AI Medical Compendium Topic:
Tomography, Optical Coherence

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Estimating Retinal Sensitivity Using Optical Coherence Tomography With Deep-Learning Algorithms in Macular Telangiectasia Type 2.

JAMA network open
IMPORTANCE: As currently used, microperimetry is a burdensome clinical testing modality for testing retinal sensitivity requiring long testing times and trained technicians.

f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks.

Medical image analysis
Obtaining expert labels in clinical imaging is difficult since exhaustive annotation is time-consuming. Furthermore, not all possibly relevant markers may be known and sufficiently well described a priori to even guide annotation. While supervised le...

A Deep Learning Algorithm to Quantify Neuroretinal Rim Loss From Optic Disc Photographs.

American journal of ophthalmology
PURPOSE: To train a deep learning (DL) algorithm that quantifies glaucomatous neuroretinal damage on fundus photographs using the minimum rim width relative to Bruch membrane opening (BMO-MRW) from spectral-domain optical coherence tomography (SDOCT)...

Rheumatoid Arthritis: Atherosclerosis Imaging and Cardiovascular Risk Assessment Using Machine and Deep Learning-Based Tissue Characterization.

Current atherosclerosis reports
PURPOSE OF THE REVIEW: Rheumatoid arthritis (RA) is a chronic, autoimmune disease which may result in a higher risk of cardiovascular (CV) events and stroke. Tissue characterization and risk stratification of patients with rheumatoid arthritis are a ...

Fully automated detection of retinal disorders by image-based deep learning.

Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
PURPOSE: With the aging population and the global diabetes epidemic, the prevalence of age-related macular degeneration (AMD) and diabetic macular edema (DME) diseases which are the leading causes of blindness is further increasing. Intravitreal inje...

Artificial intelligence-based decision-making for age-related macular degeneration.

Theranostics
Artificial intelligence (AI) based on convolutional neural networks (CNNs) has a great potential to enhance medical workflow and improve health care quality. Of particular interest is practical implementation of such AI-based software as a cloud-base...

Deep structure tensor graph search framework for automated extraction and characterization of retinal layers and fluid pathology in retinal SD-OCT scans.

Computers in biology and medicine
Maculopathy is a group of retinal disorders that affect macula and cause severe visual impairment if not treated in time. Many computer-aided diagnostic methods have been proposed over the past that automatically detect macular diseases. However, to ...

Automated geographic atrophy segmentation for SD-OCT images based on two-stage learning model.

Computers in biology and medicine
Automatic and reliable segmentation for geographic atrophy in spectral-domain optical coherence tomography (SD-OCT) images is a challenging task. To develop an effective segmentation method, a two-stage deep learning framework based on an auto-encode...

Automated assessment of breast cancer margin in optical coherence tomography images via pretrained convolutional neural network.

Journal of biophotonics
The benchmark method for the evaluation of breast cancers involves microscopic testing of a hematoxylin and eosin (H&E)-stained tissue biopsy. Resurgery is required in 20% to 30% of cases because of incomplete excision of malignant tissues. Therefore...