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Myopia is a serious threat to eye health and can even cause blindness. It is important to grade myopia and carry out targeted intervention. Nowadays, various studies using deep learning models based on optical coherence tomography (OCT) images to screen for high myopia. However, since regions of interest (ROIs) of pre-myopia and low myopia on OCT images are relatively small, it is rather difficult to use OCT images to conduct detailed myopia grading. There are few studies using OCT images for more detailed myopia grading. To address these problems, we propose a novel attention-based multi-scale feature fusion network named AMFF for myopia grading using OCT images. The proposed AMFF mainly consists of five modules: a pre-trained vision transformer (ViT) module, a multi-scale convolutional module, an attention feature fusion module, an Avg-TopK pooling module and a fully connected (FC) classifier. Firstly, unsupervised pre-training of ViT on the training set can better extract feature maps. Secondly, multi-scale convolutional layers further extract multi-scale feature maps to obtain more receptive fields and extract scale-invariant features. Thirdly, feature maps of different scales are fused through channel attention and spatial attention to further obtain more meaningful features. Lastly, the most prominent features are obtained by the weighted average of the highest activation values of each channel, and then they are used to classify myopia through a fully connected layer. Extensive experiments show that our proposed model has the superior performance compared with other state-of-the-art myopia grading models. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy (DR) is a common complication of diabetes which may lead to blindness. Early diagnosis can effectively prevent the deterioration of the disease and enable timely treatment. Ophthalmologists diagnose DR by observing ultra-wide optical coherence tomography angiography (UW-OCTA) images, which visualize unprecedented detail of DR lesions. In this paper, we propose an end-to-end task-specific network (TSNet) for joint DR grading and lesion segmentation of UW-OCTA images. Specifically, we design task-specific attention block to generate task-specific feature maps for respective segmentation and classification tasks. Furthermore, we devise task-specific fusion block to fuse the original task-specific feature map and augmented task-specific feature map for the following segmentation and classification decoders to generate DR lesion predictive mask and DR grading predictive result. Experiments on a public-available UW-OCTA dataset demonstrate that our model outperforms state-of-the-art (SOTA) multi-task models and achieves promising results on both DR lesion segmentation and DR grading classification tasks [ABSTRACT FROM AUTHOR]

Myopia, a prevalent refractive error leading to visual impairment, presents a substantial global health challenge. Current preventive measures, including pharmaceutical interventions and optical corrections, require significant resources and a dedicated workforce. Digital healthcare technology, encompassing artificial intelligence (AI) and telemedicine, offers promising solutions by delivering scalable, portable, and dependable approaches. Inspired by the success of deep learning systems in ophthalmology, these digital health technologies can revolutionize myopia management, enhancing automation, scalability, and remote monitoring. In this comprehensive review, we explore the potential applications of digital healthcare technology in measuring, preventing, controlling, and managing myopia. We highlight specific focus areas, emphasize the advancements and benefits of digital healthcare, and discuss the challenges associated with the clinical implementation of digital devices and AI applications in myopia management. Realizing the full potential of digital technologies in myopia management necessitates addressing access barriers, seamless integration with digital healthcare systems, validation of their effectiveness, and fostering user engagement. While adoption challenges persist, harnessing digital healthcare solutions can lead to improved vision, reduced myopia-related difficulties, and overall enhanced eye health. As this field advances, it holds a promising future for individuals affected by myopia. [ABSTRACT FROM AUTHOR]

As the Internet of Things continues to grow and thrive, more and more data are consumed by machines for intelligent analysis. How to simultaneously support fast machine vision analysis and obtain high-quality reconstructed images to serve human vision has become a problem that needs to be solved. We propose a fast face image compression scheme based on a Joint Super-Resolution Network (JSRNet), where images are encoded hierarchically to support machine vision and human vision. To support fast machine vision tasks, we compress the downsampled version of the input image at the encoding side and implement base layer decoding at the decoding side with a lightweight super-resolution network, thus effectively reducing the codec time. To obtain high-quality reconstructed images, we design a content transfer module to transfer the visual information generated by the base layer to the enhancement layer. The joint optimization of the base layer and the enhancement layer also allows them to better cooperate to achieve better performance. We verify the performance of this scheme on the facial landmark detection task, and the experimental results show that our method saves more bit-rate and achieves more accurate facial landmark detection results with faster codec time than baselines. The reconstructed images are more in line with human visual characteristics, and the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) are also improved significantly. [ABSTRACT FROM AUTHOR]

Hypertensive retinopathy (HR) can potentially lead to vision loss if left untreated. Early screening and treatment are critical in reducing the risk of vision loss. The computer-aided diagnostic system presents an opportunity to improve the efficiency and reliability of HR screening and diagnosis, particularly given the shortage of specialized medical professionals and the challenges faced by primary care physicians in making precise diagnoses. A notable barrier to the development of such diagnostic algorithms is the lack of publicly available benchmarks and datasets. To address these issues, we organized a challenge named “HRDC—Hypertensive Retinopathy Diagnosis Challenge” in conjunction with the Computer Graphics International (CGI) 2023 conference. The challenge provided a fundus image dataset for two clinical tasks: hypertension classification and HR classification, with each task containing 1000 images. This paper presents a concise summary and analysis of the submitted methods and results for the two challenge tasks. For hypertension classification, the best performing algorithm achieved a Kappa score of 0.3819, an F1 score of 0.6337, and a specificity of 0.8472. For HR classification, the best performing algorithm achieved a Kappa score of 0.4154, an F1 score of 0.6122, and a specificity of 0.8444. We also explored an ensemble approach to the top-ranking methods, which further improved performance beyond the individual best performing algorithm for each task. The challenge results show that there is room for further optimization of these methods, but the insights and methodologies derived from this challenge provide valuable directions for developing more precise and reliable classification models for hypertension and HR. [ABSTRACT FROM AUTHOR]

Swept-source optical coherence tomography (SS-OCT) is widely used to diagnose high myopia due to its advantage in imaging the ocular anatomic structures. Although the scleral deformation provides information on the risk of the high myopia, further validation of these highly promising findings in clinical studies has been limited by the current semi-automated software, which requires human input, and the automatic analysis of the scleral structure is quite challenging due to the ambiguous boundaries. To address these challenges, we propose a deep contour attention network (DCANet) for automatic segmentation of scleral deformation structure. Specifically, we design a scale-aware attention feature fusion module to achieve cross-scale feature fusion, which can facilitate the network to learn complementary information from multi-scale features. In addition, we develop a pyramid feature enhancement module to allow the network to learn global contextual features through the combination of receptive field and attention mechanism, and we also propose a boundary heatmap label to enrich boundary information. We evaluate the performance of the proposed method on two in-house SS-OCT datasets. In addition to the multiple metrics that are used for evaluating the segmentation performance, including Jaccard similarity coefficient, dice similarity coefficient and boundary distance error, we also propose length similarity coefficient and angle similarity coefficient to evaluate the length estimation and angle estimation, respectively. The experimental results show that our method can effectively improve the segmentation performance, and our DCANet achieves the overall best performance on two datasets compared with other state-of-the-art networks. Our findings motivate the development of clinically applicable deep learning systems for the prediction of high myopia progression on the basis of the scleral phenotypes from SS-OCT images. [ABSTRACT FROM AUTHOR]