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Drusen, small lipid deposits located below the retina, are early biomarkers of age-related macular degeneration (AMD), a condition that leads to visual impairment worldwide, especially among the elderly. The presence of AMD is linked with Alzheimer's Disease (AD) and dense deposit disease (DDD), emphasizing the critical need for early and accurate detection of drusen in retinal tissues. Optical Coherence Tomography (OCT), with its non-invasive and high-resolution imaging capabilities, stands as a pivotal tool for early AMD diagnosis through the identification of drusen. However, the reliance on manual segmentation of drusen in 3D OCT images introduces significant challenges: it is not only time-consuming but also subject to inter-observer variability, severely constraining its efficacy for widespread screening applications. These limitations underscore the critical need for an automated solution that can improve diagnostic workflows, ensure consistency across interpretations, and facilitate the processing of large datasets with improved accuracy and efficiency. In response, we propose a new deep learning-based, fully-automatic end-to-end approach for the segmentation of drusen in 3D OCT volumes. Complementary, a pivotal aspect of our research involves the first detailed comparative analysis between 2D and 3D end-to-end approaches. This comparison is crucial for understanding the impact of dimensional spatial information on the accuracy of drusen segmentation within OCT scans. The findings demonstrate that the 3D approach, by leveraging the depth and complexity of spatial data available in 3D OCT volumes, markedly surpasses the 2D approach. This superior performance underlines the importance of 3D spatial information in enhancing diagnostic precision. Through automating the segmentation process, our proposal not only makes AMD screening more efficient and precise but also significantly advances the diagnosis of retinal diseases, potentially enriching our understanding of systemic conditions connected through retinal biomarkers. [ABSTRACT FROM AUTHOR]

Patients at high risk of contracting COVID-19 require specialized monitoring throughout their illness to ensure optimal treatment at each stage. To support this monitoring, Computer-Aided Diagnosis (CAD) methods analyze clinical data to estimate the most likely outcome for each patient, using various clinical variables such as symptoms, medical history, and laboratory results to predict outcomes. Despite the numerous proposals for COVID-19 diagnosis using CAD methods, the lack of explainability in many machine learning models poses a challenge in incorporating these methods into clinical practice. Additionally, other crucial tasks such as estimating the risk of death or severe forms of the disease must be considered to identify cases that require greater monitoring. To overcome these challenges, we propose an explainable methodology for estimating the risk of hospitalization and death in COVID-19 patients using clinical data. Our methodology employs four machine learning algorithms, three feature selection methods, and a decision tree to provide explainability. Our approach achieves an accuracy of 86.16% + 0.74% for the estimation of hospitalization risk with 29 features, and an accuracy of 86.40% + 1.80% for the estimation of the risk of death with 26 features. Moreover, our methodology provides valuable insights into the relationship between clinical variables and patient outcomes, which can inform more robust and informed clinical decision-making and improve our understanding of the disease. We demonstrate the potential of our transparent and effective CAD methods to support clinical decision-making in COVID-19 patient care and further research, offering a promising solution to overcome the challenges in incorporating CAD methods into clinical practice. [ABSTRACT FROM AUTHOR]

COVID-19 is a challenging disease that was declared as global pandemic in March 2020. As the main impact of this disease is located in the pulmonary regions, chest X-ray devices are very useful to understand the severity of the disease on each patient. In order to reduce the risk of cross-contamination, the radiologists are recommended to use portable devices instead of fixed machinery, as these devices are easier to decontaminate. Moreover, the development of reliable and robust methodologies of computer-aided diagnosis systems is very relevant to reduce the workload that expert clinicians are experiencing in the current moment. In this work, we propose a comprehensive analysis of the deep features extracted from portable chest X-ray captures to perform a COVID-19 screening. We also study the optimal characterization of the problem with a lower dimensionality, contrasting the results of the feature selection methods that were chosen. Results demonstrated that the proposed approach is robust and reliable, obtaining a 90.43% of accuracy for the test set, using only 46.85% of the deep features in the context of poor quality and low detail X-ray images obtained from portable devices. [ABSTRACT FROM AUTHOR]

Optical Coherence Tomography (OCT) is a well-established medical imaging technique that allows a complete analysis and evaluation of the main retinal structures and their histopathology properties. Diabetic Macular Edema (DME) implies the accumulation of intraretinal fluid within the macular region. Diffuse Retinal Thickening (DRT) edemas are considered a relevant case of DME disease, where the pathological regions are characterized by a "sponge-like" appearance and a reduced intraretinal reflectivity, being visible in OCT images. Additionally, the presence of other structures may alter the OCT image characteristics, confusing the pathological identification process. This is the case of the retinal vessels over all the eye fundus, whose presence produce shadow projections over the retinal layers that may hide the "sponge-like" appearance of the DRT edemas. Thus, in this paper, we present a proposal for the automatic extraction of DRT edemas, also using as reference the information provided by the automatic identifications of the retinal vessels in the OCT images. To do that, firstly, the system delimits three retinal regions of interest. These retinal regions facilitate the posterior identification of the vessel structures and the segmentation of the DRT regions. For the identification of the vessels structures, the method combined the localization of the upper bright vascular profiles with the presence of their corresponding lower dark vascular shadows. Finally, a learning strategy is implemented for the segmentation of the DRT edemas. Satisfactory results were obtained, reaching values of 0.8346 and 0.9051 of Jaccard index and Dice coefficient, respectively, for the extraction of the existing DRT edemas. [ABSTRACT FROM AUTHOR]

An accurate detection of the macular edema (ME) presence constitutes a crucial ophthalmological issue as it provides useful information for the identification, diagnosis and treatment of different relevant ocular and systemic diseases. Serous Retinal Detachment (SRD) is a particular type of ME, which is characterized by the leakage of fluid that has a propensity of being accumulated in the macular region. This paper proposes a new methodology for the automatic identification and characterization of the SRD edema using Optical Coherence Tomography (OCT) images. The subretinal fluids and the External Limiting Membrane (ELM) retinal layers are identified and characterized to measure the disease severity. Four different visualization modules were designed including representative derived parameters to facilitate the doctor’s work in the diagnostic evaluation of ME. The different steps of this method were validated using the manual labelling provided by an expert clinician. The validation of the proposed method offered satisfactory results, constituting a suitable scenario with intuitive visual representations that also include different relevant biomarkers. [ABSTRACT FROM AUTHOR]

Optical Coherence Tomography Angiography (OCT-A) represents a new modality of ophthalmological imaging that stands out for being a non-invasive capture technique that facilitates the analysis of the vascular characteristics of the eye fundus. In this paper, we propose a complete automatic methodology that identifies the vascular and avascular zones in OCT-A images, quantifying each one of them for their posterior use in clinical analyses and diagnostic processes. To achieve this, we firstly intensify the vascular characteristics to facilitate the posterior extraction. Then, a set of image processing techniques are combined to differentiate both vascular and avascular regions and, finally, measure their representative parameters. The proposed methodology was tested on a set of images that were marked by an expert ophthalmologist, being used as reference in the validation of the method. The proposed approach presented satisfactory results in the validation experiments with the vascular and avascular measurements, demonstrating their utility for the diagnosis and monitoring of different vascular diseases that are frequently analysed through the retinal microcirculation. [ABSTRACT FROM AUTHOR]

The retinal vascular tortuosity is a commonly used parameter for the early diagnosis of several diseases that affects the circulatory system. The manual analysis of fundus images for the tortuosity characterization is a time-consuming and subjective task that presents a high inter-rater variability. Thus, automatic image processing methods allow the efficient computation of objective and stable parameters for the issue. The validation of these methods is crucial to ensure an objective and reliable environment for the retinal experts. This paper describes a multi-expert analysis that measures the clinical performance as well as a validation procedure of the computational tortuosity module of the Sirius framework, a computer-aided diagnosis platform for analyzing retinal images. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy is one of the leading causes of vision impairment that is commonly associated to the Macular Edema (ME) disease. The Diffuse Retinal Thickening (DRT) is a ME type derived from the local intraretinal fluid accumulation in the lower retinal layers, producing significant morphological alterations in the eye fundus. The presence and properties of these intraretinal fluids are used by the ophthalmologists as significant indicators of the clinical stage of the ME disease. Given that, the precise identification and segmentation of the DRT edema type allow the early diagnosis of the ME disease which, therefore, permits a better adjustment of the treatments, reducing their costs as well as improving the life quality of the patients. This paper proposes a novel methodology for the automatic identification and segmentation of the DRT edemas using Optical Coherence Tomography (OCT) images as source of information. Firstly, the method identifies four of the principal retinal layers that are used as reference to delimit the outer retina, region where the DRT edemas are typically originated. Inside this region, a large and heterogeneous set of features was defined to recognize the characteristic “sponge-like” patterns of the DRT edema, using intensity, texture and clinically-defined features. For this analysis, four representative classifiers were employed with the best subsets of previously selected features. This methodology was tested using 70 OCT images from where 560 samples were extracted with the presence and absence of DRT edemas. The best results were achieved by the 7- k NN classifier, reaching in the detection stage an accuracy of 0.9366, whereas in the segmentation stage obtained values of 0.6625 and 0.7899 for the Jaccard and Dice coefficients, respectively. [ABSTRACT FROM AUTHOR]

Optical Coherence Tomography (OCT) is a well-established medical imaging technique that allows the analysis of the eye fundus characteristics in real time. These images enable the experts to make a clinical evaluation of the retinal vasculature, whose morphology provides relevant information for diseases like diabetes, hypertension or arteriosclerosis. In this paper, we present a novel proposal for the automatic vasculature identification in retinal OCT images. To achieve this, we analyse the intensity profiles between representative retinal layers, previously segmented. Then, two statistical models are generated using representative samples of vessel and non-vessel profiles. The analysis of both statistical models let us optimize the discrimination of both cathegories that is used, finally, to identify the vessel locations. The proposed method was adjusted and validated using 256 OCT images, including 1274 vascular structures that were labelled by an expert clinician. Satisfactory results were provided as a precision of 94.55% and a recall of 90.25% were obtained, respectively. The method facilitates the doctors’ work allowing better analysis and treatments of vascular diseases. [ABSTRACT FROM AUTHOR]

Optical Coherence Tomography (OCT) is, nowadays, a clinical standard imaging technique in opthalmology as it provides more information than other classical modalities as can be, for instance, retinographies. OCT scans show a 3D representation of the real layout of the eye fundus in a non-invasive way, letting clinicians inspect deeply the retinal layers in a cross-sectional visualization. For that reason, OCT scans are commonly used in the study of the retinal morphology and the identification of pathological structures. Among them, an appropriate identification and analysis of any present intraretinal cystoid region is crucial to perform an adequate diagnosis of the exudative macular disease, one of the main causes of blindness in developed countries. In this work, we analyzed and characterized the intraretinal cystoid regions in OCT images by the definition of a complete and heterogeneous set of 326 intensity and texture-based features. Relief-F and L0 feature selectors were used in order to identify the optimal feature subsets that provide the best discriminative power. Representative classifiers, as the Linear Bayes Normal Classifier (LDC), Quadratic Bayes Normal Classifier (QDC) and K-Nearest Neighbor Classifier (KNN) were finally used to evaluate the potential of identification of the feature subsets. The method was validated using 51 OCT images. From them, 363 and 360 samples of cystoid and non-cystoid regions were selected, respectively. The best results were offered by the LDC classifier that, using a feature subset identified by the L0 selector, provided an accuracy of 0.9060. [ABSTRACT FROM AUTHOR]

COVID-19 is a global threat for the healthcare systems due to the rapid spread of the pathogen that causes it. In such situation, the clinicians must take important decisions, in an environment where medical resources can be insufficient. In this task, the computer-aided diagnosis systems can be very useful not only in the task of supporting the clinical decisions but also to perform relevant analyses, allowing them to understand better the disease and the factors that can identify the high risk patients. For those purposes, in this work, we use several machine learning algorithms to estimate the outcome of COVID-19 patients given their clinical information. Particularly, we perform 2 different studies: the first one estimates whether the patient is at low or at high risk of death whereas the second estimates if the patient needs hospitalization or not. The results of the analyses of this work show the most relevant features for each studied scenario, as well as the classification performance of the considered machine learning models. In particular, the XGBoost algorithm is able to estimate the need for hospitalization of a patient with an AUC-ROC of 0. 8415 ± 0. 0217 while it can also estimate the risk of death with an AUC-ROC of 0. 7992 ± 0. 0104. Results have demonstrated the great potential of the proposal to determine those patients that need a greater amount of medical resources for being at a higher risk. This provides the healthcare services with a tool to better manage their resources. • The COVID-19 outcome estimation improves the management of the healthcare resources. • 2 critical analysis are performed on patients: risk of hospitalization and death. • The features are ranked by importance using 3 different feature selection methods. • 5 machine learning models are trained to perform the proposed classification tasks. • The analyses identify relevant variables for each estimation scenario. [Display omitted] [ABSTRACT FROM AUTHOR]

One of the main issues with deep learning is the need of a significant number of samples. We intend to address this problem in the field of Optical Coherence Tomography (OCT), specifically in the context of Diabetic Macular Edema (DME). This pathology represents one of the main causes of blindness in developed countries and, due to the capturing difficulties and saturation of health services, the task of creating computer-aided diagnosis (CAD) systems is an arduous task. For this reason, we propose a solution to generate samples. Our strategy employs image-to-image Generative Adversarial Networks (GAN) to translate a binary mask into a realistic OCT image. Moreover, thanks to the clinical relationship between the retinal shape and the presence of DME fluid, we can generate both pathological and non-pathological samples by altering the binary mask morphology. To demonstrate the capabilities of our proposal, we test it against two classification strategies of the state-of-the-art. In the first one, we evaluate a system fully trained with generated images, obtaining 94.83% accuracy with respect to the state-of-the-art. In the second case, we tested it against a state-of-the-art expert model based on deep features, in which it also achieved successful results with a 98.23% of the accuracy of the original work. This way, our methodology proved to be useful in scenarios where data is scarce, and could be easily adapted to other imaging modalities and pathologies where key shape constraints in the image provide enough information to recreate realistic samples. [Display omitted] • Fully-automatic proposal able to generate labeled normal and DME retinal OCT images. • First methodology able to generate realistic labeled samples from retinal masks. • Deep learning strategy to palliate the issue of data scarcity in the medical field. • Validated as training strategy and expert model, with results akin to the original. • Easily transferable strategy to other pathologies and imaging modalities. [ABSTRACT FROM AUTHOR]

Covid-19 is a new infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Given the seriousness of the situation, the World Health Organization declared a global pandemic as the Covid-19 rapidly around the world. Among its applications, chest X-ray images are frequently used for an early diagnostic/screening of Covid-19 disease, given the frequent pulmonary impact in the patients, critical issue to prevent further complications caused by this highly infectious disease. In this work, we propose 4 fully automatic approaches for the classification of chest X-ray images under the analysis of 3 different categories: Covid-19, pneumonia and healthy cases. Given the similarity between the pathological impact in the lungs between Covid-19 and pneumonia, mainly during the initial stages of both lung diseases, we performed an exhaustive study of differentiation considering different pathological scenarios. To address these classification tasks, we evaluated 6 representative state-of-the-art deep network architectures on 3 different public datasets: (I) Chest X-ray dataset of the Radiological Society of North America (RSNA); (II) Covid-19 Image Data Collection; (III) SIRM dataset of the Italian Society of Medical Radiology. To validate the designed approaches, several representative experiments were performed using 6,070 chest X-ray radiographs. In general, satisfactory results were obtained from the designed approaches, reaching a global accuracy values of 0.9706 ± 0.0044, 0.9839 ± 0.0102, 0.9744 ± 0.0104 and 0.9744 ± 0.0104, respectively, thus helping the work of clinicians in the diagnosis and consequently in the early treatment of this relevant pandemic pathology. [Display omitted] • Covid-19 is an infectious disease caused by the most recently discovered coronavirus. • We have proposed fully automated approaches for the analysis of Covid-19. • To validate the designed approaches, exhaustive experiments were performed. • The system provides accurate identification of this potentially fatal disease. [ABSTRACT FROM AUTHOR]