retinal thickness (CRT). Results: The luminal area, L/W ratio, whole choroidal area, and IOP were significantly increased 30 min after water drinking; levels returned to the baseline at 120 min. No significant changes were found in the CRT and interstitial area. The fluctuations in the SCT after water intake were significantly correlated with those in the L/W ratio and luminal area but not with those of the interstitial area. The choroidal blood flow velocity was significantly decreased at 30 min. Fluctuations in the luminal area, L/W ratio, and whole choroidal area were significantly correlated with IOP fluctuations. Conclusions: The changes in the SCT after water drinking were mainly due to the changes in the choroidal vascular space. Dilatations of the choroidal vessels after water drinking may lead to choroidal thickening and subsequent IOP elevation. These findings should be considered in the evaluation of choroidal structure in patients with retinal disease. [ABSTRACT FROM AUTHOR]