Predicting Clinician Fixations on Glaucoma OCT Reports via CNN-Based Saliency Prediction Methods.

To predict physician fixations specifically on ophthalmology optical coherence tomography (OCT) reports from eye tracking data using CNN based saliency prediction methods in order to aid in the education of ophthalmologists and ophthalmologists-in-training. Fifteen ophthalmologists were recruited to each examine 20 randomly selected OCT reports and evaluate the likelihood of glaucoma for each report on a scale of 0-100. Eye movements were collected using a Pupil Labs Core eye-tracker.

Detecting Eye Disease Using Vision Transformers Informed by Ophthalmology Resident Gaze Data.

We showcase two proof-of-concept approaches for enhancing the Vision Transformer (ViT) model by integrating ophthalmology resident gaze data into its training. The resulting Fixation-Order-Informed ViT and Ophthalmologist-Gaze-Augmented ViT show greater accuracy and computational efficiency than ViT for detection of the eye disease, glaucoma.Clinical relevance- By enhancing glaucoma detection via our gaze-informed ViTs, we introduce a new paradigm for medical experts to directly interface with medical AI, leading the way for more accurate and interpretable AI 'teammates' in the ophthalmic clinic.

Extracting decision-making features from the unstructured eye movements of clinicians on glaucoma OCT reports and developing AI models to classify expertise.

This study aimed to investigate the eye movement patterns of ophthalmologists with varying expertise levels during the assessment of optical coherence tomography (OCT) reports for glaucoma detection. Objectives included evaluating eye gaze metrics and patterns as a function of ophthalmic education, deriving novel features from eye-tracking, and developing binary classification models for disease detection and expertise differentiation. Thirteen ophthalmology residents, fellows, and clinicians specializing in glaucoma participated in the study.

Detecting glaucoma with only OCT: Implications for the clinic, research, screening, and AI development.

A method for detecting glaucoma based only on optical coherence tomography (OCT) is of potential value for routine clinical decisions, for inclusion criteria for research studies and trials, for large-scale clinical screening, as well as for the development of artificial intelligence (AI) decision models. Recent work suggests that the OCT probability (p-) maps, also known as deviation maps, can play a key role in an OCT-based method. However, artifacts seen on the p-maps of healthy control eyes can resemble patterns of damage due to glaucoma.

A multimodal deep learning system to distinguish late stages of AMD and to compare expert vs. AI ocular biomarkers.

Within the next 1.5 decades, 1 in 7 U.S.

Strategies to Improve Convolutional Neural Network Generalizability and Reference Standards for Glaucoma Detection From OCT Scans.

Purpose: To develop and evaluate methods to improve the generalizability of convolutional neural networks (CNNs) trained to detect glaucoma from optical coherence tomography retinal nerve fiber layer probability maps, as well as optical coherence tomography circumpapillary disc (circle) b-scans, and to explore impact of reference standard (RS) on CNN accuracy.

Methods: CNNs previously optimized for glaucoma detection from retinal nerve fiber layer probability maps, and newly developed CNNs adapted for glaucoma detection from optical coherence tomography b-scans, were evaluated on an unseen dataset (i.e.

Robust and Interpretable Convolutional Neural Networks to Detect Glaucoma in Optical Coherence Tomography Images.

Recent studies suggest that deep learning systems can now achieve performance on par with medical experts in diagnosis of disease. A prime example is in the field of ophthalmology, where convolutional neural networks (CNNs) have been used to detect retinal and ocular diseases. However, this type of artificial intelligence (AI) has yet to be adopted clinically due to questions regarding robustness of the algorithms to datasets collected at new clinical sites and a lack of explainability of AI-based predictions, especially relative to those of human expert counterparts.

An Automated Method for Assessing Topographical Structure-Function Agreement in Abnormal Glaucomatous Regions.

Purpose: To develop an automated/objective method for topographically comparing abnormal regions on optical coherence tomography (OCT) and visual field (VF) tests of eyes with early glaucoma.

Methods: A custom R program was developed that allows for both visualization and automatic assessment of the topographical agreement between functional (24-2 and/or 10-2 VF) and structural (widefield OCT retinal nerve fiber layer and/or retinal ganglion cell layer) deviation/probability maps. It was optimized using information from 98 eyes: 53 diagnosed as "definitely glaucoma" (DG) and 45 recruited as healthy (H) controls.

Enhancing the Accuracy of Glaucoma Detection from OCT Probability Maps using Convolutional Neural Networks.

We describe and assess convolutional neural network (CNN) models for detection of glaucoma based upon optical coherence tomography (OCT) retinal nerve fiber layer (RNFL) probability maps. CNNs pretrained on natural images performed comparably to CNNs trained solely on OCT data, and all models showed high accuracy in detecting glaucoma, with receiver operating characteristic area under the curve (AUC) scores ranging from 0.930 to 0.