Eyecare-cloud: an innovative electronic medical record cloud platform for pediatric research and clinical care.

Background And Objectives: Clinical data are essential for developing cloud platforms for intelligent diagnosis and treatment decision of diseases. However, cloud platforms for data sharing and exchange with clinicians are poorly suited. We aim to establish Eyecare-cloud, a platform which provide a novel method for clinical data and medical image sharing, to provide a convenient tool for clinicians.

A fundus image dataset for intelligent retinopathy of prematurity system.

Image-based artificial intelligence (AI) systems stand as the major modality for evaluating ophthalmic conditions. However, most of the currently available AI systems are designed for experimental research using single-central datasets. Most of them fell short of application in real-world clinical settings.

Automated detection of nine infantile fundus diseases and conditions in retinal images using a deep learning system.

Purpose: We developed an Infant Retinal Intelligent Diagnosis System (IRIDS), an automated system to aid early diagnosis and monitoring of infantile fundus diseases and health conditions to satisfy urgent needs of ophthalmologists.

Methods: We developed IRIDS by combining convolutional neural networks and transformer structures, using a dataset of 7697 retinal images (1089 infants) from four hospitals. It identifies nine fundus diseases and conditions, namely, retinopathy of prematurity (ROP) (mild ROP, moderate ROP, and severe ROP), retinoblastoma (RB), retinitis pigmentosa (RP), Coats disease, coloboma of the choroid, congenital retinal fold (CRF), and normal.

Multiplatform tear proteomic profiling reveals novel non-invasive biomarkers for diabetic retinopathy.

Objectives: To investigate a comprehensive proteomic profile of the tear fluid in patients with diabetic retinopathy (DR) and further define non-invasive biomarkers.

Methods: A cross-sectional, multicentre study that includes 46 patients with DR, 28 patients with diabetes mellitus (DM), and 30 healthy controls (HC). Tear samples were collected with Schirmer strips.

Refractive Status and Biometric Characteristics of Children With Familial Exudative Vitreoretinopathy.

Purpose: To compare biometric characteristics between patients with early-stage familial exudative vitreoretinopathy (FEVR) and healthy controls.

Methods: This case-control study included 50 FEVR eyes in stage 1-2 and 50 control eyes matched by age, gender and spherical equivalent (SE). Biometric parameters including axial length (AL), white-to-white diameter (WTW), central corneal thickness (CCT), anterior chamber depth (ACD), lens thickness (LT), pupil diameter, vitreous chamber depth, anterior and posterior corneal surface curvature radius (ACR and PCR), anterior lens surface curvature radius (ALR) and posterior lens surface curvature radius were measured using IOLMaster 700 and compared between cases and controls using paired t-test.