Interpretable Diabetic Retinopathy Diagnosis based on Biomarker Activation Map

Deep learning classifiers provide the most accurate means of automatically diagnosing diabetic retinopathy (DR) based on optical coherence tomography (OCT) and its angiography (OCTA). The power of these models is attributable in part to the inclusion of hidden layers that provide the complexity required to achieve a desired task. However, hidden layers also render algorithm outputs difficult to interpret. Here we introduce a novel biomarker activation map (BAM) framework based on generative adversarial learning that allows clinicians to verify and understand classifiers decision-making. A data set including 456 macular scans were graded as non-referable or referable DR based on current clinical standards. A DR classifier that was used to evaluate our BAM was first trained based on this data set. The BAM generation framework was designed by combing two U-shaped generators to provide meaningful interpretability to this classifier. The main generator was trained to take referable scans as input and produce an output that would be classified by the classifier as non-referable. The BAM is then constructed as the difference image between the output and input of the main generator. To ensure that the BAM only highlights classifier-utilized biomarkers an assistant generator was trained to do the opposite, producing scans that would be classified as referable by the classifier from non-referable scans. The generated BAMs highlighted known pathologic features including nonperfusion area and retinal fluid. A fully interpretable classifier based on these highlights could help clinicians better utilize and verify automated DR diagnosis.Comment: 12 pages, 8 figure

A Survey on Automated Diagnosis of Alzheimer's Disease Using Optical Coherence Tomography and Angiography

Retinal optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA) are promising tools for the (early) diagnosis of Alzheimer's disease (AD). These non-invasive imaging techniques are cost-effective and more accessible than alternative neuroimaging tools. However, interpreting and classifying multi-slice scans produced by OCT devices is time-consuming and challenging even for trained practitioners. There are surveys on machine learning and deep learning approaches concerning the automated analysis of OCT scans for various diseases such as glaucoma. However, the current literature lacks an extensive survey on the diagnosis of Alzheimer's disease or cognitive impairment using OCT or OCTA. This has motivated us to do a comprehensive survey aimed at machine/deep learning scientists or practitioners who require an introduction to the problem. The paper contains 1) an introduction to the medical background of Alzheimer's Disease and Cognitive Impairment and their diagnosis using OCT and OCTA imaging modalities, 2) a review of various technical proposals for the problem and the sub-problems from an automated analysis perspective, 3) a systematic review of the recent deep learning studies and available OCT/OCTA datasets directly aimed at the diagnosis of Alzheimer's Disease and Cognitive Impairment. For the latter, we used Publish or Perish Software to search for the relevant studies from various sources such as Scopus, PubMed, and Web of Science. We followed the PRISMA approach to screen an initial pool of 3073 references and determined ten relevant studies (N=10, out of 3073) that directly targeted AD diagnosis. We identified the lack of open OCT/OCTA datasets (about Alzheimer's disease) as the main issue that is impeding the progress in the field.Comment: Submitted to Computerized Medical Imaging and Graphics. Concept, methodology, invest, data curation, and writing org.draft by Yasemin Turkan. Concept, method, writing review editing, and supervision by F. Boray Te

Clinical Applications of Artificial Intelligence in Glaucoma

Ophthalmology is one of the major imaging-intensive fields of medicine and thus has potential for extensive applications of artificial intelligence (AI) to advance diagnosis, drug efficacy, and other treatment-related aspects of ocular disease. AI has made impressive progress in ophthalmology within the past few years and two autonomous AIenabled systems have received US regulatory approvals for autonomously screening for mid-level or advanced diabetic retinopathy and macular edema. While no autonomous AI-enabled system for glaucoma screening has yet received US regulatory approval, numerous assistive AI-enabled software tools are already employed in commercialized instruments for quantifying retinal images and visual fields to augment glaucoma research and clinical practice. In this literature review (non-systematic), we provide an overview of AI applications in glaucoma, and highlight some limitations and considerations for AI integration and adoption into clinical practice

The Role of Medical Image Modalities and AI in the Early Detection, Diagnosis and Grading of Retinal Diseases: A Survey.

Traditional dilated ophthalmoscopy can reveal diseases, such as age-related macular degeneration (AMD), diabetic retinopathy (DR), diabetic macular edema (DME), retinal tear, epiretinal membrane, macular hole, retinal detachment, retinitis pigmentosa, retinal vein occlusion (RVO), and retinal artery occlusion (RAO). Among these diseases, AMD and DR are the major causes of progressive vision loss, while the latter is recognized as a world-wide epidemic. Advances in retinal imaging have improved the diagnosis and management of DR and AMD. In this review article, we focus on the variable imaging modalities for accurate diagnosis, early detection, and staging of both AMD and DR. In addition, the role of artificial intelligence (AI) in providing automated detection, diagnosis, and staging of these diseases will be surveyed. Furthermore, current works are summarized and discussed. Finally, projected future trends are outlined. The work done on this survey indicates the effective role of AI in the early detection, diagnosis, and staging of DR and/or AMD. In the future, more AI solutions will be presented that hold promise for clinical applications

Deep Generative Modeling Based Retinal Image Analysis

In the recent past, deep learning algorithms have been widely used in retinal image analysis (fundus and OCT) to perform tasks like segmentation and classification. But to build robust and highly efficient deep learning models amount of the training images, the quality of the training images is extremely necessary. The quality of an image is also an extremely important factor for the clinical diagnosis of different diseases. The main aim of this thesis is to explore two relatively under-explored area of retinal image analysis, namely, the retinal image quality enhancement and artificial image synthesis. In this thesis, we proposed a series of deep generative modeling based algorithms to perform these above-mentioned tasks. From a mathematical perspective, the generative model is a statistical model of the joint probability distribution between an observable variable and a target variable. The generative adversarial network (GAN), variational auto-encoder(VAE) are some popular generative models. Generative models can be used to generate new samples from a given distribution. The OCT images have inherent speckle noise in it, fundus images do not suffer from noises in general, but the newly developed tele-ophthalmoscope devices produce images with relatively low spatial resolution and blur. Different GAN based algorithms were developed to generate corresponding high-quality images fro its low-quality counterpart. A combination of residual VAE and GAN was implemented to generate artificial retinal fundus images with their corresponding artificial blood vessel segmentation maps. This will not only help to generate new training images as many as needed but also will help to reduce the privacy issue of releasing personal medical data

The Role of Optical Coherence Tomography Angiography in Glaucoma

Glaucoma is the second leading cause of blindness worldwide, affecting eighty million people globally and three million patients in the USA. Primary open-angle glaucoma, the most common type, is a multifactorial progressive optic nerve neurodegenerative disorder that leads to loss of optic nerve head (ONH) tissue, thinning of the retinal nerve fiber layer, and corresponding visual field (VF) defects with or without elevated intraocular pressure (IOP). Risk factors include older age, black or Hispanic race, elevated IOP, thin central corneal thickness, disk hemorrhage, and low ocular perfusion pressure. The two prevalent theories explaining glaucomatous damage are mechanical (elevated IOP) and vascular (compromised optic nerve perfusion). Current diagnostic methods, such as measuring IOP, VF testing, and ONH evaluation, are subjective and often unreliable. Optical coherence tomography angiography (OCTA) is a rapid, non-invasive imaging modality that provides 3-D, volumetric details of both the structure and vascular networks of the retina and optic nerve. Various researchers have shown that OCTA provides an accurate and objective evaluation of the retina and the optic nerve in glaucoma. This chapter describes the role of OCTA in managing patients with glaucoma