AIMC Topic: Tomography, Optical Coherence
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Macular patterns of neuronal and visual field loss in recovered optic neuritis identified by machine learning.
Scientific reports
Dec 28, 2024
We used machine learning to investigate the residual visual field (VF) deficits and macula retinal ganglion cell (RGC) thickness loss patterns in recovered optic neuritis (ON). We applied archetypal analysis (AA) to 377 same-day pairings of 10-2 VF a...
CircWaveDL: Modeling of optical coherence tomography images based on a new supervised tensor-based dictionary learning for classification of macular abnormalities.
Artificial intelligence in medicine
Dec 24, 2024
Modeling Optical Coherence Tomography (OCT) images is crucial for numerous image processing applications and aids ophthalmologists in the early detection of macular abnormalities. Sparse representation-based models, particularly dictionary learning (...
Predicting macular hole surgery outcomes: Integrating preoperative OCT features with supervised machine learning statistical models.
Indian journal of ophthalmology
Dec 24, 2024
PURPOSE: To evaluate various supervised machine learning (ML) statistical models to predict anatomical outcomes after macular hole (MH) surgery using preoperative optical coherence tomography (OCT) features.
Automated feature selection for early keratoconus screening optimization.
Biomedical physics & engineering express
Dec 20, 2024
In this paper, an automated feature selection (FS) method is presented to optimize machine learning (ML) models' performances, enhancing early keratoconus screening. A total of 448 parameters were analyzed from a dataset comprising 3162 observations ...
Enhancing AI reliability: A foundation model with uncertainty estimation for optical coherence tomography-based retinal disease diagnosis.
Cell reports. Medicine
Dec 19, 2024
Inability to express the confidence level and detect unseen disease classes limits the clinical implementation of artificial intelligence in the real world. We develop a foundation model with uncertainty estimation (FMUE) to detect 16 retinal conditi...
Line-field confocal optical coherence tomography coupled with artificial intelligence algorithms as tool to investigate wound healing: A prospective, randomized, single-blinded pilot study.
Journal of the European Academy of Dermatology and Venereology : JEADV
Dec 17, 2024
BACKGROUND: Ablative fractional photothermolysis serves as an excellent in vivo model for studying wound healing. The advent of non-invasive imaging devices, such as line-field confocal optical coherence tomography (LC-OCT), enhances this model by en...
Estimation of foveal avascular zone area from a B-scan OCT image using machine learning algorithms.
PloS one
Dec 16, 2024
PURPOSE: The objective of this study is to estimate the area of the Foveal Avascular Zone (FAZ) from B-scan OCT images using machine learning algorithms.
CMFNet: a cross-dimensional modal fusion network for accurate vessel segmentation based on OCTA data.
Medical & biological engineering & computing
Dec 13, 2024
Optical coherence tomography angiography (OCTA) is a novel non-invasive retinal vessel imaging technique that can display high-resolution 3D vessel structures. The quantitative analysis of retinal vessel morphology plays an important role in the auto...
Retinal fluid quantification using a novel deep learning algorithm in patients treated with faricimab in the TRUCKEE study.
Eye (London, England)
Dec 11, 2024
BACKGROUND: Investigate retinal fluid changes via a novel deep-learning algorithm in real-world patients receiving faricimab for the treatment of neovascular age-related macular degeneration (nAMD).
Evaluating deep learning models for classifying OCT images with limited data and noisy labels.
Scientific reports
Dec 5, 2024
The use of deep learning for OCT image classification could enhance the diagnosis and monitoring of retinal diseases. However, challenges like variability in retinal abnormalities, noise, and artifacts in OCT images limit its clinical use. Our study ...
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