2024年1月8日 · We demonstrate a DCEN method for a cataract clinical assessment model based on fundus images and deep learning algorithms, which can simultaneously provide 97.62% accuracy of cataract type classification and 97.03% accuracy of cataract severity classification.

2023年10月31日 · ffERG and mfERG can be used to detect fundus disease in mature cataract patients. The preoperative visual electrophysiological examination has high clinical value in predicting postoperative vision of mature cataract patients with fundus disease. Different types of cataracts have different effects on electrophysiological examination results.

Two hundred cataract patients (200 eyes) with preoperative measurements by wide-field fundus imaging and macular SD-OCT were enrolled. Cataract severity was graded from 1 to 4 according to the degree of macular obscuring by cataract artifact in fundus photo images.

The Fundus Cataract-AI project aims to automate cataract diagnosis and grading using deep learning techniques applied to a single standard macula-centric fundus photo.

2021年9月15日 · In this paper, a novel deep neural network, namely CataractNet, is proposed for automatic cataract detection in fundus images. The loss and activation functions are tuned to train the network with small kernels, fewer training parameters, and layers.

In this study, we developed a visual function-based DLS for populations based on fundus images, especially for the screening of visually impaired cataracts. In addition, we used images taken by different types of fundus cameras from three institutions to evaluate the effectiveness and generalizability of the system.

2024年11月19日 · The cloudy lens of cataract patients forms a hazy degeneration in the fundus images, making it challenging to observe the patient's fundus vessels, which brings difficulties to the diagnosis process. To address this issue, this paper establishes a new cataract image restoration method named Catintell.

2022年6月9日 · A cataract simulation model is firstly designed to collect synthesized cataract sets (SCS) formed by cataract fundus images sharing identical structures. Then high-frequency components (HFCs) are extracted from the SCS to constrain structure consistency such that the structure preservation in SCR-Net is enforced.

2020年11月8日 · This paper aims to provide a novel hybrid convolutional and recurrent neural network (CRNN) for fundus image-based cataract classification. The proposed CRNN fuses the advantages of convolution neural network and recurrent neural network to preserve long- and short-term spatial correlation between the patches.

Use of a confocal aperture significantly improved vessel contrast but may reduce overall image intensity. CONCLUSIONS--Scanning laser ophthalmoscopy may offer a method to observe and record fine fundus detail in patients who have marked cataract.