Digital dual test syphilis/HIV detection based on Fourier Descriptors of Cyclic Voltammetry curves.

BACKGROUND: Effective and timely detection is vital for mitigating the severe impacts of Sexually Transmitted Infections (STI), including syphilis and HIV. Cyclic Voltammetry (CV) sensors have shown promise as diagnostic tools for these STI, offering a pathway towards cost-effective solutions in primary health care settings. OBJECTIVE: This study aims to pioneer the use of Fourier Descriptors (FDs) in analyzing CV curves as 2D closed contours, targeting the simultaneous detection of syphilis and HIV. METHODS: Raw CV signals are filtered, resampled, and transformed into 2D closed contours for FD extraction. Essential shape characteristics are captured through selected coefficients. A complementary geometrical analysis further extracts features like curve areas and principal axes lengths from CV curves. A Mahalanobis Distance Classifier is employed for differentiation between patient and control groups. RESULTS: The evaluation of the proposed method revealed promising results with classification performance metrics such as Accuracy and F1-Score consistently achieving values rounded to 0.95 for syphilis and 0.90 for HIV. These results underscore the potential efficacy of the proposed approach in differentiating between patient and control samples for STI detection. CONCLUSION: By integrating principles from biosensors, signal processing, image processing, machine learning, and medical diagnostics, this study presents a comprehensive approach to enhance the detection of both syphilis and HIV. This setts the stage for advanced and accessible STI diagnostic solutions.

Half dose ChAdOx1 nCoV-19 vaccine was equivalent to full doses to reduce moderate and severe COVID-19 cases.

Objectives: Previously, we presented the effectiveness of ChAdOx1 nCoV-19 half-dose (HD) immunization for preventing new COVID-19 cases. Here, we evaluated the administration of an HD of ChAdOx1 nCoV-19 in the primary immunization protocol (up to two doses) in reducing moderate and severe cases, hospitalizations, and deaths when compared to the administration of full doses (FD) after a long-term follow-up. Methods: We evaluated data from 29,469 participants between January 2021 and November 2022 who received an HD or FD vaccine and crossed this information with their medical records to identify those who developed moderate or severe cases. All participants were classified into four groups according to their immunization status and followed 500 days after the last vaccine administration. Results: The propensity-score matching analysis indicates that the administration of the two HDs of ChAdOx1 nCoV-19 was equivalent to the use of two FDs to reduce moderate and severe COVID-19 cases. The relative risk of being infected and developing moderate or severe conditions after the administration of at least one HD or FD was similar 150 or 500 days after the administration of the immunizers. Conclusion: Administering two HDs can be used safely as a cost-effective alternative to the primary immunization protocol.

Evaluation of massive education in prison health: a perspective of health care for the person deprived of freedom in Brazil.

Education, with an emphasis on prison health, has acted as a policy inducing changes in work processes, which the Brazilian National Health System (SUS) has used, and which is present in permanent health education, which promotes health care for people deprived of liberty. This article aims to present an analysis of the impacts of the strategy of massive education on prison health in Brazil from the perspective of health professionals and other actors operating in the Brazilian prison system. The data used in the study come from a questionnaire consisting of 37 questions applied nationwide between March and June 2022. Responses were collected from students who completed the course "Health Care for People Deprived of Freedom" of the learning pathway "Prison System", available in the Virtual Learning Environment of the Brazilian Health System (AVASUS). This course was offered nationally, whose adhesion (enrollment) occurred spontaneously, i.e., the course was not a mandatory. The data collected allowed us to analyze the impacts of massive education on prison health. The study also shows that the search for the course is made by several areas of knowledge, with a higher incidence in the health area, but also in other areas, such as humanities, which also work directly with the guarantee of the rights of people deprived of liberty, which are professionals in the areas of social work, psychology, and education. The analysis based on the data suggests that the massive education mediated by technology through the courses of the learning pathway, besides disseminating knowledge-following the action plan of the 2030 Agenda of the United Nations Educational, Scientific and Cultural Organization (UNESCO)-, are an effective tool to promote resilience in response to prison health and care demands of people deprived of liberty.

Computational methods applied to syphilis: where are we, and where are we going?

Syphilis is an infectious disease that can be diagnosed and treated cheaply. Despite being a curable condition, the syphilis rate is increasing worldwide. In this sense, computational methods can analyze data and assist managers in formulating new public policies for preventing and controlling sexually transmitted infections (STIs). Computational techniques can integrate knowledge from experiences and, through an inference mechanism, apply conditions to a database that seeks to explain data behavior. This systematic review analyzed studies that use computational methods to establish or improve syphilis-related aspects. Our review shows the usefulness of computational tools to promote the overall understanding of syphilis, a global problem, to guide public policy and practice, to target better public health interventions such as surveillance and prevention, health service delivery, and the optimal use of diagnostic tools. The review was conducted according to PRISMA 2020 Statement and used several quality criteria to include studies. The publications chosen to compose this review were gathered from Science Direct, Web of Science, Springer, Scopus, ACM Digital Library, and PubMed databases. Then, studies published between 2015 and 2022 were selected. The review identified 1,991 studies. After applying inclusion, exclusion, and study quality assessment criteria, 26 primary studies were included in the final analysis. The results show different computational approaches, including countless Machine Learning algorithmic models, and three sub-areas of application in the context of syphilis: surveillance (61.54%), diagnosis (34.62%), and health policy evaluation (3.85%). These computational approaches are promising and capable of being tools to support syphilis control and surveillance actions.

Digital health solution for monitoring and surveillance of Amyotrophic Lateral Sclerosis in Brazil.

Amyotrophic Lateral Sclerosis (ALS) is a complex and rare neurodegenerative disease given its heterogeneity. Despite being known for many years, few countries have accurate information about the characteristics of people diagnosed with ALS, such as data regarding diagnosis and clinical features of the disease. In Brazil, the lack of information about ALS limits data for the research progress and public policy development that benefits people affected by this health condition. In this context, this article aims to show a digital health solution development and application for research, intervention, and strengthening of the response to ALS in the Brazilian Health System. The proposed solution is composed of two platforms: the Brazilian National ALS Registry, responsible for the data collection in a structured way from ALS patients all over Brazil; and the Brazilian National ALS Observatory, responsible for processing the data collected in the National Registry and for providing a monitoring room with indicators on people diagnosed with ALS in Brazil. The development of this solution was supported by the Brazilian Ministry of Health (MoH) and was carried out by a multidisciplinary team with expertise in ALS. This solution represents a tool with great potential for strengthening public policies and stands out for being the only public database on the disease, besides containing innovations that allow data collection by health professionals and/or patients. By using both platforms, it is believed that it will be possible to understand the demographic and epidemiological data of ALS in Brazil, since the data will be able to be analyzed by care teams and also by public health managers, both in the individual and collective monitoring of people living with ALS in Brazil.

Digital Alternative Communication for Individuals with Amyotrophic Lateral Sclerosis: What We Have.

Amyotrophic Lateral Sclerosis is a disease that compromises the motor system and the functional abilities of the person in an irreversible way, causing the progressive loss of the ability to communicate. Tools based on Augmentative and Alternative Communication are essential for promoting autonomy and improving communication, life quality, and survival. This Systematic Literature Review aimed to provide evidence on eye-image-based Human-Computer Interaction approaches for the Augmentative and Alternative Communication of people with Amyotrophic Lateral Sclerosis. The Systematic Literature Review was conducted and guided following a protocol consisting of search questions, inclusion and exclusion criteria, and quality assessment, to select primary studies published between 2010 and 2021 in six repositories: Science Direct, Web of Science, Springer, IEEE Xplore, ACM Digital Library, and PubMed. After the screening, 25 primary studies were evaluated. These studies showcased four low-cost, non-invasive Human-Computer Interaction strategies employed for Augmentative and Alternative Communication in people with Amyotrophic Lateral Sclerosis. The strategies included Eye-Gaze, which featured in 36% of the studies; Eye-Blink and Eye-Tracking, each accounting for 28% of the approaches; and the Hybrid strategy, employed in 8% of the studies. For these approaches, several computational techniques were identified. For a better understanding, a workflow containing the development phases and the respective methods used by each strategy was generated. The results indicate the possibility and feasibility of developing Human-Computer Interaction resources based on eye images for Augmentative and Alternative Communication in a control group. The absence of experimental testing in people with Amyotrophic Lateral Sclerosis reiterates the challenges related to the scalability, efficiency, and usability of these technologies for people with the disease. Although challenges still exist, the findings represent important advances in the fields of health sciences and technology, promoting a promising future with possibilities for better life quality.

Osteoporosis screening using machine learning and electromagnetic waves.

Osteoporosis is a disease characterized by impairment of bone microarchitecture that causes high socioeconomic impacts in the world because of fractures and hospitalizations. Although dual-energy X-ray absorptiometry (DXA) is the gold standard for diagnosing the disease, access to DXA in developing countries is still limited due to its high cost, being present only in specialized hospitals. In this paper, we analyze the performance of Osseus, a low-cost portable device based on electromagnetic waves that measures the attenuation of the signal that crosses the medial phalanx of a patient's middle finger and was developed for osteoporosis screening. The analysis is carried out by predicting changes in bone mineral density using Osseus measurements and additional common risk factors used as input features to a set of supervised classification models, while the results from DXA are taken as target (real) values during the training of the machine learning algorithms. The dataset consisted of 505 patients who underwent osteoporosis screening with both devices (DXA and Osseus), of whom 21.8% were healthy and 78.2% had low bone mineral density or osteoporosis. A cross-validation with k-fold = 5 was considered in model training, while 20% of the whole dataset was used for testing. The obtained performance of the best model (Random Forest) presented a sensitivity of 0.853, a specificity of 0.879, and an F1 of 0.859. Since the Random Forest (RF) algorithm allows some interpretability of its results (through the impurity check), we were able to identify the most important variables in the classification of osteoporosis. The results showed that the most important variables were age, body mass index, and the signal attenuation provided by Osseus. The RF model, when used together with Osseus measurements, is effective in screening patients and facilitates the early diagnosis of osteoporosis. The main advantages of such early screening are the reduction of costs associated with exams, surgeries, treatments, and hospitalizations, as well as improved quality of life for patients.

Development of a Cyclic Voltammetry-Based Method for the Detection of Antigens and Antibodies as a Novel Strategy for Syphilis Diagnosis.

The improvement of laboratory diagnosis is a critical step for the reduction of syphilis cases around the world. In this paper, we present the development of an impedance-based method for detecting T. pallidum antigens and antibodies as an auxiliary tool for syphilis laboratory diagnosis. We evaluate the voltammetric signal obtained after incubation in carbon or gold nanoparticle-modified carbon electrodes in the presence or absence of Poly-L-Lysine. Our results indicate that the signal obtained from the electrodes was sufficient to distinguish between infected and non-infected samples immediately (T0') or 15 min (T15') after incubation, indicating its potential use as a point-of-care method as a screening strategy.

Effectiveness of COVID-19 Vaccination on Reduction of Hospitalizations and Deaths in Elderly Patients in Rio Grande do Norte, Brazil.

Since the COVID-19 pandemic emerged, vaccination has been the core strategy to mitigate the spread of SARS-CoV-2 in humans. This paper analyzes the impact of COVID-19 vaccination on hospitalizations and deaths in the state of Rio Grande do Norte, Brazil. We analyzed data from 23,516 hospitalized COVID-19 patients diagnosed between April 2020 and August 2021. We excluded the data from patients hospitalized through direct occupancy, unknown outcomes, and unconfirmed COVID-19 cases, resulting in data from 12,635 patients cross-referenced with the immunization status during hospitalization. Our results indicated that administering at least one dose of the immunizers was sufficient to significantly reduce the occurrence of moderate and severe COVID-19 cases among patients under 59 years. Considering the partially or fully immunized patients, the mean age is similar between the analyzed groups, despite the occurrence of comorbidities and higher than that observed among not immunized patients. Thus, immunized patients present lower Unified Score for Prioritization (USP) levels when diagnosed with COVID-19. Our data suggest that COVID-19 vaccination significantly reduced the hospitalization and death of elderly patients (60+ years) after administration of at least one dose. Comorbidities do not change the mean age of moderate/severe COVID-19 cases and the days required for the hospitalization of these patients.

Electronic health records in Brazil: Prospects and technological challenges.

Electronic Health Records (EHR) are critical tools for advancing digital health worldwide. In Brazil, EHR development must follow specific standards, laws, and guidelines that contribute to implementing beneficial resources for population health monitoring. This paper presents an audit of the main approaches used for EHR development in Brazil, thus highlighting prospects, challenges, and existing gaps in the field. We applied a systematic review protocol to search for articles published from 2011 to 2021 in seven databases (Science Direct, Web of Science, PubMed, Springer, IEEE Xplore, ACM Digital Library, and SciELO). Subsequently, we analyzed 14 articles that met the inclusion and quality criteria and answered our research questions. According to this analysis, 78.58% (11) of the articles state that interoperability between systems is essential for improving patient care. Moreover, many resources are being designed and deployed to achieve this communication between EHRs and other healthcare systems in the Brazilian landscape. Besides interoperability, the articles report other considerable elements: (i) the need for increased security with the deployment of permission resources for viewing patient data, (ii) the absence of accurate data for testing EHRs, and (iii) the relevance of defining a methodology for EHR development. Our review provides an overview of EHR development in Brazil and discusses current gaps, innovative approaches, and technological solutions that could potentially address the related challenges. Lastly, our study also addresses primary elements that could contribute to relevant components of EHR development in the context of Brazil's public health system. Systematic review registration: PROSPERO, identifier CRD42021233219, https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021233219.

Massive health education through technological mediation: Analyses and impacts on the syphilis epidemic in Brazil.

With syphilis cases on the rise, Brazil declared an epidemic in 2016. To address the consequent public health crisis, the Ministry of Health laid out a rapid response plan, namely, the "Syphilis No!" Project (SNP), a national instrument to fight the disease which encompasses four dimensions: (a) management and governance, (b) surveillance, (c) comprehensive care, and (d) strengthening of educommunication. In the dimension of education, the SNP developed the learning pathway "Syphilis and other Sexually Transmitted Infections (STIs)" to strengthen and promote Health Education. This pathway features 54 Massive Open Online Courses (MOOCs), delivered through the Virtual Learning Environment of the Brazilian Health System (AVASUS). This paper analyzes the impacts of the learning pathway "Syphilis and other STIs" on the response to the epidemic in Brazil, highlighting the educational process of the learning pathway and its social implications from the perspective of the United Nations' 2030 Agenda and its Sustainable Development Goals. Three distinct databases were used to organize the educational data: the learning pathway "Syphilis and other STIs" from AVASUS, the National Registry of HealthCare Facilities from the Brazilian Ministry of Health (MoH), and the Brazilian Occupation Classification, from the Ministry of Labor. The analysis provides a comprehensive description of the 54 courses of the learning pathway, which has 177,732 enrollments and 93,617 participants from all Brazilian regions, especially the Southeast, which accounts for the highest number of enrollees. Additionally, it is worth noting that students living abroad also enrolled in the courses. Data characterization provided a demographic study focused on the course participants' profession and level of care practiced, revealing that the majority (85%) worked in primary and secondary healthcare. These practitioners are the target audience of the learning pathway and, accordingly, are part of the personnel directly engaged in healthcare services that fight the syphilis epidemic in Brazil.

Syphilis response policies and their assessments: A scoping review.

Syphilis is one of the most common sexually transmitted infections (STIs) worldwide and has shown a rising trend in recent years, according to a report published by the World Health Organization (WHO) in 2021. Given this problem, the present study aims to develop a scoping review of what has been done in the world after the publication of the global strategy for the elimination of STIs, with a specific focus on syphilis. Thus, we searched for papers on health policies in response to syphilis in Pubmed, Scopus, ScienceDirect, and EBSCO by CINAHL, as well as in official documents from international health organizations. The period from January 1, 2016, to August 14, 2022 was considered. Our search returned 880 papers addressing "Syphilis," "Health Policy," and "Health Policies" combined. Twenty-three papers fulfilled the inclusion and exclusion criteria according to two research questions set out for this scoping review. Our findings suggest that Brazil and Peru presented the greatest adequacy of the strategies provided by WHO in 2016 and the Pan American Health Organization (PAHO) in 2017, aiming tothe goals set out in the UN's 2030 Agenda for sustainable development. Among the studies found, six countries (Cuba, Thailand, Belarus, Armenia, Moldova, and Puerto Rico) reported the elimination of mother-to-child transmission (MTCT) of syphilis, but the most recent data are from 2016. Furthermore, it is essential to mention that no country has been found that has presented a comprehensive response to syphilis, noting the control or elimination of the disease in all key populations. Thus, it is necessary to constantly monitor national policies based on in-depth studies on the quality of the response, the challenges, and the national, regional, and global perspectives for the control of the disease until 2030, the year in which the SDGs will be reviewed. Systematic review registration: https://osf.io/x9er5/?view_only=0cc0062222ec45dcb2f4d41484d285b6, identifier: 10.17605/OSF.IO/X9ER5.

The relevancy of massive health education in the Brazilian prison system: The course "health care for people deprived of freedom" and its impacts.

Introduction: Brazil has one of the largest prison populations globally, with over 682,000 imprisoned people. Prison health is a public health emergency as it presents increasingly aggravating disease rates, mainly sexually transmitted infections (STI). And this problem already affects both developed and developing nations. Therefore, when thinking about intervention strategies to improve this scenario in Brazil, the course "Health Care for People Deprived of Freedom" (ASPPL), aimed at prison health, was developed. This course was implemented in the Virtual Learning Environment of the Brazilian Health System (AVASUS). Given this context, this study analyzed the aspects associated with massive training through technological mediation and its impacts on prison health. Methods: This cross-sectional study analyzed data from 8,118 ASPPL course participants. The data analyzed were collected from six sources, namely: (i) AVASUS, (ii) National Registry of Health Care Facilities (CNES), (iii) Brazilian Occupational Classification (CBO), (iv) National Prison Department (DEPEN); (v) Brazilian Institute of Geography and Statistics (IBGE); and the (iv) Brazilian Ministry of Health (MoH), through the Outpatient Information System of the Brazilian National Health System (SIA/SUS). A data processing pipeline was conducted using Python 3.8.9. Results: The ASPPL course had 8,118 participants distributed across the five Brazilian regions. The analysis of course evaluation by participants who completed it shows that 5,190 (63.93%) reported a significant level of satisfaction (arithmetic mean = 4.9, median = 5, and standard deviation = 0.35). The analysis revealed that 3,272 participants (40.31%) are health workers operating in distinct levels of care. The prison system epidemiological data shows an increase in syphilis diagnosis in correctional facilities. Conclusions: The course enabled the development of a massive training model for various health professionals at all care levels and regions of Brazil. This is particularly important in a country with a continental size and a large health workforce like Brazil. As a result, social and prison health impacts were observed.

Translational research in health technologies: A scoping review.

Introduction: The current debate on the process of technological innovation points out as a challenge for universities consolidation of competencies that allow the generation and transfer of knowledge to society. The Translational Research (TR) approach has as one of its main objectives the acceleration of the innovation process, based on the transposition from basic science to applied science and innovation, which comprises the different stages of research, development and innovation. The literature points out that the dynamics of translation, which results in new technologies, are complex, transdisciplinary, inter-institutional, systemic, and non-linear. The main objective of this review is to contribute to the adoption of institutional strategies and the formulation of public policies aimed at solving today's social and economic challenges, ensuring access to technologies and sustainability for the health system. The specific objectives were: (i) to systematize studies that characterized translational research in medical devices; (ii) map the challenges for the implementation of translational health research; (iii) contribute to the design of institutional strategies; and (iv) support the formulation of public policies. Methods: This study used the scoping review technique, according to PRISMA-ScR and the Joanna Briggs Institute guidelines. Concerning the extraction of relevant articles, the journals indexed in Bireme, Pubmed, Scopus, Web of Science, and Google Scholar were consulted for selecting relevant articles. The search was carried out on November 28, 2021, updated on April 29, 2022, and there were no restrictions as to the year of publication, language or type of analysis. Studies that did not answer the research question were excluded, as they dealt exclusively with the pharmaceutical segment, the translation of knowledge into clinical practice, or addressed the process of translational research applied to specific diseases or technologies. Results: Thirty-three articles were included indicating that the approach of translation of research is multidisciplinary and transdisciplinary and encompasses knowledge and aspects that go beyond basic and applied research and incorporates final steps concerning regulatory aspects, clinical research, market analysis, technology transfer, production and incorporation of technologies into the health system.

Virtual Learning Environment of the Brazilian Health System (AVASUS): Efficiency of Results, Impacts, and Contributions.

The Virtual Learning Environment of the Brazilian Health System (AVASUS) is a free and open distance education platform of the Ministry of Health (MS). AVASUS is a scalable virtual learning environment that has surpassed 800,000 users, 2 million enrollments, and 310 courses in its catalog. The objective of this paper was to assess the impacts of the educational offerings on health services and AVASUS course participants' professional practice. This study analyzed data from AVASUS, the Brazilian National Registry of Health Care Facilities (CNES), the Brazilian Occupational Classification (CBO), and a questionnaire applied to 720-course participants from five regions of Brazil. After acquiring and extracting data, computational methods were used for the evaluation process. Only the responses of 462 participants were considered for data analysis, as they had a formal link to CNES. The results showed that respondents recommended 76.2% of AVASUS courses to peers. Accordingly, the quality of educational offerings motivated 81.3% of such recommendations. In addition, 75.6% of course participants who answered the questionnaire also indicated that AVASUS course contents contribute to enhancing existing health services in the health facilities where they work. Finally, 24.6% of all responses mentioned that courses available in AVASUS were essential in offering new health services in such facilities.

Stochastic Petri net model describing the relationship between reported maternal and congenital syphilis cases in Brazil.

INTRODUCTION: Syphilis is a sexually transmitted disease (STD) caused by Treponema pallidum subspecies pallidum. In 2016, it was declared an epidemic in Brazil due to its high morbidity and mortality rates, mainly in cases of maternal syphilis (MS) and congenital syphilis (CS) with unfavorable outcomes. This paper aimed to mathematically describe the relationship between MS and CS cases reported in Brazil over the interval from 2010 to 2020, considering the likelihood of diagnosis and effective and timely maternal treatment during prenatal care, thus supporting the decision-making and coordination of syphilis response efforts. METHODS: The model used in this paper was based on stochastic Petri net (SPN) theory. Three different regressions, including linear, polynomial, and logistic regression, were used to obtain the weights of an SPN model. To validate the model, we ran 100 independent simulations for each probability of an untreated MS case leading to CS case (PUMLC) and performed a statistical t-test to reinforce the results reported herein. RESULTS: According to our analysis, the model for predicting congenital syphilis cases consistently achieved an average accuracy of 93% or more for all tested probabilities of an untreated MS case leading to CS case. CONCLUSIONS: The SPN approach proved to be suitable for explaining the Notifiable Diseases Information System (SINAN) dataset using the range of 75-95% for the probability of an untreated MS case leading to a CS case (PUMLC). In addition, the model's predictive power can help plan actions to fight against the disease.

miRNAs as biomarkers for early cancer detection and their application in the development of new diagnostic tools.

Over the last decades, microRNAs (miRNAs) have emerged as important molecules associated with the regulation of gene expression in humans and other organisms, expanding the strategies available to diagnose and handle several diseases. This paper presents a systematic review of literature of miRNAs related to cancer development and explores the main techniques used to quantify these molecules and their limitations as screening strategy. The bibliographic research was conducted using the online databases, PubMed, Google Scholar, Web of Science, and Science Direct searching the terms "microRNA detection", "miRNA detection", "miRNA and prostate cancer", "miRNA and cervical cancer", "miRNA and cervix cancer", "miRNA and breast cancer", and "miRNA and early cancer diagnosis". Along the systematic review over 26,000 published papers were reported, and 252 papers were returned after applying the inclusion and exclusion criteria, which were considered during this review. The aim of this study is to identify potential miRNAs related to cancer development that may be useful for early cancer diagnosis, notably in the breast, prostate, and cervical cancers. In addition, we suggest a preliminary top 20 miRNA panel according to their relevance during the respective cancer development. Considering the progressive number of new cancer cases every year worldwide, the development of new diagnostic tools is critical to refine the accuracy of screening tests, improving the life expectancy and allowing a better prognosis for the affected patients.

Machine learning applied to retinal image processing for glaucoma detection: review and perspective.

INTRODUCTION: This is a systematic review on the main algorithms using machine learning (ML) in retinal image processing for glaucoma diagnosis and detection. ML has proven to be a significant tool for the development of computer aided technology. Furthermore, secondary research has been widely conducted over the years for ophthalmologists. Such aspects indicate the importance of ML in the context of retinal image processing. METHODS: The publications that were chosen to compose this review were gathered from Scopus, PubMed, IEEEXplore and Science Direct databases. Then, the papers published between 2014 and 2019 were selected . Researches that used the segmented optic disc method were excluded. Moreover, only the methods which applied the classification process were considered. The systematic analysis was performed in such studies and, thereupon, the results were summarized. DISCUSSION: Based on architectures used for ML in retinal image processing, some studies applied feature extraction and dimensionality reduction to detect and isolate important parts of the analyzed image. Differently, other works utilized a deep convolutional network. Based on the evaluated researches, the main difference between the architectures is the number of images demanded for processing and the high computational cost required to use deep learning techniques. CONCLUSIONS: All the analyzed publications indicated it was possible to develop an automated system for glaucoma diagnosis. The disease severity and its high occurrence rates justify the researches which have been carried out. Recent computational techniques, such as deep learning, have shown to be promising technologies in fundus imaging. Although such a technique requires an extensive database and high computational costs, the studies show that the data augmentation and transfer learning techniques have been applied as an alternative way to optimize and reduce networks training.

A fuzzy model for processing and monitoring vital signs in ICU patients.

BACKGROUND: The area of the hospital automation has been the subject of much research, addressing relevant issues which can be automated, such as: management and control (electronic medical records, scheduling appointments, hospitalization, among others); communication (tracking patients, staff and materials), development of medical, hospital and laboratory equipment; monitoring (patients, staff and materials); and aid to medical diagnosis (according to each speciality). METHODS: In this context, this paper presents a Fuzzy model for helping medical diagnosis of Intensive Care Unit (ICU) patients and their vital signs monitored through a multiparameter heart screen. Intelligent systems techniques were used in the data acquisition and processing (sorting, transforming, among others) it into useful information, conducting pre-diagnosis and providing, when necessary, alert signs to the medical staff. CONCLUSIONS: The use of fuzzy logic turned to the medical area can be very useful if seen as a tool to assist specialists in this area. This paper presented a fuzzy model able to monitor and classify the condition of the vital signs of hospitalized patients, sending alerts according to the pre-diagnosis done helping the medical diagnosis.