AIMC Topic: Fluorescein Angiography
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Identification of diabetic retinopathy classification using machine learning algorithms on clinical data and optical coherence tomography angiography.
Eye (London, England)
Jun 13, 2024
BACKGROUND: To apply machine learning (ML) algorithms to perform multiclass diabetic retinopathy (DR) classification using both clinical data and optical coherence tomography angiography (OCTA).
Artificial intelligence in therapeutic management of hyperlipidemic ocular pathology.
Experimental eye research
Jun 3, 2024
Hyperlipidemia has many ocular manifestations, the most prevalent being retinal vascular occlusion. Hyperlipidemic lesions and occlusions to the vessels supplying the retina result in permanent blindness, necessitating prompt detection and treatment....
Intraoperative left-sided colorectal anastomotic testing in clinical practice: a multi-treatment machine-learning analysis of the iCral3 prospective cohort.
Updates in surgery
May 20, 2024
BACKGROUND: Current evidence about intraoperative anastomotic testing after left-sided colorectal resections is still controversial. The aim of this study was to analyze the impact of Indocyanine Green fluorescent angiography (ICG-FA) and air-leak te...
Deep learning segmentation of non-perfusion area from color fundus images and AI-generated fluorescein angiography.
Scientific reports
May 11, 2024
The non-perfusion area (NPA) of the retina is an important indicator in the visual prognosis of patients with branch retinal vein occlusion (BRVO). However, the current evaluation method of NPA, fluorescein angiography (FA), is invasive and burdensom...
Deep Learning for prediction of late recurrence of retinal detachment using preoperative and postoperative ultra-wide field imaging.
Acta ophthalmologica
Apr 29, 2024
PURPOSE: To elaborate a deep learning (DL) model for automatic prediction of late recurrence (LR) of rhegmatogenous retinal detachment (RRD) using pseudocolor and fundus autofluorescence (AF) ultra-wide field (UWF) images obtained preoperatively and ...
Multimodality Fusion Strategies in Eye Disease Diagnosis.
Journal of imaging informatics in medicine
Apr 19, 2024
Multimodality fusion has gained significance in medical applications, particularly in diagnosing challenging diseases like eye diseases, notably diabetic eye diseases that pose risks of vision loss and blindness. Mono-modality eye disease diagnosis p...
Predicting central choroidal thickness from colour fundus photographs using deep learning.
PloS one
Mar 29, 2024
The estimation of central choroidal thickness from colour fundus images can improve disease detection. We developed a deep learning method to estimate central choroidal thickness from colour fundus images at a single institution, using independent da...
Deep-learning automated quantification of longitudinal OCT scans demonstrates reduced RPE loss rate, preservation of intact macular area and predictive value of isolated photoreceptor degeneration in geographic atrophy patients receiving C3 inhibition treatment.
The British journal of ophthalmology
Mar 20, 2024
OBJECTIVE: To evaluate the role of automated optical coherence tomography (OCT) segmentation, using a validated deep-learning model, for assessing the effect of C3 inhibition on the area of geographic atrophy (GA); the constituent features of GA on O...
Using Deep Learning to Segment Retinal Vascular Leakage and Occlusion in Retinal Vasculitis.
Ocular immunology and inflammation
Jan 23, 2024
PURPOSE: Retinal vasculitis (RV) is characterised by retinal vascular leakage, occlusion or both on fluorescein angiography (FA). There is no standard scheme available to segment RV features. We aimed to develop a deep learning model to segment both ...
Uncovering Language Disparity of ChatGPT on Retinal Vascular Disease Classification: Cross-Sectional Study.
Journal of medical Internet research
Jan 22, 2024
BACKGROUND: Benefiting from rich knowledge and the exceptional ability to understand text, large language models like ChatGPT have shown great potential in English clinical environments. However, the performance of ChatGPT in non-English clinical set...
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