The Association Between Sleep Disorders and Incidence of Dry Eye Disease in Ningbo: Data From an Integrated Health Care Network.

PURPOSE: To investigate the association between sleep disorders and dry eye disease (DED) in Ningbo, China. METHODS: Our data came from the Yinzhou Health Information System (HIS), including 257932 patients and was based on a 1:1 matching method (sleep disorder patients vs. patients without sleep disorders) during 2013-2020. Sleep disorders and DED were identified using ICD-10 codes. Cox proportional hazards regression was used to identify the association between sleep disorders and DED. RESULTS: The eight-year incidence of DED was significantly higher in participants with diagnosis of sleep disorders (sleep disorders: 50.66%, no sleep disorders: 16.48%, P < 0.01). Sleep disorders were positively associated with the diagnosis of DED (HR: 3.06, 95% CI: 2.99-3.13, P < 0.01), when sex, age, hypertension, diabetes and other systemic diseases were adjusted. In the sleep disorders patients, advancing age, female sex, and presence of coexisting disease (hypertension, diabetes, hyperlipidemia, thyroid disease, depression, heart disease, and arthritis) were significantly associated with the development of DED by the multivariate cox regression analysis (all P < 0.05).In addition, there was a significantly positive association between estazolam and the incidence of DED in both sleep disorder and non-sleep disorder groups (all P < 0.05). CONCLUSIONS: Sleep disrder was associated with a three-time increased risk of DED. This association can be helpful in effective management of both sleep disorders and DED.

Comparison of deep learning systems and cornea specialists in detecting corneal diseases from low-quality images.

The performance of deep learning in disease detection from high-quality clinical images is identical to and even greater than that of human doctors. However, in low-quality images, deep learning performs poorly. Whether human doctors also have poor performance in low-quality images is unknown. Here, we compared the performance of deep learning systems with that of cornea specialists in detecting corneal diseases from low-quality slit lamp images. The results showed that the cornea specialists performed better than our previously established deep learning system (PEDLS) trained on only high-quality images. The performance of the system trained on both high- and low-quality images was superior to that of the PEDLS while inferior to that of a senior corneal specialist. This study highlights that cornea specialists perform better in low-quality images than the system trained on high-quality images. Adding low-quality images with sufficient diagnostic certainty to the training set can reduce this performance gap.

Preventing corneal blindness caused by keratitis using artificial intelligence.

Keratitis is the main cause of corneal blindness worldwide. Most vision loss caused by keratitis can be avoidable via early detection and treatment. The diagnosis of keratitis often requires skilled ophthalmologists. However, the world is short of ophthalmologists, especially in resource-limited settings, making the early diagnosis of keratitis challenging. Here, we develop a deep learning system for the automated classification of keratitis, other cornea abnormalities, and normal cornea based on 6,567 slit-lamp images. Our system exhibits remarkable performance in cornea images captured by the different types of digital slit lamp cameras and a smartphone with the super macro mode (all AUCs>0.96). The comparable sensitivity and specificity in keratitis detection are observed between the system and experienced cornea specialists. Our system has the potential to be applied to both digital slit lamp cameras and smartphones to promote the early diagnosis and treatment of keratitis, preventing the corneal blindness caused by keratitis.

Development of a deep learning-based image quality control system to detect and filter out ineligible slit-lamp images: A multicenter study.

BACKGROUND AND OBJECTIVE: Previous studies developed artificial intelligence (AI) diagnostic systems only using eligible slit-lamp images for detecting corneal diseases. However, images of ineligible quality (including poor-field, defocused, and poor-location images), which are inevitable in the real world, can cause diagnostic information loss and thus affect downstream AI-based image analysis. Manual evaluation for the eligibility of slit-lamp images often requires an ophthalmologist, and this procedure can be time-consuming and labor-intensive when applied on a large scale. Here, we aimed to develop a deep learning-based image quality control system (DLIQCS) to automatically detect and filter out ineligible slit-lamp images (poor-field, defocused, and poor-location images). METHODS: We developed and externally evaluated the DLIQCS based on 48,530 slit-lamp images (19,890 individuals) that were derived from 4 independent institutions using different types of digital slit lamp cameras. To find the best deep learning model for the DLIQCS, we used 3 algorithms (AlexNet, DenseNet121, and InceptionV3) to train models. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy were leveraged to assess the performance of each algorithm for the classification of poor-field, defocused, poor-location, and eligible images. RESULTS: In an internal test dataset, the best algorithm DenseNet121 had AUCs of 0.999, 1.000, 1.000, and 1.000 in the detection of poor-field, defocused, poor-location, and eligible images, respectively. In external test datasets, the AUCs of the best algorithm DenseNet121 for identifying poor-field, defocused, poor-location, and eligible images were ranged from 0.997 to 0.997, 0.983 to 0.995, 0.995 to 0.998, and 0.999 to 0.999, respectively. CONCLUSIONS: Our DLIQCS can accurately detect poor-field, defocused, poor-location, and eligible slit-lamp images in an automated fashion. This system may serve as a prescreening tool to filter out ineligible images and enable that only eligible images would be transferred to the subsequent AI diagnostic systems.

Development of a deep learning-based image eligibility verification system for detecting and filtering out ineligible fundus images: A multicentre study.

BACKGROUND: Recent advances in artificial intelligence (AI) have shown great promise in detecting some diseases based on medical images. Most studies developed AI diagnostic systems only using eligible images. However, in real-world settings, ineligible images (including poor-quality and poor-location images) that can compromise downstream analysis are inevitable, leading to uncertainty about the performance of these AI systems. This study aims to develop a deep learning-based image eligibility verification system (DLIEVS) for detecting and filtering out ineligible fundus images. METHODS: A total of 18,031 fundus images (9,188 subjects) collected from 4 clinical centres were used to develop and evaluate the DLIEVS for detecting eligible, poor-location, and poor-quality fundus images. Four deep learning algorithms (AlexNet, DenseNet121, Inception V3, and ResNet50) were leveraged to train models to obtain the best model for the DLIEVS. The performance of the DLIEVS was evaluated using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity, as compared with a reference standard determined by retina experts. RESULTS: In the internal test dataset, the best algorithm (DenseNet121) achieved AUCs of 1.000, 0.999, and 1.000 for the classification of eligible, poor-location, and poor-quality images, respectively. In the external test datasets, the AUCs of the best algorithm (DenseNet121) for detecting eligible, poor-location, and poor-quality images were ranged from 0.999-1.000, 0.997-1.000, and 0.997-0.999, respectively. CONCLUSIONS: Our DLIEVS can accurately discriminate poor-quality and poor-location images from eligible images. This system has the potential to serve as a pre-screening technique to filter out ineligible images obtained from real-world settings, ensuring only eligible images will be applied in the subsequent image-based AI diagnostic analyses.