Artifcial intelligence on the identifcation of risk groups for osteoporosis: a general review

• Autor(a) principal: Cruz, Agnaldo Souza
• Data de Publicação: 2018
• Outros Autores: Lins, Hertz Wilton de Castro, Valentim, Ricardo Alexsandro de Medeiros, Filho, José M. F., Silva, Sandro G. da
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UFRN
• Texto Completo: https://repositorio.ufrn.br/jspui/handle/123456789/29292
• topic: Artifcial intelligence; Osteoporosis; Fracture; Neural network; Computeraided detection system

Introduction: The goal of this paper is to present a critical review on the main systems that use artifcial intelligence to identify groups at risk for osteoporosis or fractures. The systems considered for this study were those that fulflled the following requirements: range of coverage in diagnosis, low cost and capability to identify more signifcant 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 identifed that there are several methods in the use of artifcial intelligence to help the screening of risk groups of osteoporosis or fractures. However, such systems were limited to a specifc ethnic group, gender or age. For future research, new challenges are presented Conclusions: It is necessary to develop research with the unifcation of diferent databases and grouping of the various attributes and clinical factors, in order to reach a greater comprehensiveness in the identifcation of risk groups of osteoporosis. For this purpose, the use of any predictive tool should be performed in diferent 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 unifcation, developing evidence-based standards for the evaluation of the most signifcant risk factors.