RESUMO
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.
RESUMO
Objetivo: discutir se replicar o Projeto "Sífilis Não", naquilo que se refere ao tratamento de vastas quantidades de dados pessoais relativos à saúde sem o consentimento dos titulares, seria possível em solo português. Metodologia: análise da legislação referente à proteção de dados pessoais brasileira, portuguesa e europeia, tendo o Projeto "Sífilis Não" como o caso em estudo. Resultados: o tratamento de dados pessoais sensíveis sem o consentimento do titular é, em regra, proibido pelo Regulamento Geral de Proteção de Dados, entretanto, o tratamento por motivo de interesse público na área da saúde e para fins de pesquisa científica é autorizado, desde que sejam garantidas as liberdades fundamentais dos titulares. Conclusão: tendo em vista que o Projeto "Sífilis Não" é um projeto de pesquisa que envolve o enfrentamento e erradicação da sífilis em todas as suas formas, hipóteses específicas da legislação portuguesa e europeia autorizam o tratamento de dados pessoais sensíveis mesmo sem o consentimento dos titulares, notadamente o tratamento de dados pessoais por motivos de interesse público no domínio da saúde pública e para fins de investigação científica.
Objective: to discuss whether the reproduction of the "No Syphilis" Project regarding the processing of large scale of personal data related to health without the consent of the holders would be possible in Portugal. Methods: analysis of the Brazilian, Portuguese, and European legislation on personal data protection, with the "No Syphilis" Project as a case study. Results: the processing of sensitive personal data without the consent of the owner is, as a rule, prohibited by the General Data Protection Regulation, however the processing for reasons of public interest in the field of health and scientific research purposes is authorized, provided that the fundamental freedoms of the holders are guaranteed. Conclusion: considering that the "No Syphilis" Project is a research project that involves addressing and eradicating syphilis in all its forms, specific hypotheses of Portuguese and European legislation authorize the processing of sensitive personal data even without the consent of the holders, specifically, the processing of personal data for reasons of public interest in the field of public health and scientific research purposes.
Objetivo: discutir si la reproducción del Proyecto "Sífilis No", con respecto al procesamiento de grandes cantidades de datos personales relacionados con la salud sin el consentimiento de los titulares, sería posible en el terreno portugués. Metodología: análisis de la legislación sobre protección de datos personales brasileños, portugueses y europeos, con el Proyecto "Sífilis No" como caso en estudio. Resultados: el tratamiento de datos personales sensibles sin el consentimiento del titular está, por regla general, prohibido por el Reglamento General de Protección de Datos, sin embargo, el tratamiento por razones de interés público en el ámbito de la salud y con fines de investigación científica está autorizado, siempre que se garanticen las libertades fundamentales de los titulares. Conclusión: considerando que el Proyecto "Sífilis No" es un proyecto de investigación que implica hacer frente y erradicar la sífilis en todas sus formas, hipótesis específicas de la legislación portuguesa y europea autorizan el procesamiento de datos personales sensibles incluso sin el consentimiento de los titulares, en concreto, el tratamiento de datos personales por razones de interés público en el ámbito de la salud pública y con fines de investigación científica.
RESUMO
INTRODUCTION: 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. METHODS: 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. DISCUSSION: 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. CONCLUSIONS: 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.
