AIMC Topic: Fundus Oculi
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A multi-class fundus disease classification system based on an adaptive scale discriminator and hybrid loss.
Computational biology and chemistry
Oct 11, 2024
Fundus images are crucial in the observation and detection of ophthalmic diseases. However, detecting multiple ophthalmic diseases from fundus images using deep learning techniques is a complex and challenging task One challenge is the complexity of ...
Artificial intelligence methods in diagnosis of retinoblastoma based on fundus imaging: a systematic review and meta-analysis.
Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
Sep 18, 2024
BACKGROUND: Artificial intelligence (AI) algorithms for the detection of retinoblastoma (RB) by fundus image analysis have been proposed as a potentially effective technique to facilitate diagnosis and screening programs. However, doubts remain about...
Evaluation of AI-enhanced non-mydriatic fundus photography for diabetic retinopathy screening.
Photodiagnosis and photodynamic therapy
Sep 7, 2024
OBJECTIVE: To assess the feasibility of using non-mydriatic fundus photography in conjunction with an artificial intelligence (AI) reading platform for large-scale screening of diabetic retinopathy (DR).
Empowering Portable Age-Related Macular Degeneration Screening: Evaluation of a Deep Learning Algorithm for a Smartphone Fundus Camera.
BMJ open
Sep 5, 2024
OBJECTIVES: Despite global research on early detection of age-related macular degeneration (AMD), not enough is being done for large-scale screening. Automated analysis of retinal images captured via smartphone presents a potential solution; however,...
SCINet: A Segmentation and Classification Interaction CNN Method for Arteriosclerotic Retinopathy Grading.
Interdisciplinary sciences, computational life sciences
Sep 2, 2024
As a common disease, cardiovascular and cerebrovascular diseases pose a great harm threat to human wellness. Even using advanced and comprehensive treatment methods, there is still a high mortality rate. Arteriosclerosis, as an important factor refle...
Artificial intelligence-based extraction of quantitative ultra-widefield fluorescein angiography parameters in retinal vein occlusion.
Canadian journal of ophthalmology. Journal canadien d'ophtalmologie
Aug 31, 2024
OBJECTIVE: To examine the association between quantitative vascular parameters extracted from intravenous fluorescein angiography (IVFA) and baseline clinical characteristics of patients with retinal vein occlusion (RVO).
Development of oculomics artificial intelligence for cardiovascular risk factors: A case study in fundus oculomics for HbA1c assessment and clinically relevant considerations for clinicians.
Asia-Pacific journal of ophthalmology (Philadelphia, Pa.)
Aug 28, 2024
Artificial Intelligence (AI) is transforming healthcare, notably in ophthalmology, where its ability to interpret images and data can significantly enhance disease diagnosis and patient care. Recent developments in oculomics, the integration of ophth...
Metadata information and fundus image fusion neural network for hyperuricemia classification in diabetes.
Computer methods and programs in biomedicine
Aug 23, 2024
OBJECTIVE: In diabetes mellitus patients, hyperuricemia may lead to the development of diabetic complications, including macrovascular and microvascular dysfunction. However, the level of blood uric acid in diabetic patients is obtained by sampling p...
Automated identification of fleck lesions in Stargardt disease using deep learning enhances lesion detection sensitivity and enables morphometric analysis of flecks.
The British journal of ophthalmology
Aug 22, 2024
PURPOSE: To classify fleck lesions and assess artificial intelligence (AI) in identifying flecks in Stargardt disease (STGD).
Real-world evaluation of RetCAD deep-learning system for the detection of referable diabetic retinopathy and age-related macular degeneration.
Clinical & experimental optometry
Aug 12, 2024
CLINICAL RELEVANCE: The challenges of establishing retinal screening programs in rural settings may be mitigated by the emergence of deep-learning systems for early disease detection.
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