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Found 4 papers, 4 papers with code
Choroidalyzer: An open-source, end-to-end pipeline for choroidal analysis in optical coherence tomography
We analysed segmentation agreement (AUC, Dice) and choroid metrics agreement (Pearson, Spearman, mean absolute error (MAE)) in internal and external test sets.
An open-source deep learning algorithm for efficient and fully-automatic analysis of the choroid in optical coherence tomography
Results: DeepGPET achieves excellent agreement with GPET on data from 3 clinical studies (AUC=0. 9994, Dice=0. 9664; Pearson correlation of 0. 8908 for choroidal thickness and 0. 9082 for choroidal area), while reducing the mean processing time per image on a standard laptop CPU from 34. 49s ($\pm$15. 09) using GPET to 1. 25s ($\pm$0. 10) using DeepGPET.
Evaluation of an automated choroid segmentation algorithm in a longitudinal kidney donor and recipient cohort
Significant associations were mostly stronger with automated CT (eGFR P<0. 001, creatinine P=0. 004, urea P=0. 04) compared to manual CT (eGFR P=0. 002, creatinine P=0. 01, urea P=0. 03).
Detection of multiple retinal diseases in ultra-widefield fundus images using deep learning: data-driven identification of relevant regions
Previous studies showed that deep learning (DL) models are effective for detecting retinal disease in UWF images, but primarily considered individual diseases under less-than-realistic conditions (excluding images with other diseases, artefacts, comorbidities, or borderline cases; and balancing healthy and diseased images) and did not systematically investigate which regions of the UWF images are relevant for disease detection.
