Diseases are currently managed by grading systems, where patients are stratified by grading systems into stages that indicate patient risk and guide clinical management. However, these broad categories typically lack prognostic value, and proposals for new biomarkers are currently limited to anecdotal observations. In this work, we introduce a deep-learning-based biomarker proposal system for the purpose of accelerating biomarker discovery in age-related macular degeneration (AMD). It works by first training a neural network using self-supervised contrastive learning to discover, without any clinical annotations, features relating to both known and unknown AMD biomarkers present in 46,496 retinal optical coherence tomography (OCT) images. To interpret the discovered biomarkers, we partition the images into 30 subsets, termed clusters, that contain similar features. We then conduct two parallel 1.5-hour semi-structured interviews with two independent teams of retinal specialists that describe each cluster in clinical language. Overall, both teams independently identified clearly distinct characteristics in 27 of 30 clusters, of which 23 were related to AMD. Seven were recognised as known biomarkers already used in established grading systems and 16 depicted biomarker combinations or subtypes that are either not yet used in grading systems, were only recently proposed, or were unknown. Clusters separated incomplete from complete retinal atrophy, intraretinal from subretinal fluid and thick from thin choroids, and in simulation outperformed clinically-used grading systems in prognostic value. Overall, contrastive learning enabled the automatic proposal of AMD biomarkers that go beyond the set used by clinically established grading systems. Ultimately, we envision that equipping clinicians with discovery-oriented deep-learning tools can accelerate discovery of novel prognostic biomarkers.

Purpose: To develop an open-source, fully-automatic deep learning algorithm, DeepGPET, for choroid region segmentation in optical coherence tomography (OCT) data. Methods: We used a dataset of 715 OCT B-scans (82 subjects, 115 eyes) from 3 clinical studies related to systemic disease. Ground truth segmentations were generated using a clinically validated, semi-automatic choroid segmentation method, Gaussian Process Edge Tracing (GPET). We finetuned a UNet with MobileNetV3 backbone pre-trained on ImageNet. Standard segmentation agreement metrics, as well as derived measures of choroidal thickness and area, were used to evaluate DeepGPET, alongside qualitative evaluation from a clinical ophthalmologist. 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. Both methods performed similarly according to a clinical ophthalmologist, who qualitatively judged a subset of segmentations by GPET and DeepGPET, based on smoothness and accuracy of segmentations. Conclusions: DeepGPET, a fully-automatic, open-source algorithm for choroidal segmentation, will enable researchers to efficiently extract choroidal measurements, even for large datasets. As no manual interventions are required, DeepGPET is less subjective than semi-automatic methods and could be deployed in clinical practice without necessitating a trained operator.

QUT researchers have applied artificial intelligence (AI) deep learning techniques to develop a more accurate and detailed method for analysing images of the back of the eye to help clinicians better detect and track eye diseases, such as glaucoma and aged-related macular degeneration. Their findings have been published in Nature Scientific Reports. Study lead author QUT Senior Research Fellow Dr David Alonso-Caneiro, from the Faculty of Health School of Optometry and Vision Science, said the team had explored a range of state-of-the-art deep learning techniques to analyse Optical Coherence Tomography (OCT) images. OCT is a common instrument used by optometrists and ophthalmologists. It takes cross-sectional images of the eye which show different tissue layers.

Using Artificial Intelligence, researchers are now able to identify the back of the eye images. Scientists have utilized Artificial intelligence (AI) to produce a far more accurate and in-depth method for analyzing images of the rear of this eye, a prior which may help ophthalmologists better identify and monitor eye diseases as glaucoma, and age-related macular degeneration. In the study, released in the Scientific journal report, the scientists looked for a new way of analyzing images from a state-of-the-art instrument known as the Optical Coherence Tomography (OCT). The scientists, together with those from the Queensland Faculty of Technology (QUT) found Australia, explored a range of machine learning strategies to analyze OCT pictures. The retina and the choroid are the two main tissue layers at the back of the eye and researchers tried extracting images from these two layers.

Researchers at the Queensland University of Technology (QUT) have applied artificial intelligence (AI) to develop a more accurate and detailed method for analysing images of the back of the eye to help clinicians better detect and track eye diseases. In the study, the group of researchers explored a range of deep learning techniques to analyse Optical Coherence Tomography (OCT) images, said David Alonso-Caneiro, QUT senior research fellow and study lead author. OCT, which takes cross-sectional images of the eye to show different tissue layers, is a common instrument used by optometrists and ophthalmologists. These images are around four microns in size and can help clinicians detect eye diseases such as glaucoma and age-related macular degeneration. The team collected OCT chorio-retinal eye scans from an 18-month longitudinal study of 101 children with good vision and healthy eyes, and used these images to train the AI program to detect patterns and define the choroid boundaries.

Researchers at the Queensland University of Technology (QUT) have applied artificial intelligence (AI) to develop a more accurate and detailed method for analysing images of the back of the eye to help clinicians better detect and track eye diseases. In the study, the group of researchers explored a range of deep learning techniques to analyse Optical Coherence Tomography (OCT) images, said David Alonso-Caneiro, QUT senior research fellow and study lead author. OCT, which takes cross-sectional images of the eye to show different tissue layers, is a common instrument used by optometrists and ophthalmologists. These images are around four microns in size and can help clinicians detect eye diseases such as glaucoma and age-related macular degeneration. The team collected OCT chorio-retinal eye scans from an 18-month longitudinal study of 101 children with good vision and healthy eyes, and used these images to train the AI program to detect patterns and define the choroid boundaries.