The new era of artificial intelligence in neuroradiology: current research and promising tools
Radiology has a number of characteristics that make it an especially suitable medical discipline for early artificial intelligence (AI) adoption. These include having a well-established digital workflow, standardized protocols for image storage, and numerous well-defined interpretive activities. The...
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| Format: | Article |
| Language: | English |
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Thieme Revinter Publicações
2024-06-01
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| Series: | Arquivos de Neuro-Psiquiatria |
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| Online Access: | http://www.thieme-connect.de/DOI/DOI?10.1055/s-0044-1779486 |
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| author | Fabíola Bezerra de Carvalho Macruz Ana Luiza Mandetta Pettengil Dias Celi Santos Andrade Mariana Penteado Nucci Carolina de Medeiros Rimkus Leandro Tavares Lucato Antônio José da Rocha Felipe Campos Kitamura |
| author_facet | Fabíola Bezerra de Carvalho Macruz Ana Luiza Mandetta Pettengil Dias Celi Santos Andrade Mariana Penteado Nucci Carolina de Medeiros Rimkus Leandro Tavares Lucato Antônio José da Rocha Felipe Campos Kitamura |
| author_sort | Fabíola Bezerra de Carvalho Macruz |
| collection | DOAJ |
| description | Radiology has a number of characteristics that make it an especially suitable medical discipline for early artificial intelligence (AI) adoption. These include having a well-established digital workflow, standardized protocols for image storage, and numerous well-defined interpretive activities. The more than 200 commercial radiologic AI-based products recently approved by the Food and Drug Administration (FDA) to assist radiologists in a number of narrow image-analysis tasks such as image enhancement, workflow triage, and quantification, corroborate this observation. However, in order to leverage AI to boost efficacy and efficiency, and to overcome substantial obstacles to widespread successful clinical use of these products, radiologists should become familiarized with the emerging applications in their particular areas of expertise. In light of this, in this article we survey the existing literature on the application of AI-based techniques in neuroradiology, focusing on conditions such as vascular diseases, epilepsy, and demyelinating and neurodegenerative conditions. We also introduce some of the algorithms behind the applications, briefly discuss a few of the challenges of generalization in the use of AI models in neuroradiology, and skate over the most relevant commercially available solutions adopted in clinical practice. If well designed, AI algorithms have the potential to radically improve radiology, strengthening image analysis, enhancing the value of quantitative imaging techniques, and mitigating diagnostic errors. |
| format | Article |
| id | doaj-art-9d9aa526fb4f4a4ab848e15f83a513bb |
| institution | Kabale University |
| issn | 0004-282X 1678-4227 |
| language | English |
| publishDate | 2024-06-01 |
| publisher | Thieme Revinter Publicações |
| record_format | Article |
| series | Arquivos de Neuro-Psiquiatria |
| spelling | doaj-art-9d9aa526fb4f4a4ab848e15f83a513bb2025-08-20T03:38:19ZengThieme Revinter PublicaçõesArquivos de Neuro-Psiquiatria0004-282X1678-42272024-06-01820600101210.1055/s-0044-1779486The new era of artificial intelligence in neuroradiology: current research and promising toolsFabíola Bezerra de Carvalho Macruz0https://orcid.org/0000-0001-6009-7631Ana Luiza Mandetta Pettengil Dias1https://orcid.org/0000-0001-6716-8340Celi Santos Andrade2https://orcid.org/0000-0003-0382-3232Mariana Penteado Nucci3https://orcid.org/0000-0002-1502-9215Carolina de Medeiros Rimkus4https://orcid.org/0000-0002-3866-1299Leandro Tavares Lucato5https://orcid.org/0000-0001-9181-5245Antônio José da Rocha6https://orcid.org/0000-0003-2591-9171Felipe Campos Kitamura7https://orcid.org/0000-0002-9992-5630Universidade de São Paulo, Hospital das Clínicas, Departamento de Radiologia e Oncologia, Seção de Neurorradiologia, Faculdade de Medicina, São Paulo SP, Brazil.Diagnósticos da América SA, São Paulo SP, Brazil.Centro de Diagnósticos Brasil, Alliança Group, São Paulo SP, Brazil.Universidade de São Paulo, Laboratório de Investigação Médica em Ressonância Magnética (LIM 44), São Paulo SP, Brazil.Universidade de São Paulo, Hospital das Clínicas, Departamento de Radiologia e Oncologia, Seção de Neurorradiologia, Faculdade de Medicina, São Paulo SP, Brazil.Universidade de São Paulo, Hospital das Clínicas, Departamento de Radiologia e Oncologia, Seção de Neurorradiologia, Faculdade de Medicina, São Paulo SP, Brazil.Diagnósticos da América SA, São Paulo SP, Brazil.Diagnósticos da América SA, São Paulo SP, Brazil.Radiology has a number of characteristics that make it an especially suitable medical discipline for early artificial intelligence (AI) adoption. These include having a well-established digital workflow, standardized protocols for image storage, and numerous well-defined interpretive activities. The more than 200 commercial radiologic AI-based products recently approved by the Food and Drug Administration (FDA) to assist radiologists in a number of narrow image-analysis tasks such as image enhancement, workflow triage, and quantification, corroborate this observation. However, in order to leverage AI to boost efficacy and efficiency, and to overcome substantial obstacles to widespread successful clinical use of these products, radiologists should become familiarized with the emerging applications in their particular areas of expertise. In light of this, in this article we survey the existing literature on the application of AI-based techniques in neuroradiology, focusing on conditions such as vascular diseases, epilepsy, and demyelinating and neurodegenerative conditions. We also introduce some of the algorithms behind the applications, briefly discuss a few of the challenges of generalization in the use of AI models in neuroradiology, and skate over the most relevant commercially available solutions adopted in clinical practice. If well designed, AI algorithms have the potential to radically improve radiology, strengthening image analysis, enhancing the value of quantitative imaging techniques, and mitigating diagnostic errors.http://www.thieme-connect.de/DOI/DOI?10.1055/s-0044-1779486Artificial IntelligenceDeep LearningMachine LearningNeuroradiologyInteligência ArtificialAprendizado ProfundoAprendizado de MáquinaNeurorradiologia |
| spellingShingle | Fabíola Bezerra de Carvalho Macruz Ana Luiza Mandetta Pettengil Dias Celi Santos Andrade Mariana Penteado Nucci Carolina de Medeiros Rimkus Leandro Tavares Lucato Antônio José da Rocha Felipe Campos Kitamura The new era of artificial intelligence in neuroradiology: current research and promising tools Arquivos de Neuro-Psiquiatria Artificial Intelligence Deep Learning Machine Learning Neuroradiology Inteligência Artificial Aprendizado Profundo Aprendizado de Máquina Neurorradiologia |
| title | The new era of artificial intelligence in neuroradiology: current research and promising tools |
| title_full | The new era of artificial intelligence in neuroradiology: current research and promising tools |
| title_fullStr | The new era of artificial intelligence in neuroradiology: current research and promising tools |
| title_full_unstemmed | The new era of artificial intelligence in neuroradiology: current research and promising tools |
| title_short | The new era of artificial intelligence in neuroradiology: current research and promising tools |
| title_sort | new era of artificial intelligence in neuroradiology current research and promising tools |
| topic | Artificial Intelligence Deep Learning Machine Learning Neuroradiology Inteligência Artificial Aprendizado Profundo Aprendizado de Máquina Neurorradiologia |
| url | http://www.thieme-connect.de/DOI/DOI?10.1055/s-0044-1779486 |
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