Artificial intelligence-based prediction of diabetic retinopathy evolution (EviRed): protocol for a prospective cohort.

INTRODUCTION: An important obstacle in the fight against diabetic retinopathy (DR) is the use of a classification system based on old imaging techniques and insufficient data to accurately predict its evolution. New imaging techniques generate new valuable data, but we lack an adapted classification based on these data. The main objective of the Evaluation Intelligente de la Rétinopathie Diabétique, Intelligent evaluation of DR (EviRed) project is to develop and validate a system assisting the ophthalmologist in decision-making during DR follow-up by improving the prediction of its evolution. METHODS AND ANALYSIS: A cohort of up to 5000 patients with diabetes will be recruited from 18 diabetology departments and 14 ophthalmology departments, in public or private hospitals in France and followed for an average of 2 years. Each year, systemic health data as well as ophthalmological data will be collected. Both eyes will be imaged by using different imaging modalities including widefield photography, optical coherence tomography (OCT) and OCT-angiography. The EviRed cohort will be divided into two groups: one group will be randomly selected in each stratum during the inclusion period to be representative of the general diabetic population. Their data will be used for validating the algorithms (validation cohort). The data for the remaining patients (training cohort) will be used to train the algorithms. ETHICS AND DISSEMINATION: The study protocol was approved by the French South-West and Overseas Ethics Committee 4 on 28 August 2020 (CPP2020-07-060b/2020-A01725-34/20.06.16.41433). Prior to the start of the study, each patient will provide a written informed consent documenting his or her agreement to participate in the clinical trial. Results of this research will be disseminated in peer-reviewed publications and conference presentations. The database will also be available for further study or development that could benefit patients. TRIAL REGISTRATION NUMBER: NCT04624737.

Hybrid Fusion of High-Resolution and Ultra-Widefield OCTA Acquisitions for the Automatic Diagnosis of Diabetic Retinopathy.

Optical coherence tomography angiography (OCTA) can deliver enhanced diagnosis for diabetic retinopathy (DR). This study evaluated a deep learning (DL) algorithm for automatic DR severity assessment using high-resolution and ultra-widefield (UWF) OCTA. Diabetic patients were examined with 6×6 mm2 high-resolution OCTA and 15×15 mm2 UWF-OCTA using PLEX®Elite 9000. A novel DL algorithm was trained for automatic DR severity inference using both OCTA acquisitions. The algorithm employed a unique hybrid fusion framework, integrating structural and flow information from both acquisitions. It was trained on data from 875 eyes of 444 patients. Tested on 53 patients (97 eyes), the algorithm achieved a good area under the receiver operating characteristic curve (AUC) for detecting DR (0.8868), moderate non-proliferative DR (0.8276), severe non-proliferative DR (0.8376), and proliferative/treated DR (0.9070). These results significantly outperformed detection with the 6×6 mm2 (AUC = 0.8462, 0.7793, 0.7889, and 0.8104, respectively) or 15×15 mm2 (AUC = 0.8251, 0.7745, 0.7967, and 0.8786, respectively) acquisitions alone. Thus, combining high-resolution and UWF-OCTA acquisitions holds the potential for improved early and late-stage DR detection, offering a foundation for enhancing DR management and a clear path for future works involving expanded datasets and integrating additional imaging modalities.

Reliability and Reproducibility of Pigment Epithelial Detachment Volume Measurements in AMD Using a New Tool: ReVAnalyzer.

BACKGROUND AND OBJECTIVE: To describe and present the reliability and reproducibility of a new software, Retinal Volume Analyzer (ReVAnalyzer), for pigment epithelium detachment (PED) volume quantification. PATIENTS AND METHODS: This is a retrospective study including patients with PEDs secondary to exudative age-related macular degeneration (AMD). Macular volume scans on spectral-domain optical coherence tomography on enhanced depth imaging mode were performed in all eyes. Image batches were then exported in .xml format to the ReVAnalyzer software. A semiautomated PED volume measurement was performed by three independent readers (RBG, VC, OS) twice, at the beginning and end of a 15-day period. Bland-Altman assessment for agreement was used to compare intra- and interobserver observations. RESULTS: Twenty eyes of 20 patients presenting with PED were analyzed. Bland-Altman analysis indicated a good agreement between inter- and intraobserver measurements. The intraclass correlation coefficient for intraobserver PED volume measurements and between the three observers (interobserver) was greater than 0.99, demonstrating high reproducibility and consistency of the methodology. CONCLUSIONS: ReVAnalyzer is a reliable tool that can assist in the analysis of PED volume with high reproducibility. This type of specific retinal volume analysis can be of help for monitoring disease activity and therapeutic response in AMD. [Ophthalmic Surg Lasers Imaging Retina. 2019;50:e242-e249.].

Exudate detection in color retinal images for mass screening of diabetic retinopathy.

The automatic detection of exudates in color eye fundus images is an important task in applications such as diabetic retinopathy screening. The presented work has been undertaken in the framework of the TeleOphta project, whose main objective is to automatically detect normal exams in a tele-ophthalmology network, thus reducing the burden on the readers. A new clinical database, e-ophtha EX, containing precisely manually contoured exudates, is introduced. As opposed to previously available databases, e-ophtha EX is very heterogeneous. It contains images gathered within the OPHDIAT telemedicine network for diabetic retinopathy screening. Image definition, quality, as well as patients condition or the retinograph used for the acquisition, for example, are subject to important changes between different examinations. The proposed exudate detection method has been designed for this complex situation. We propose new preprocessing methods, which perform not only normalization and denoising tasks, but also detect reflections and artifacts in the image. A new candidates segmentation method, based on mathematical morphology, is proposed. These candidates are characterized using classical features, but also novel contextual features. Finally, a random forest algorithm is used to detect the exudates among the candidates. The method has been validated on the e-ophtha EX database, obtaining an AUC of 0.95. It has been also validated on other databases, obtaining an AUC between 0.93 and 0.95, outperforming state-of-the-art methods.

Multimedia data mining for automatic diabetic retinopathy screening.

This paper presents TeleOphta, an automatic system for screening diabetic retinopathy in teleophthalmology networks. Its goal is to reduce the burden on ophthalmologists by automatically detecting non referable examination records, i.e. examination records presenting no image quality problems and no pathological signs related to diabetic retinopathy or any other retinal pathology. TeleOphta is an attempt to put into practice years of algorithmic developments from our groups. It combines image quality metrics, specific lesion detectors and a generic pathological pattern miner to process the visual content of eye fundus photographs. This visual information is further combined with contextual data in order to compute an abnormality risk for each examination record. The TeleOphta system was trained and tested on a large dataset of 25,702 examination records from the OPHDIAT screening network in Paris. It was able to automatically detect 68% of the non referable examination records while achieving the same sensitivity as a second ophthalmologist. This suggests that it could safely reduce the burden on ophthalmologists by 56%.

Rapid image evaluation system for corneal in vivo confocal microscopy.

PURPOSE: To develop rapid image processing techniques for the objective analysis of corneal in vivo confocal micrographs. METHODS: Perpendicular central corneal volume scans from healthy volunteers were obtained via laser in vivo confocal microscopy. The layer in each volume scan that contained the nerve plexus was detected by applying software operators to analyze image features on the basis of their size, shape, and contrast. Dendritic immune cells were detected in the nerve image on the basis of cellular size, lack of elongation, and brightness relative to the nerves. Images that were 20 µm anterior to the best nerve layer images were used for the analysis of epithelial wing cells; wing cell detection was based on extended regional minima and a watershed transformation. RESULTS: The software successfully detected the best nerve layer images in 15 scans from 15 eyes. Manual and automatic analyses were 81.8% in agreement for dendritic immune cells (for 11 cells in a representative image) and 94.4% in agreement for wing cells (for 466 cells in the image). Within 10 seconds per scan, the software calculated the number, mean length, and mean density of immune cells; the number, mean size, and mean density of wing cells; and the number and mean length of nerves. Factors defining the shape and position of cells and nerves also were available. CONCLUSIONS: The software rapidly and accurately analyzed the in vivo confocal micrographs of the healthy central corneas, yielding quantitative results to describe the nerves, dendritic immune cells, and wing cells.

A multiple-instance learning framework for diabetic retinopathy screening.

A novel multiple-instance learning framework, for automated image classification, is presented in this paper. Given reference images marked by clinicians as relevant or irrelevant, the image classifier is trained to detect patterns, of arbitrary size, that only appear in relevant images. After training, similar patterns are sought in new images in order to classify them as either relevant or irrelevant images. Therefore, no manual segmentations are required. As a consequence, large image datasets are available for training. The proposed framework was applied to diabetic retinopathy screening in 2-D retinal image datasets: Messidor (1200 images) and e-ophtha, a dataset of 25,702 examination records from the Ophdiat screening network (107,799 images). In this application, an image (or an examination record) is relevant if the patient should be referred to an ophthalmologist. Trained on one half of Messidor, the classifier achieved high performance on the other half of Messidor (A(z)=0.881) and on e-ophtha (A(z)=0.761). We observed, in a subset of 273 manually segmented images from e-ophtha, that all eight types of diabetic retinopathy lesions are detected.

Studying disagreements among retinal experts through image analysis.

In recent years, many image analysis algorithms have been presented to assist Diabetic Retinopathy (DR) screening. The goal was usually to detect healthy examination records automatically, in order to reduce the number of records that should be analyzed by retinal experts. In this paper, a novel application is presented: these algorithms are used to 1) discover image characteristics that sometimes cause an expert to disagree with his/her peers and 2) warn the expert whenever these characteristics are detected in an examination record. In a DR screening program, each examination record is only analyzed by one expert, therefore analyzing disagreements among experts is challenging. A statistical framework, based on Parzen-windowing and the Patrick-Fischer distance, is presented to solve this problem. Disagreements among eleven experts from the Ophdiat screening program were analyzed, using an archive of 25,702 examination records.