CADe and CADx systems for the detection and diagnosis of lung cancer have been important areas of research in recent decades. However, these areas are being worked on separately. CADe systems do not present the radiological characteristics of tumors, and CADx systems do not detect nodules and do not have good levels of automation. As a result, these systems are not yet widely used in clinical settings. The purpose of this article is to develop a new system for detection and diagnosis of pulmonary nodules on CT images, grouping them into a single system for the identification and characterization of the nodules to improve the level of automation. The article also presents as contributions: the use of Watershed and Histogram of oriented Gradients (HOG) techniques for distinguishing the possible nodules from other structures and feature extraction for pulmonary nodules, respectively. For the diagnosis, it is based on the likelihood of malignancy allowing more aid in the decision making by the radiologists. A rule-based classifier and Support Vector Machine (SVM) have been used to eliminate false positives. The database used in this research consisted of 420 cases obtained randomly from LIDC-IDRI. The segmentation method achieved an accuracy of 97 % and the detection system showed a sensitivity of 94.4 % with 7.04 false positives per case. Different types of nodules (isolated, juxtapleural, juxtavascular and ground-glass) with diameters between 3 mm and 30 mm have been detected. For the diagnosis of malignancy our system presented ROC curves with areas of: 0.91 for nodules highly unlikely of being malignant, 0.80 for nodules moderately unlikely of being malignant, 0.72 for nodules with indeterminate malignancy, 0.67 for nodules moderately suspicious of being malignant and 0.83 for nodules highly suspicious of being malignant. From our preliminary results, we believe that our system is promising for clinical applications assisting radiologists in the detection and diagnosis of lung cancer.

The goal of this paper is to present a critical review of major Computer-Aided Detection systems (CADe) for lung cancer in order to identify challenges for future research. CADe systems must meet the following requirements: improve the performance of radiologists providing high sensitivity in the diagnosis, a low number of false positives (FP), have high processing speed, present high level of automation, low cost (of implementation, training, support and maintenance), the ability to detect different types and shapes of nodules, and software security assurance. The relevant literature related to “CADe for lung cancer” was obtained from PubMed, IEEEXplore and Science Direct database. Articles published from 2009 to 2013, and some articles previously published, were used. A systemic analysis was made on these articles and the results were summarized. Based on literature search, it was observed that many if not all systems described in this survey have the potential to be important in clinical practice. However, no significant improvement was observed in sensitivity, number of false positives, level of automation and ability to detect different types and shapes of nodules in the studied period. Challenges were presented for future research. Further research is needed to improve existing systems and propose new solutions. For this, we believe that collaborative efforts through the creation of open source software communities are necessary to develop a CADe system with all the requirements mentioned and with a short development cycle. In addition, future CADe systems should improve the level of automation, through integration with picture archiving and communication systems (PACS) and the electronic record of the patient, decrease the number of false positives, measure the evolution of tumors, evaluate the evolution of the oncological treatment, and its possible prognosis.

The goal of this paper is to present a critical review on the main systems that use artificial intelligence to identify groups at risk for osteoporosis or fractures. The systems considered for this study were those that fulfilled the following requirements: range of coverage in diagnosis, low cost and capability to identify more significant somatic factors. A bibliographic research was done in the databases, PubMed, IEEExplorer Latin American and Caribbean Center on Health Sciences Information (LILACS), Medical Literature Analysis and Retrieval System Online (MEDLINE), Cumulative Index to Nursing and Allied Health Literature (CINAHL), Scopus, Web of Science, and Science Direct searching the terms “Neural Network”, “Osteoporosis Machine Learning” and “Osteoporosis Neural Network”. Studies with titles not directly related to the research topic and older data that reported repeated strategies were excluded. The search was carried out with the descriptors in German, Spanish, French, Italian, Mandarin, Portuguese and English; but only studies written in English were found to meet the established criteria. Articles covering the period 2000–2017 were selected; however, articles prior to this period with great relevance were included in this study. Based on the collected research, it was identified that there are several methods in the use of artificial intelligence to help the screening of risk groups of osteoporosis or fractures. However, such systems were limited to a specific ethnic group, gender or age. For future research, new challenges are presented. It is necessary to develop research with the unification of different databases and grouping of the various attributes and clinical factors, in order to reach a greater comprehensiveness in the identification of risk groups of osteoporosis. For this purpose, the use of any predictive tool should be performed in different populations with greater participation of male patients and inclusion of a larger age range for the ones involved. The biggest challenge is to deal with all the data complexity generated by this unification, developing evidence-based standards for the evaluation of the most significant risk factors.

Introduction: The use of machine learning (ML) techniques in healthcare encompasses an emerging concept that envisages vast contributions to the tackling of rare diseases. In this scenario, amyotrophic lateral sclerosis (ALS) involves complexities that are yet not demystified. In ALS, the biomedical signals present themselves as potential biomarkers that, when used in tandem with smart algorithms, can be useful to applications within the context of the disease.Methods: This Systematic Literature Review (SLR) consists of searching for and investigating primary studies that use ML techniques and biomedical signals related to ALS. Following the definition and execution of the SLR protocol, 18 articles met the inclusion, exclusion, and quality assessment criteria, and answered the SLR research questions.Discussions: Based on the results, we identified three classes of ML applications combined with biomedical signals in the context of ALS: diagnosis (72.22%), communication (22.22%), and survival prediction (5.56%).Conclusions: Distinct algorithmic models and biomedical signals have been reported and present promising approaches, regardless of their classes. In summary, this SLR provides an overview of the primary studies analyzed as well as directions for the construction and evolution of technology-based research within the scope of ALS.

Resumo É inegável o papel dos avanços tecnológicos para o fortalecimento da saúde. No tocante às tecnologias digitais, trata do uso crescente dos sistemas de informação e análise de dados em saúde nas ações de preparo, vigilância e resposta a surtos epidemiológicos, tema abordado neste artigo no contexto da pandemia provocada pelo vírus Sars-CoV-2 no estado do Rio Grande do Norte. Este estudo parte do pressuposto de que é possível potencializar a gestão da resposta à Covid-19 por meio da saúde digital. Assim, a pesquisa desenvolveu um Ecossistema tecnológico que integra diferentes sistemas de informação para atender as necessidades previstas nas normativas internacionais frente à pandemia. Este artigo descreve, além do Ecossistema e sua estrutura, um conjunto de análises sobre a aplicação desse dispositivo por diversos atores institucionais. O Ecossistema foi a principal ferramenta em uso no estado para o processo decisório em resposta à Covid-19, sendo um modelo para a intervenção de saúde digital no Sistema Único de Saúde. A experiência do Rio Grande do Norte reúne, portanto, elementos que contribuem para os estudos sobre resiliência de sistemas e análises de políticas públicas em saúde em situações de emergência.

To fight against the rising incidence of syphilis, the Brazilian Ministry of Health (MoH) launched the “Syphilis No!” Project (SNP), with specific resources funded by a parliamentary amendment. Then, in 2018, a national rapid response started to be implemented on the Brazilian Unified Health System (SUS, Sistema Único de Saúde) in two strategic lines (1) to reinforce SUS's universal actions and (2) to implement specific ones to 100 municipalities chosen by the MoH as priorities for syphilis congenital response. In 2015, such localities represented 6895% of congenital syphilis cases in Brazil. In this context, SNP has implemented actions to strengthen epidemiological surveillance of acquired syphilis and congenital syphilis by instituting an integrated and collaborative response through health services networks and reinforcing interstate relations. A quasi-experimental study using time series analysis was conducted to assess immediate impacts and changes to the trend in national congenital syphilis before and after the project, from September 2016 to December 2019. Data were assessed considering rates of congenital syphilis per 1,000 live births in all priority municipalities (n=100) covered by the project and in non-priority municipalities (n=5,470) from all five macro-regions of Brazil. Priority municipalities showed a greater reduction (change in trend) in comparison to non-priority. The linear regression model revealed trend changes after the intervention, with both groups of municipalities showing a drop in the average monthly number of cases per 1,000 live births, with a reduction of -0·21 (CI 95% -0·33 to -0·09; p=0·0011) in priority municipalities and of -0·10 (CI 95% -0.19 to -0.02; p=0·0216) in non-priority municipalities. The study using ITS provides important evidence on the direction, timing, and magnitude of the effects of interventions introduced as part of the SNP on congenital syphilis in Brazil. Our results suggest that the Syphilis No! Project influenced the trends of congenital syphilis in Brazil from 2018, with higher reductions achieved in the priority municipalities. The research is funded by a grant to the Syphilis No! Project from Brazilian Ministry of Health (Project Number: 54/2017). The funders had no role in study design, analysis, decision to publish, or preparation of the manuscript.