Automated Identification of Diabetic Retinopathy: A Survey

Diabetes strikes when the pancreas stops to produce sufficient insulin, gradually disturbing the retina of the human eye, leading to diabetic retinopathy. The blood vessels in the retina become changed and have abnormality. Exudates are concealed, micro-aneurysms and haemorrhages occur in the retina of eye, which intern leads to blindness. The presence of these structures signifies the harshness of the disease. A systematized Diabetic Retinopathy screening system will enable the detection of lesions accurately, consequently facilitating the ophthalmologists. Micro-aneurysms are the initial clinical signs of diabetic retinopathy. Timely identification of diabetic retinopathy plays a major role in the success of managing the disease. The main task is to extract exudates, which are similar in color property and size of the optic disk; afterwards micro-aneurysms are alike in color and closeness with blood vessels. The primary objective of this review is to survey the methods, techniques potential benefits and limitations of automated detection of micro-aneurysm in order to better manage translation into clinical practice, based on extensive experience with systems used by opthalmologists treating diabetic retinopathy

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

Enhancing Retinal Scan Classification: A Comparative Study of Transfer Learning and Ensemble Techniques

Ophthalmic diseases are a significant health concern globally, causing visual impairment and blindness in millions of people, particularly in dispersed populations. Among these diseases, retinal fundus diseases are a leading cause of irreversible vision loss, and early diagnosis and treatment can prevent this outcome. Retinal fundus scans have become an indispensable tool for doctors to diagnose multiple ocular diseases simultaneously. In this paper, the results of a variety of deep learning models (DenseNet-201, ResNet125V2, XceptionNet, EfficientNet-B7, MobileNetV2, and EfficientNetV2M) and ensemble learning approaches are presented, which can accurately detect 20 common fundus diseases by analyzing retinal fundus scan images. The proposed model is able to achieve a remarkable accuracy of 96.98% for risk classification and 76.92% for multi-disease detection, demonstrating its potential for use in clinical settings. By utilizing the proposed model, doctors can provide swift and accurate diagnoses to patients, improving their chances of receiving timely treatment and preserving their vision

Automatic Segmentation and Intuitive Visualisation of the Epiretinal Membrane in 3D OCT Images Using Deep Convolutional Approaches

[Abstract] Epiretinal Membrane (ERM) is a disease caused by a thin layer of scar tissue that is formed on the surface of the retina. When this membrane appears over the macula, it can cause distorted or blurred vision. Although normally idiopathic, its presence can also be indicative of other pathologies such as diabetic macular edema or vitreous haemorrhage. ERM removal surgery can preserve more visual acuity the earlier it is performed. For this purpose, we present a fully automatic segmentation system that can help the clinicians to determine the ERM presence and location over the eye fundus using 3D Optical Coherence Tomography (OCT) volumes. The proposed system uses a convolutional neural network architecture to classify patches of the retina surface. All the 2D OCT slices of the 3D OCT volume of a patient are combined to produce an intuitive colour map over the 2D fundus reconstruction, providing a visual representation of the presence of ERM which therefore facilitates the diagnosis and treatment of this relevant eye disease. A total of 2.428 2D OCT slices obtained from 20 OCT 3D volumes was used in this work. To validate the designed methodology, several representative experiments were performed. We obtained satisfactory results with a Dice Coefficient of 0.826 ± 0.112 and a Jaccard Index of 0.714 ± 0.155, proving its applicability for diagnosis purposes. The proposed system also demonstrated its simplicity and competitive performance with respect to other state-of-the-art approaches.10.13039/501100004587-Instituto de Salud Carlos III, Government of Spain, research project (Grant Number: DTS18/00136), 10.13039/501100004837-Ministerio de Ciencia e Innovación y Universidades, Government of Spain, research project (Grant Number: RTI2018-095894-B-I00), 10.13039/501100004837-Ministerio de Ciencia e Innovación, Government of Spain through the research project (Grant Number: PID2019-108435RB-I00), 10.13039/501100008425-Consellería de Cultura, Educación e Universidade, Xunta de Galicia, Grupos de Referencia Competitiva (Grant Number: ED431C 2020/24), 10.13039/501100010769-Axencia Galega de Innovación (GAIN), Xunta de Galicia (Grant Number: IN845D 2020/38), 10.13039/501100008425-CITIC, Centro de Investigación de Galicia, receives financial support from the Consellería de Educación, Universidade e Formación Profesional, Xunta de Galicia, through the ERDF (80%) and Secretaría Xeral de Universidades (20%) (Grant Number: ED431G 2019/01)Xunta de Galicia; ED431C 2020/24Xunta de Galicia; IN845D 2020/38Xunta de Galicia; ED431G 2019/0

A Foundation LAnguage-Image model of the Retina (FLAIR): Encoding expert knowledge in text supervision

Foundation vision-language models are currently transforming computer vision, and are on the rise in medical imaging fueled by their very promising generalization capabilities. However, the initial attempts to transfer this new paradigm to medical imaging have shown less impressive performances than those observed in other domains, due to the significant domain shift and the complex, expert domain knowledge inherent to medical-imaging tasks. Motivated by the need for domain-expert foundation models, we present FLAIR, a pre-trained vision-language model for universal retinal fundus image understanding. To this end, we compiled 37 open-access, mostly categorical fundus imaging datasets from various sources, with up to 97 different target conditions and 284,660 images. We integrate the expert's domain knowledge in the form of descriptive textual prompts, during both pre-training and zero-shot inference, enhancing the less-informative categorical supervision of the data. Such a textual expert's knowledge, which we compiled from the relevant clinical literature and community standards, describes the fine-grained features of the pathologies as well as the hierarchies and dependencies between them. We report comprehensive evaluations, which illustrate the benefit of integrating expert knowledge and the strong generalization capabilities of FLAIR under difficult scenarios with domain shifts or unseen categories. When adapted with a lightweight linear probe, FLAIR outperforms fully-trained, dataset-focused models, more so in the few-shot regimes. Interestingly, FLAIR outperforms by a large margin more generalist, larger-scale image-language models, which emphasizes the potential of embedding experts' domain knowledge and the limitations of generalist models in medical imaging.Comment: The pre-trained model is available at: https://github.com/jusiro/FLAI

A Mobile Solution for Lateral Segment Photographed Images based Deep Keratoconus Screening Method

Keratoconus (KC) is a condition of the bulging of the eye cornea. It is a common non-inflammatory ocular disorder that affects mostly the younger populace below the age of 30.  The eye cornea bulges because of the conical displacement of either outwards or downwards. Such condition can greatly reduce one’s visual ability. Therefore, in this paper, we afford a mobile solution to mitigate the KC disorder using the state-of-the-art deep transfer learning method. We intend to use the pre-trained VGGNet-16 model and a conventional convolutional neural network to detect KC automatically. The experimental work uses a total of 4000 side view lateral segment photographed images (LSPIs) comprising 2000 of KC and non-KC or healthy each involving 125 subjects. The LSPIs were extracted from the video data captured using a smartphone. Fine tuning of three hyperparameters namely the learning rate (LR), batch size (BS) and epoch number (EN) were carried out during the training phase to generate the best model of which, the VGGNet-16 model fulfilled it. For the KC detection task, our proposed model achieves an accuracy of 95.75%, a sensitivity of 92.25%, and specificity of 99.25% using the LR, BS and EN of 0.0001, 16, and 70, respectively. These results confirmed the high potential of our proposed solution to apprehend KC prevalence towards an automated KC screening procedure

Learning Two-Stream CNN for Multi-Modal Age-related Macular Degeneration Categorization

This paper tackles automated categorization of Age-related Macular Degeneration (AMD), a common macular disease among people over 50. Previous research efforts mainly focus on AMD categorization with a single-modal input, let it be a color fundus image or an OCT image. By contrast, we consider AMD categorization given a multi-modal input, a direction that is clinically meaningful yet mostly unexplored. Contrary to the prior art that takes a traditional approach of feature extraction plus classifier training that cannot be jointly optimized, we opt for end-to-end multi-modal Convolutional Neural Networks (MM-CNN). Our MM-CNN is instantiated by a two-stream CNN, with spatially-invariant fusion to combine information from the fundus and OCT streams. In order to visually interpret the contribution of the individual modalities to the final prediction, we extend the class activation mapping (CAM) technique to the multi-modal scenario. For effective training of MM-CNN, we develop two data augmentation methods. One is GAN-based fundus / OCT image synthesis, with our novel use of CAMs as conditional input of a high-resolution image-to-image translation GAN. The other method is Loose Pairing, which pairs a fundus image and an OCT image on the basis of their classes instead of eye identities. Experiments on a clinical dataset consisting of 1,099 color fundus images and 1,290 OCT images acquired from 1,099 distinct eyes verify the effectiveness of the proposed solution for multi-modal AMD categorization