170 research outputs found
Novel Fundus Image Preprocessing for Retcam Images to Improve Deep Learning Classification of Retinopathy of Prematurity
Retinopathy of Prematurity (ROP) is a potentially blinding eye disorder
because of damage to the eye's retina which can affect babies born prematurely.
Screening of ROP is essential for early detection and treatment. This is a
laborious and manual process which requires trained physician performing
dilated ophthalmological examination which can be subjective resulting in lower
diagnosis success for clinically significant disease. Automated diagnostic
methods can assist ophthalmologists increase diagnosis accuracy using deep
learning. Several research groups have highlighted various approaches. This
paper proposes the use of new novel fundus preprocessing methods using
pretrained transfer learning frameworks to create hybrid models to give higher
diagnosis accuracy. The evaluations show that these novel methods in comparison
to traditional imaging processing contribute to higher accuracy in classifying
Plus disease, Stages of ROP and Zones. We achieve accuracy of 97.65% for Plus
disease, 89.44% for Stage, 90.24% for Zones with limited training dataset.Comment: 10 pages, 4 figures, 7 tables. arXiv admin note: text overlap with
arXiv:1904.08796 by other author
A Review on Machine Learning Methods in Diabetic Retinopathy Detection
Ocular disorders have a broad spectrum. Some of them, such as Diabetic Retinopathy, are more common in low-income or low-resource countries. Diabetic Retinopathy is a cause related to vision loss and ocular impairment in the world. By identifying the symptoms in the early stages, it is possible to prevent the progress of the disease and also reach blindness. Considering the prevalence of different branches of Artificial Intelligence in many fields, including medicine, and the significant progress achieved in the use of big data to investigate ocular impairments, the potential of Artificial Intelligence algorithms to process and analyze Fundus images was used to identify symptoms associated with Diabetic Retinopathy. Under the studies, the proposed models for transformers provide better interpretability for doctors and scientists. Artificial Intelligence algorithms are also helpful in anticipating future health issues after appraising premature cases of the ailment. Especially in ophthalmology, a trustworthy diagnosis of visual outcomes helps physicians in advising disease and clinical decision-making while reducing health management costs
FRAMEWORK FOR LOW-QUAL ITY RETINAL MOSAICING
The medical equipment used to capture retinal fundus images is generally expensive.
With the development of technology and the emergence of smartphones, new portable
screening options have emerged, one of them being the D-Eye device. This and
other similar devices associated with a smartphone, when compared to specialized
equipment, present lower quality in the retinal video captured, yet with sufficient
quality to perform a medical pre-screening. From this, if necessary, individuals can
be referred for specialized screening, in order to obtain a medical diagnosis.
This dissertation contributes to the development of a framework, which is a tool
that allows grouping a set of developed and explored methods, applied to low-quality
retinal videos. Three areas of intervention were defined: the extraction of relevant
regions in video sequences; creating mosaicing images in order to obtain a summary
image of each retinal video; develop of a graphical interface to accommodate the
previous contributions.
To extract the relevant regions from these videos (the retinal zone), two methods
were proposed, one of them is based on more classical image processing approaches
such as thresholds and Hough Circle transform. The other performs the extraction
of the retinal location by applying a neural network, which is one of the methods
reported in the literature with good performance for object detection, the YOLOv4.
The mosaicing process was divided into two stages; in the first stage, the GLAMpoints
neural network was applied to extract relevant points. From these, some
transformations are carried out to have in the same referential the overlap of common
regions of the images. In the second stage, a smoothing process was performed
in the transition between images.
A graphical interface was developed to encompass all the above methods to
facilitate access to and use of them. In addition, other features were implemented,
such as comparing results with ground truth and exporting videos containing only
regions of interest
Artificial intelligence for diabetic retinopathy in low-income and middle-income countries: a scoping review.
Diabetic retinopathy (DR) is a leading cause of blindness globally. There is growing evidence to support the use of artificial intelligence (AI) in diabetic eye care, particularly for screening populations at risk of sight loss from DR in low-income and middle-income countries (LMICs) where resources are most stretched. However, implementation into clinical practice remains limited. We conducted a scoping review to identify what AI tools have been used for DR in LMICs and to report their performance and relevant characteristics. 81 articles were included. The reported sensitivities and specificities were generally high providing evidence to support use in clinical practice. However, the majority of studies focused on sensitivity and specificity only and there was limited information on cost, regulatory approvals and whether the use of AI improved health outcomes. Further research that goes beyond reporting sensitivities and specificities is needed prior to wider implementation
A ResNet is All You Need? Modeling A Strong Baseline for Detecting Referable Diabetic Retinopathy in Fundus Images
Deep learning is currently the state-of-the-art for automated detection of
referable diabetic retinopathy (DR) from color fundus photographs (CFP). While
the general interest is put on improving results through methodological
innovations, it is not clear how good these approaches perform compared to
standard deep classification models trained with the appropriate settings. In
this paper we propose to model a strong baseline for this task based on a
simple and standard ResNet-18 architecture. To this end, we built on top of
prior art by training the model with a standard preprocessing strategy but
using images from several public sources and an empirically calibrated data
augmentation setting. To evaluate its performance, we covered multiple
clinically relevant perspectives, including image and patient level DR
screening, discriminating responses by input quality and DR grade, assessing
model uncertainties and analyzing its results in a qualitative manner. With no
other methodological innovation than a carefully designed training, our ResNet
model achieved an AUC = 0.955 (0.953 - 0.956) on a combined test set of 61007
test images from different public datasets, which is in line or even better
than what other more complex deep learning models reported in the literature.
Similar AUC values were obtained in 480 images from two separate in-house
databases specially prepared for this study, which emphasize its generalization
ability. This confirms that standard networks can still be strong baselines for
this task if properly trained.Comment: Accepted for publication at the 18th International Symposium on
Medical Information Processing and Analysis (SIPAIM 2022
Artificial Intelligence in Medicine: A New Way to Diagnose and Treat Disease
Artificial intelligence (AI) has immense potential to transform medicine by improving diagnostic accuracy and enabling personalized treatments. This paper explores how AI systems analyze medical images, lab tests, genetic data, and patient histories to detect disease earlier and guide therapy selection. Though still an emerging field, impressive results demonstrate AI can surpass human clinicians on diagnostic tasks. For example, an AI system detected breast cancer from mammograms more accurately than expert radiologists. In ophthalmology, AI outperformed ophthalmologists in diagnosing diabetic retinopathy. By finding subtle patterns in complex datasets, AI promises to catch diseases like cancer in early, more treatable stages. Beyond diagnosis, AI can identify optimal treatments for individual patients based on their genetic makeup and lifestyle factors. Researchers are also using AI to design new medications. While AI offers many benefits, challenges remain regarding clinician displacement, legal liability, data privacy, and the "black box" nature of AI reasoning. More research is needed, but it is clear that AI will fundamentally alter medical practice. AI empowers clinicians to provide earlier, more precise diagnoses and tailored therapies for patients. Though it will not replace doctors, by automating routine tasks and uncovering hidden insights, AI can free physicians to focus on holistic care. The future of medicine lies in humans and smart machines working together
Artificial Intelligence in Assessing Cardiovascular Diseases and Risk Factors via Retinal Fundus Images: A Review of the Last Decade
Background: Cardiovascular diseases (CVDs) continue to be the leading cause
of mortality on a global scale. In recent years, the application of artificial
intelligence (AI) techniques, particularly deep learning (DL), has gained
considerable popularity for evaluating the various aspects of CVDs. Moreover,
using fundus images and optical coherence tomography angiography (OCTA) to
diagnose retinal diseases has been extensively studied. To better understand
heart function and anticipate changes based on microvascular characteristics
and function, researchers are currently exploring the integration of AI with
non-invasive retinal scanning. Leveraging AI-assisted early detection and
prediction of cardiovascular diseases on a large scale holds excellent
potential to mitigate cardiovascular events and alleviate the economic burden
on healthcare systems. Method: A comprehensive search was conducted across
various databases, including PubMed, Medline, Google Scholar, Scopus, Web of
Sciences, IEEE Xplore, and ACM Digital Library, using specific keywords related
to cardiovascular diseases and artificial intelligence. Results: A total of 87
English-language publications, selected for relevance were included in the
study, and additional references were considered. This study presents an
overview of the current advancements and challenges in employing retinal
imaging and artificial intelligence to identify cardiovascular disorders and
provides insights for further exploration in this field. Conclusion:
Researchers aim to develop precise disease prognosis patterns as the aging
population and global CVD burden increase. AI and deep learning are
transforming healthcare, offering the potential for single retinal image-based
diagnosis of various CVDs, albeit with the need for accelerated adoption in
healthcare systems.Comment: 40 pages, 5 figures, 2 tables, 91 reference
Deep learning for diabetic retinopathy analysis : a review, research challenges, and future directions
Deep learning (DL) enables the creation of computational models comprising multiple processing layers that learn data representations at multiple levels of abstraction. In the recent past, the use of deep learning has been proliferating, yielding promising results in applications across a growing number of fields, most notably in image processing, medical image analysis, data analysis, and bioinformatics. DL algorithms have also had a significant positive impact through yielding improvements in screening, recognition, segmentation, prediction, and classification applications across different domains of healthcare, such as those concerning the abdomen, cardiac, pathology, and retina. Given the extensive body of recent scientific contributions in this discipline, a comprehensive review of deep learning developments in the domain of diabetic retinopathy (DR) analysis, viz., screening, segmentation, prediction, classification, and validation, is presented here. A critical analysis of the relevant reported techniques is carried out, and the associated advantages and limitations highlighted, culminating in the identification of research gaps and future challenges that help to inform the research community to develop more efficient, robust, and accurate DL models for the various challenges in the monitoring and diagnosis of DR
Image Quality Improvement of Medical Images using Deep Learning for Computer-aided Diagnosis
Retina image analysis is an important screening tool for early detection of multiple dis eases such as diabetic retinopathy which greatly impairs visual function. Image analy sis and pathology detection can be accomplished both by ophthalmologists and by the
use of computer-aided diagnosis systems. Advancements in hardware technology led to
more portable and less expensive imaging devices for medical image acquisition. This
promotes large scale remote diagnosis by clinicians as well as the implementation of
computer-aided diagnosis systems for local routine disease screening. However, lower cost equipment generally results in inferior quality images. This may jeopardize the
reliability of the acquired images and thus hinder the overall performance of the diagnos tic tool. To solve this open challenge, we carried out an in-depth study on using different
deep learning-based frameworks for improving retina image quality while maintaining
the underlying morphological information for the diagnosis. Our results demonstrate
that using a Cycle Generative Adversarial Network for unpaired image-to-image trans lation leads to successful transformations of retina images from a low- to a high-quality
domain. The visual evidence of this improvement was quantitatively affirmed by the two
proposed validation methods. The first used a retina image quality classifier to confirm a
significant prediction label shift towards a quality enhance. On average, a 50% increase
of images being classified as high-quality was verified. The second analysed the perfor mance modifications of a diabetic retinopathy detection algorithm upon being trained
with the quality-improved images. The latter led to strong evidence that the proposed
solution satisfies the requirement of maintaining the images’ original information for
diagnosis, and that it assures a pathology-assessment more sensitive to the presence of
pathological signs. These experimental results confirm the potential effectiveness of our
solution in improving retina image quality for diagnosis. Along with the addressed con tributions, we analysed how the construction of the data sets representing the low-quality
domain impacts the quality translation efficiency. Our findings suggest that by tackling
the problem more selectively, that is, constructing data sets more homogeneous in terms
of their image defects, we can obtain more accentuated quality transformations
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