Applications of deep learning for detecting ophthalmic diseases with ultrawide-field fundus

AIM:To summarize the application of deep learning in detecting ophthalmic disease with ultrawide-field fundus images and analyze the advantages,limitations,and possible solutions common to all tasks.METHODS:We searched three academic databases,including PubMed,Web of Science,and Ovid,with the date of August 2022.We matched and screened according to the target keywords and publication year and retrieved a total of 4358 research papers according to the keywords,of which 23 studies were retrieved on applying deep learning in diagnosing ophthalmic disease with ultrawide-field images.RESULTS:Deep learning in ultrawide-field images can detect various ophthalmic diseases and achieve great performance,including diabetic retinopathy,glaucoma,age-related macular degeneration,retinal vein occlusions,retinal detachment,and other peripheral retinal diseases.Compared to fundus images,the ultrawide-field fundus scanning laser ophthalmoscopy enables the capture of the ocular fundus up to 200°in a single exposure,which can observe more areas of the retina.CONCLUSION:The combination of ultrawide-field fundus images and artificial intelligence will achieve great performance in diagnosing multiple ophthalmic diseases in the future.

Research Progress in the Application of Lycium Barbarum Polysaccharide in Ophthalmic Diseases

Lycium barbarum polysaccharides(LBP)are widely used in age-related macular degeneration,glaucoma,diabetic retinopathy,and other eye diseases.With further studies on the pharmacological action of Lycium barbarum polysaccharides in recent years,researchers have found that Lycium barbarum polysaccharides(LBP)play a protective role in the treatment of various eye diseases through anti-oxidation,scavenging oxygen free radicals,and inhibiting inflammatory reaction as well as cell senescence.This paper reviews the application of Lycium barbarum polysaccharides in ophthalmology.

Application of ultrasound microbubble contrast technology in ophthalmic targeted therapy:literature analysis

AIM: To analyze the application of microbubble contrast technology in the treatment of ophthalmic diseases, mainly analyzing its advantages and existing problems. METHODS: A total of 30 representative literatures about the application of ultrasound contrast agent in gene targeted therapy at home and abroad were collected, and focusing on sorting out the literature reporting the treatment of ophthalmic diseases with microbubble contrast technology in recent years, then recalling its advantages and problems, finally making reasonable assessment on existing problems and proposing possible solutions to the problems. RESULTS: Due to its unique safety and efficacy, the treatment of ophthalmic diseases with microbubble contrast technology has increasingly drawn the attention of clinicians, but two relevant issues should be considered: first, the nature of contrast agent and the choice of corresponding ultrasound parameters; second, relative incidence of tissue bleeding, intravascular hemolysis, moderate or severe allergy as well as other side effects. CONCLUSION: Microbubble may become the carrier of targeted therapy, and as a kind of new non-invasive delivery system, the ultrasound contrast agent has broad application prospects, but its application in ophthalmic research is still in its initial stage and the safety of contrast-enhanced ultrasound still needs further study.

Customized anterior segment photoacoustic imaging for ophthalmic burn evaluation in vivo

Photoacoustic imaging has many advantages in ophthalmic application including high-resolution,requirement of no exogenous contrast agent,and noninvasive acquisition of both morphologic and functional information.However,due to the limited depth of focus of the imaging method and large curvature of the eye,it remains a challenge to obtain high quality vascular image of entire anterior segment.Here,we proposed a new method to achieve high quality imaging of anterior segment.The new method applied a curvature imaging strategy based on only one time scanning,and hence is time efficient and more suitable for ophthalmic imaging compared to previously reported methods using similar strategy.A custom-built photoacoustic imaging system was adapted for ophthalmic application and a customized image processing method was developed to quantitatively analyze both morphologic and functional information in vasculature of the anterior segment.The results showed that the new method improved the image quality of anterior segment significantly compared to that of conventional high resolution photoacoustic imaging.More importantly,we applied the new method to study ophthalmic disease in an in vivo mouse model for the first time.The results verified the suitability and advantages of the new method for imaging the entire anterior segment and the numerous potentials of applying it in ophthalmic imaging in future.

A clinicopathological study on IgG4-related ophthalmic disease

AIM： To investigate clinicopathological features of immunoglobulin G4-related ophthalmic disease（IgG4-ROD）, and analyze the recurrence rates following systemic corticosteroid administration.METHODS： We retrospectively searched clinical features, laboratory and histological findings based on the medical records of 21 patients with IgG4-ROD. All the patients examined in this study underwent surgical resection in the ocular adnexal lesions and underwent histological evaluation. This study further investigated clinical and histopathological features of 15 patients who received systemic corticosteroid after the resection.RESULTS： The mean age of the patients consisting of 7 males（33%） and 14 females（67%） was 61y. Fourteen patients were diagnosed as definitive, and 2 and 5 patients were probable and possible IgG4-ROD, respectively. Eyelid swelling was an initial symptom in 11 patients（52%） who did not show systemic involvements at a diagnosis. Fifteen patients received systemic corticosteroid administration, and all showed remission of inflammation. Among them, 10 patients did not recur, whereas 5 patients（33%） recurred during tapering. There were no significant difference between patients with or without recurrence in clinicopathological features.CONCLUSION： In this study, female patients are more predominant in IgG4-ROD. While inflammation recurs in one-third of patients, this study do not identify factors associated with recurrence after systemic corticosteroid administration.

M^(2)LC-Net: A Multi-Modal Multi-Disease Long-Tailed Classification Network for Real Clinical Scenes

Leveraging deep learning-based techniques to classify diseases has attracted extensive research interest in recent years.Nevertheless,most of the current studies only consider single-modal medical images,and the number of ophthalmic diseases that can be classified is relatively small.Moreover,imbalanced data distribution of different ophthalmic diseases is not taken into consideration,which limits the application of deep learning techniques in realistic clinical scenes.In this paper,we propose a Multimodal Multi-disease Long-tailed Classification Network(M^(2)LC-Net)in response to the challenges mentioned above.M^(2)LC-Net leverages ResNet18-CBAM to extract features from fundus images and Optical Coherence Tomography(OCT)images,respectively,and conduct feature fusion to classify 11 common ophthalmic diseases.Moreover,Class Activation Mapping(CAM)is employed to visualize each mode to improve interpretability of M^(2)LC-Net.We conduct comprehensive experiments on realistic dataset collected from a Grade III Level A ophthalmology hospital in China,including 34,396 images of 11 disease labels.Experimental results demonstrate effectiveness of our proposed model M^(2)LC-Net.Compared with the stateof-the-art,various performance metrics have been improved significantly.Specifically,Cohen’s kappa coefficient κ has been improved by 3.21%,which is a remarkable improvement.