Development and research status of intelligent ophthalmology in China.

This paper analyzes the current status, technological developments, academic exchange platforms, and future challenges and solutions in the field of intelligent ophthalmology (IO) in China. In terms of technology, significant progress has been made in various areas, including diabetic retinopathy, fundus image analysis, quality assessment of medical artificial intelligence products, clinical research methods, technical evaluation, and industry standards. Researchers continually enhance the safety and standardization of IO technology by formulating a series of clinical application guidelines and standards.

Early detection of dementia through retinal imaging and trustworthy AI.

Alzheimer's disease (AD) is a global healthcare challenge lacking a simple and affordable detection method. We propose a novel deep learning framework, Eye-AD, to detect Early-onset Alzheimer's Disease (EOAD) and Mild Cognitive Impairment (MCI) using OCTA images of retinal microvasculature and choriocapillaris. Eye-AD employs a multilevel graph representation to analyze intra- and inter-instance relationships in retinal layers.

Fei-yan-qing-hua decoction exerts an anti-inflammatory role during influenza by inhibiting the infiltration of macrophages and neutrophils through NF-κB and p38 MAPK pathways.

Ethnopharmacological Relevance: Fei-Yan-Qing-Hua decoction (FYQHD) is an empirical formula that has shown clinical success in treating community-acquired pneumonia (CAP) for two decades. Influenza viral infection is a significant trigger for severe pneumonia, yet the role of FYQHD in treating influenza remains unclear.

Aim Of The Study: This study aimed to assess the potential efficacy of FYQHD in treating influenza viral infection and to elucidate its underlying mechanisms.

Vessel-promoted OCT to OCTA image translation by heuristic contextual constraints.

Optical Coherence Tomography Angiography (OCTA) is a crucial tool in the clinical screening of retinal diseases, allowing for accurate 3D imaging of blood vessels through non-invasive scanning. However, the hardware-based approach for acquiring OCTA images presents challenges due to the need for specialized sensors and expensive devices. In this paper, we introduce a novel method called TransPro, which can translate the readily available 3D Optical Coherence Tomography (OCT) images into 3D OCTA images without requiring any additional hardware modifications.

Multi-rater Prism: Learning self-calibrated medical image segmentation from multiple raters.

In medical image segmentation, it is often necessary to collect opinions from multiple experts to make the final decision. This clinical routine helps to mitigate individual bias. However, when data is annotated by multiple experts, standard deep learning models are often not applicable.

Semi-Implicit Denoising Diffusion Models (SIDDMs).

Despite the proliferation of generative models, achieving fast sampling during inference without compromising sample diversity and quality remains challenging. Existing models such as Denoising Diffusion Probabilistic Models (DDPM) deliver high-quality, diverse samples but are slowed by an inherently high number of iterative steps. The Denoising Diffusion Generative Adversarial Networks (DDGAN) attempted to circumvent this limitation by integrating a GAN model for larger jumps in the diffusion process.

Rujin Jiedu decoction protects against influenza virus infection by modulating gut microbiota.

Background: Rujin Jiedu decoction (RJJDD) is a classical prescription of Traditional Chinese Medicine that has long been applied to treat pneumonia caused by external infection, but whether and how it benefits influenza virus therapy remains largely unclear. The aim of this study was to investigate the anti-inflammatory effect of RJJDD on the mouse model of influenza and to explore its potential mechanism.

Methods: The mice were mock-infected with PBS or infected with PR8 virus followed by treatment with RJJDD or antiviral oseltamivir.

MedSyn: Text-Guided Anatomy-Aware Synthesis of High-Fidelity 3-D CT Images.

This paper introduces an innovative methodology for producing high-quality 3D lung CT images guided by textual information. While diffusion-based generative models are increasingly used in medical imaging, current state-of-the-art approaches are limited to low-resolution outputs and underutilize radiology reports' abundant information. The radiology reports can enhance the generation process by providing additional guidance and offering fine-grained control over the synthesis of images.

Towards Lightweight Super-Resolution With Dual Regression Learning.

Deep neural networks have exhibited remarkable performance in image super-resolution (SR) tasks by learning a mapping from low-resolution (LR) images to high-resolution (HR) images. However, the SR problem is typically an ill-posed problem and existing methods would come with several limitations. First, the possible mapping space of SR can be extremely large since there may exist many different HR images that can be super-resolved from the same LR image.

Multi-risk factors joint prediction model for risk prediction of retinopathy of prematurity.

Purpose: Retinopathy of prematurity (ROP) is a retinal vascular proliferative disease common in low birth weight and premature infants and is one of the main causes of blindness in children.In the context of predictive, preventive and personalized medicine (PPPM/3PM), early screening, identification and treatment of ROP will directly contribute to improve patients' long-term visual prognosis and reduce the risk of blindness. Thus, our objective is to establish an artificial intelligence (AI) algorithm combined with clinical demographics to create a risk model for ROP including treatment-requiring retinopathy of prematurity (TR-ROP) infants.

Bibliometric analysis of hotspots and trends of global myopia research.

Aim: To gain insights into the global research hotspots and trends of myopia.

Methods: Articles were downloaded from January 1, 2013 to December 31, 2022 from the Science Core Database website and were mainly statistically analyzed by bibliometrics software.

Results: A total of 444 institutions in 87 countries published 4124 articles.

Platycodin D facilitates antiviral immunity through inhibiting cytokine storm via targeting K63-linked TRAF6 ubiquitination.

Influenza virus infection is a worldwide challenge that causes heavy burdens on public health. The mortality rate of severe influenza patients is often associated with hyperactive immunological abnormalities characterized by hypercytokinemia. Due to the continuous mutations and the occurrence of drug-resistant influenza virus strains, the development of host-directed immunoregulatory drugs is urgently required.

Open Fundus Photograph Dataset with Pathologic Myopia Recognition and Anatomical Structure Annotation.

Pathologic myopia (PM) is a common blinding retinal degeneration suffered by highly myopic population. Early screening of this condition can reduce the damage caused by the associated fundus lesions and therefore prevent vision loss. Automated diagnostic tools based on artificial intelligence methods can benefit this process by aiding clinicians to identify disease signs or to screen mass populations using color fundus photographs as inputs.

Predicting Implantable Collamer Lens Vault Using Machine Learning Based on Various Preoperative Biometric Factors.

Purpose: To predict the vault size after Implantable Collamer Lens (ICL) V4c implantation using machine learning methods and to compare the predicted vault with the conventional manufacturer's nomogram.

Methods: This study included 707 patients (707 eyes) who underwent ICL V4c implantation at the Department of Ophthalmology, Peking Union Medical College Hospital, from September 2019 to January 2022. Random Forest Regression (RFR), XGBoost, and linear regression (LR) were used to predict the vault size 1 week after ICL V4c implantation.

Corrigendum: Compound Dihuang Granule inhibits nigrostriatal pathway apoptosis in Parkinson's disease by suppressing the JNK/AP-1 pathway.

[This corrects the article DOI: 10.3389/fphar.2021.

Fei-Yan-Qing-Hua decoction decreases hyperinflammation by inhibiting HMGB1/RAGE signaling and promotes bacterial phagocytosis in the treatment of sepsis.

Ethnopharmacological Relevance: Fei-Yan-Qing-Hua decoction (FYQHD), derived from the renowned formula Ma Xing Shi Gan tang documented in Zhang Zhong Jing's "Treatise on Exogenous Febrile Disease" during the Han Dynasty, has demonstrated notable efficacy in the clinical treatment of pneumonia resulting from bacterial infection. However, its molecular mechanisms underlying the therapeutic effects remains elusive.

Aim Of The Study: This study aimed to investigate the protective effects of FYQHD against lipopolysaccharide (LPS) and carbapenem-resistant Klebsiella pneumoniae (CRKP)-induced sepsis in mice and to elucidate its specific mechanism of action.

GAMMA challenge: Glaucoma grAding from Multi-Modality imAges.

Glaucoma is a chronic neuro-degenerative condition that is one of the world's leading causes of irreversible but preventable blindness. The blindness is generally caused by the lack of timely detection and treatment. Early screening is thus essential for early treatment to preserve vision and maintain life quality.

Xuanbai Chengqi Decoction alleviates acute lung injury by inhibiting NLRP3 inflammasome.

Ethnopharmacological Relevance: Acute lung injury/acute respiratory distress syndrome (ALI/ARDS) is a prevalent critical respiratory disorder caused mostly by infection and other factors. However, effective drug therapies are currently lacking. Xuanbai Chengqi Decoction (XCD), a traditional Chinese medicine (TCM) prescription, is commonly employed to treat lung diseases.

Guidelines on clinical research evaluation of artificial intelligence in ophthalmology (2023).

With the upsurge of artificial intelligence (AI) technology in the medical field, its application in ophthalmology has become a cutting-edge research field. Notably, machine learning techniques have shown remarkable achievements in diagnosing, intervening, and predicting ophthalmic diseases. To meet the requirements of clinical research and fit the actual progress of clinical diagnosis and treatment of ophthalmic AI, the Ophthalmic Imaging and Intelligent Medicine Branch and the Intelligent Medicine Committee of Chinese Medicine Education Association organized experts to integrate recent evaluation reports of clinical AI research at home and abroad and formed a guideline on clinical research evaluation of AI in ophthalmology after several rounds of discussion and modification.

A generic fundus image enhancement network boosted by frequency self-supervised representation learning.

Fundus photography is prone to suffer from image quality degradation that impacts clinical examination performed by ophthalmologists or intelligent systems. Though enhancement algorithms have been developed to promote fundus observation on degraded images, high data demands and limited applicability hinder their clinical deployment. To circumvent this bottleneck, a generic fundus image enhancement network (GFE-Net) is developed in this study to robustly correct unknown fundus images without supervised or extra data.

Crystalline lens nuclear age prediction as a new biomarker of nucleus degeneration.

Background: The crystalline lens is a transparent structure of the eye to focus light on the retina. It becomes muddy, hard and dense with increasing age, which makes the crystalline lens gradually lose its function. We aim to develop a nuclear age predictor to reflect the degeneration of the crystalline lens nucleus.

Fundus Image-Label Pairs Synthesis and Retinopathy Screening via GANs With Class-Imbalanced Semi-Supervised Learning.

Retinopathy is the primary cause of irreversible yet preventable blindness. Numerous deep-learning algorithms have been developed for automatic retinal fundus image analysis. However, existing methods are usually data-driven, which rarely consider the costs associated with fundus image collection and annotation, along with the class-imbalanced distribution that arises from the relative scarcity of disease-positive individuals in the population.