Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
40039324
Diagnosing choroidal nevus in color fundus images is challenging for clinicians not regularly practicing it. Machine learning (ML) has proven effective in detecting and analyzing such abnormalities with high accuracy and efficiencyThis research is pa...
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
40039310
This paper proposes an automatic method to identify important positions and their color features in retinal fundus images for gender classification using deep learning. The proposed method consists of MALCC (Model Analysis by Local Color Characterist...
Diabetic retinopathy (DR), the leading cause of vision loss among diabetic adults worldwide, underscores the importance of early detection and timely treatment using fundus images to prevent vision loss. However, existing deep learning methods strugg...
PURPOSE: To compare the prediction accuracy of brachial-ankle pulse wave velocity (baPWV) from color fundus photographs (CFPs) using different deep learning models.
Glaucoma, an optic nerve disease resulting in blindness if left untreated, is a difficult condition in healthcare in view of its diagnostic difficulties. Past approaches are based on assessment of the fundus images and the size of the cup and the dis...
OBJECTIVE: To resolve the underestimation problem and investigate the mechanism of the AI model which employed to predict cardiovascular disease (CVD) risk scores from retinal fundus photos.
PURPOSE: Loss of retinal perfusion is associated with both onset and worsening of diabetic retinopathy (DR). Optical coherence tomography angiography is a noninvasive method for measuring the nonperfusion area (NPA) and has promise as a scalable scre...
BMC medical informatics and decision making
40055729
BACKGROUND: Retinal vein occlusion (RVO) is a leading cause of vision loss globally. Routine health check-up data-including demographic information, medical history, and laboratory test results-are commonly utilized in clinical settings for disease r...
Automated segmentation of retinal blood vessels in fundus images plays a key role in providing ophthalmologists with critical insights for the non-invasive diagnosis of common eye diseases. Early and precise detection of these conditions is essential...
Early detection of Diabetic Retinopathy (DR) is vital for preserving vision and preventing deterioration of eyesight. Fundus Fluorescein Angiography (FFA), recognized as the gold standard for diagnosing DR, effectively reveals abnormalities in retina...