Retinopathy of prematurity (ROP) is a major cause of vision loss and blindness among premature infants. Timely screening, diagnosis, and intervention can effectively prevent the deterioration of ROP. However, there are several challenges in ROP diagnosis globally, including high subjectivity, low screening efficiency, regional disparities in screening coverage, and severe shortage of pediatric ophthalmologists. The application of artificial intelligence (AI) as an assistive tool for diagnosis or an automated method for ROP diagnosis can improve the efficiency and objectivity of ROP diagnosis, expand screening coverage, and enable automated screening and quantified diagnostic results. In the global environment that emphasizes the development and application of medical imaging AI, developing more accurate diagnostic networks, exploring more effective AI-assisted diagnosis methods, and enhancing the interpretability of AI-assisted diagnosis, can accelerate the improvement of AI policies of ROP and the implementation of AI products, promoting the development of ROP diagnosis and treatment.

Objective:To study a deep learning-based dual-modality fundus camera which was used to study retinal blood oxygen saturation and vascular morphology changes in eyes with branch retinal vein occlusion (BRVO).Methods:A prospective study. From May to October 2020, 31 patients (31 eyes) of BRVO (BRVO group) and 20 healthy volunteers (20 eyes) with matched gender and age (control group) were included in the study. Among 31 patients (31 eyes) in BRVO group, 20 patients (20 eyes) received one intravitreal injection of anti-vascular endothelial growth factor drugs before, and 11 patients (11 eyes) did not receive any treatment. They were divided into treatment group and untreated group accordingly. Retinal images were collected with a dual-modality fundus camera; arterial and vein segments were segmented in the macular region of interest (MROI) using deep learning; the optical density ratio was used to calculate retinal blood oxygen saturation (SO 2) on the affected and non-involved sides of the eyes in the control group and patients in the BRVO group, and calculated the diameter, curvature, fractal dimension and density of arteriovenous in MROI. Quantitative data were compared between groups using one-way analysis of variance. Results:There was a statistically significant difference in arterial SO 2 (SO 2 -A) in the MROI between the affected eyes, the fellow eyes in the BRVO group and the control group ( F=4.925, P<0.001), but there was no difference in the venous SO 2 (SO 2-V) ( F=0.607, P=0.178). Compared with the control group, the SO 2-A in the MROI of the affected side and the non-involved side of the untreated group was increased, and the difference was statistically significant ( F=4.925, P=0.012); there was no significant difference in SO 2-V ( F=0.607, P=0.550). There was no significant difference in SO 2-A and SO 2-V in the MROI between the affected side, the non-involved side in the treatment group and the control group ( F=0.159, 1.701; P=0.854, 0.197). There was no significant difference in SO 2-A and SO 2-V in MROI between the affected side of the treatment group, the untreated group and the control group ( F=2.553, 0.265; P=0.088, 0.546). The ophthalmic artery diameter, arterial curvature, arterial fractal dimension, vein fractal dimension, arterial density, and vein density were compared in the untreated group, the treatment group, and the control group, and the differences were statistically significant ( F=3.527, 3.322, 7.251, 26.128, 4.782, 5.612; P=0.047, 0.044, 0.002, <0.001, 0.013, 0.006); there was no significant difference in vein diameter and vein curvature ( F=2.132, 1.199; P=0.143, 0.321). Conclusion:Arterial SO 2 in BRVO patients is higher than that in healthy eyes, it decreases after anti-anti-vascular endothelial growth factor drugs treatment, SO 2-V is unchanged.