A Novel Method for the Measurement of Retinal Arteriolar Bifurcation.

INTRODUCTION: The purpose of this research was to develop protocols for evaluating the bifurcation parameters of retinal arteriole and establish a reference range of normal values. METHODS: In this retrospective study, we measured a total of 1314 retinal arteriolar bifurcations from 100 fundus photographs. We selected 200 from these bifurcations for testing inter-measurer and inter-method agreement. Additionally, we calculated the normal reference range for retinal arteriolar bifurcation parameters and analyzed the effects of gender, age, and anatomical features on retinal arteriolar bifurcation. RESULTS: The measurement method proposed in this study has demonstrated nearly perfect consistency among different measurers, with interclass correlation coefficient (ICC) for all bifurcation parameters of retinal arteriole exceeding 0.95. Among healthy individuals, the retinal arteriolar caliber was narrowest in young adults and increased in children, teenagers, and the elderly; retinal arteriolar caliber was greater in females than in males; and the diameter of the inferior temporal branch exceeded that of the superior temporal branch. The angle between the two branches of retinal arteriolar bifurcation was also greater in females than in males. When using the center of the optic disc as a reference point, the angle between the two branches of the retinal arteriole at the proximal or distal ends increased. In contrast, the estimated optimum theoretical values of retinal arteriolar bifurcation were not affected by these factors. CONCLUSIONS: The method for the measurement of retinal arteriolar bifurcation in this study was highly accurate and reproducible. The diameter and branching angle of the retinal arteriolar bifurcation were more susceptible to the influence of gender, age, and anatomical features. In comparison, the estimated optimum theoretical values of retinal arteriolar bifurcation were relatively stable. Video available for this article.

ACCV: automatic classification algorithm of cataract video based on deep learning.

PURPOSE: A real-time automatic cataract-grading algorithm based on cataract video is proposed. MATERIALS AND METHODS: In this retrospective study, we set the video of the eye lens section as the research target. A method is proposed to use YOLOv3 to assist in positioning, to automatically identify the position of the lens and classify the cataract after color space conversion. The data set is a cataract video file of 38 people's 76 eyes collected by a slit lamp. Data were collected using five random manner, the method aims to reduce the influence on the collection algorithm accuracy. The video length is within 10 s, and the classified picture data are extracted from the video file. A total of 1520 images are extracted from the image data set, and the data set is divided into training set, validation set and test set according to the ratio of 7:2:1. RESULTS: We verified it on the 76-segment clinical data test set and achieved the accuracy of 0.9400, with the AUC of 0.9880, and the F1 of 0.9388. In addition, because of the color space recognition method, the detection per frame can be completed within 29 microseconds and thus the detection efficiency has been improved significantly. CONCLUSION: With the efficiency and effectiveness of this algorithm, the lens scan video is used as the research object, which improves the accuracy of the screening. It is closer to the actual cataract diagnosis and treatment process, and can effectively improve the cataract inspection ability of non-ophthalmologists. For cataract screening in poor areas, the accessibility of ophthalmology medical care is also increased.

Portable Handheld Slit-Lamp Based on a Smartphone Camera for Cataract Screening.

PURPOSE: As part of plans to provide help to people in remote and poor areas who have no medical resources, a portable slit-lamp based on a smartphone was proposed. This would help in early screening of cataract diseases. METHODS: This means a microlens is designed that would work with a phone's camera. The phone's photo taking function is used in capturing the image of the eyes lens to replace the observation system of the desktop slit-lamp. A simplified slit light band was designed. In order for the light source part to meet the portable requirements of the slit-lamp, the adjustable and diffused light functions of the ligaments were removed in this design. Furthermore, the images collected by the smartphone are uploaded to the deep learning cataract screening system, which can achieve real-time and effective screening of cataract. RESULTS: Unlike the desktop slit-lamp, which needs skilled personnel to operate, this device can be easily operated by less-skilled or inexperienced doctors. This eliminates the concerns of inaccurate diagnosis based on the use of unskilled professionals. Due to the portability, ease of use, and simplicity in obtaining crystal images of this device, it serves as a promising platform for nonhospital screening and telemedicine. CONCLUSIONS: In this paper, we invented a small portable device for screening cataract. This device is to make screening and diagnosis of cataract in remote areas very fast and effective. It will also solve the problem of inadequate specialized doctors and equipment in those areas as well. Translational Relevance. Smartphones can be used with portable slit-lamps to capture the images of the lens.