Artificial intelligence on the identification of risk groups for osteoporosis, a general review
Abstract 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...
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| Format: | Article |
| Language: | English |
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BMC
2018-01-01
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| Series: | BioMedical Engineering OnLine |
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| Online Access: | http://link.springer.com/article/10.1186/s12938-018-0436-1 |
| _version_ | 1818067792512942080 |
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| author | Agnaldo S. Cruz Hertz C. Lins Ricardo V. A. Medeiros José M. F. Filho Sandro G. da Silva |
| author_facet | Agnaldo S. Cruz Hertz C. Lins Ricardo V. A. Medeiros José M. F. Filho Sandro G. da Silva |
| author_sort | Agnaldo S. Cruz |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-12-10T15:29:19Z |
| format | Article |
| id | doaj.art-be9e44a922594f8c8fa1c4fa98faec07 |
| institution | Directory Open Access Journal |
| issn | 1475-925X |
| language | English |
| last_indexed | 2024-12-10T15:29:19Z |
| publishDate | 2018-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BioMedical Engineering OnLine |
| spelling | doaj.art-be9e44a922594f8c8fa1c4fa98faec072022-12-22T01:43:26ZengBMCBioMedical Engineering OnLine1475-925X2018-01-0117111710.1186/s12938-018-0436-1Artificial intelligence on the identification of risk groups for osteoporosis, a general reviewAgnaldo S. Cruz0Hertz C. Lins1Ricardo V. A. Medeiros2José M. F. Filho3Sandro G. da Silva4Centro de Tecnologia, Universidade Federal do Rio Grande do Norte UFRNLaboratory of Technological Innovation in Healthcare, Federal University of Rio Grande do Norte (UFRN)Laboratory of Technological Innovation in Healthcare, Federal University of Rio Grande do Norte (UFRN)Laboratory of Technological Innovation in Healthcare, Federal University of Rio Grande do Norte (UFRN)Centro de Tecnologia, Universidade Federal do Rio Grande do Norte UFRNAbstract 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.http://link.springer.com/article/10.1186/s12938-018-0436-1Artificial intelligenceOsteoporosisFractureNeural networkComputer-aided detection system |
| spellingShingle | Agnaldo S. Cruz Hertz C. Lins Ricardo V. A. Medeiros José M. F. Filho Sandro G. da Silva Artificial intelligence on the identification of risk groups for osteoporosis, a general review BioMedical Engineering OnLine Artificial intelligence Osteoporosis Fracture Neural network Computer-aided detection system |
| title | Artificial intelligence on the identification of risk groups for osteoporosis, a general review |
| title_full | Artificial intelligence on the identification of risk groups for osteoporosis, a general review |
| title_fullStr | Artificial intelligence on the identification of risk groups for osteoporosis, a general review |
| title_full_unstemmed | Artificial intelligence on the identification of risk groups for osteoporosis, a general review |
| title_short | Artificial intelligence on the identification of risk groups for osteoporosis, a general review |
| title_sort | artificial intelligence on the identification of risk groups for osteoporosis a general review |
| topic | Artificial intelligence Osteoporosis Fracture Neural network Computer-aided detection system |
| url | http://link.springer.com/article/10.1186/s12938-018-0436-1 |
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