RESUMEN
Diabetic retinopathy (DR) poses a significant challenge in diabetes management, with its progression often asymptomatic until advanced stages. This underscores the urgent need for cost-effective and reliable screening methods. Consequently, the integration of Artificial Intelligence tools presents a promising avenue to address this need effectively. We provide an overview of the current state of the art results and techniques in DR screening using AI, while also identifying gaps in research for future exploration. By synthesizing existing database and pinpointing areas requiring further investigation, this paper seeks to guide the direction of future research in the field of automatic diabetic retinopathy screening. There has been a continuous rise in the number of articles detailing Deep Learning methods designed for the automatic screening of Diabetic Retinopathy especially by the year 2021. Researchers utilized various databases, with a primary focus on the IDRiD dataset. This dataset comprises color fundus images captured at an ophthalmological clinic situated in India. It comprises 516 images that depict various stages of diabetic retinopathy and diabetic macular edema. Each of the chosen papers concentrates on various DR signs. Nevertheless, a significant portion of the authors primarily focused on detecting exudates, which remains insufficient to assess the overall presence of this disease. Various AI methods have been employed to identify DR signs. Among the chosen papers, 4.7% utilized detection methods, 46.5% employed classification techniques, 41.9% relied on segmentation, and 7% opted for a combination of classification and segmentation. Metrics calculated from 80% of the articles employing preprocessing techniques demonstrated the significant benefits of this approach in enhancing results quality. In addition, multiple Deep Learning techniques, starting by classification, detection then segmentation. Researchers used mostly YOLO for detection, ViT for classification and U-Net for segmentation. Another perspective on the evolving landscape of AI models for diabetic retinopathy screening lies in the increasing adoption of Convolutional Neural Networks for classification tasks and U-Net architectures for segmentation purposes;However, there is a growing realization within the research community that these techniques, while powerful individually, can be even more effective when integrated. This integration holds promise for not only diagnosing DR but also accurately classifying its different stages, thereby enabling more tailored treatment strategies. Despite this potential, the development of AI models for DR screening is fraught with challenges. Chief among these is the difficulty in obtaining high-quality, labeled data necessary for training models to perform effectively. This scarcity of data poses significant barriers to achieving robust performance and can hinder progress in developing accurate screening systems. Moreover, managing the complexity of these models, particularly deep neural networks, presents its own set of challenges. Additionally, interpreting the outputs of these models and ensuring their reliability in real-world clinical settings remain ongoing concerns. Furthermore, the iterative process of training and adapting these models to specific datasets can be time-consuming and resource-intensive. These challenges underscore the multifaceted nature of developing effective AI models for DR screening. Addressing these obstacles requires concerted efforts from researchers, clinicians, and technologists to innovate new approaches and overcome existing limitations. By doing so, a full potential of AI may transform DR screening and improve patient outcomes.
RESUMEN
Diabetic retinopathy (DR) remains one of the world's frequent eye illnesses, leading to vision loss among working-aged individuals. Hemorrhages and exudates are examples of signs of DR. However, artificial intelligence (AI), particularly deep learning (DL), is poised to impact nearly every aspect of human life and gradually transform medical practice. Insight into the condition of the retina is becoming more accessible thanks to major advancements in diagnostic technology. AI approaches can be used to assess lots of morphological datasets derived from digital images in a rapid and noninvasive manner. Computer-aided diagnosis tools for automatic detection of DR early-stage signs will ease the pressure on clinicians. In this work, we apply two methods to the color fundus images taken on-site at the Cheikh Zaïd Foundation's Ophthalmic Center in Rabat to detect both exudates and hemorrhages. First, we apply the U-Net method to segment exudates and hemorrhages into red and green colors, respectively. Second, the You Look Only Once Version 5 (YOLOv5) method identifies the presence of hemorrhages and exudates in an image and predicts a probability for each bounding box. The segmentation proposed method obtained a specificity of 85%, a sensitivity of 85%, and a Dice score of 85%. The detection software successfully detected 100% of diabetic retinopathy signs, the expert doctor detected 99% of DR signs, and the resident doctor detected 84%.
RESUMEN
Hereditary connective tissue diseases form a heterogeneous group of disorders that affect collagen and extracellular matrix components. The cornea and the skin are among the major forms of connective tissues, and syndromes affecting both organs are often due to mutations in single genes. Brittle cornea syndrome is one of the pathologies that illustrates this association well. Furthermore, sex hormones are known to play a role in the maintenance of the structure and the integrity of the connective tissue including the skin and cornea, and may be involved in pathogenesis of oculocutaneous diseases. Herein, a double consanguineous family of Moroccan origin with two affected siblings, with suspected brittle cornea syndrome, was recruited. Ophthalmic examinations and genetic testing were performed in all the nuclear family individuals. Clinical examinations showed that the two affected boys presented with thinning of the cornea, blue sclera, keratoconus, hyperelasticity of the skin, joint hypermobility, muscle weakness, hearing loss and dental abnormalities that are compatible with the diagnosis of BCS disease. They showed however additional clinical signs including micropenis, hypospadias and cryptorchidism, suggesting abnormalities in endocrine pathways. Using a duo exome sequencing analysis performed in the mother and the propositus, we identified the novel homozygous missense mutation c.461G > A (p.Arg154Gln) in the short-chain dehydrogenase/reductase family 42E member 1 (SDR42E1) gene. This novel mutation, which co-segregated with the disease in the family, was predicted to be pathogenic by bioinformatics tools. SDR42E1 stability analysis using DynaMut web-server showed that the p.Arg154Gln mutations has a destabilizing effect with a ΔΔG value of -1.039 kcal/mol. As this novel gene belongs to the large family of short-chain dehydrogenases/reductases (SDR) thought to be involved in steroid biosynthesis, endocrinological investigations subsequently revealed that the two patients also had low levels of cholesterol. Karyotyping revealed a normal 46,XY karyotype for the two boys, excluding other causes of disorders of sex development due to chromosomal rearrangements. In conclusion, our study reveals that mutation in the novel SDR42E1 gene alters the steroid hormone synthesis and associated with a new syndrome we named oculocutaneous genital syndrome. In addition, this study highlights the role of SDR42E1 in the regulation of cholesterol metabolism in the maintenance of connective tissue and sexual maturation in humans.
