Radiomics and Artificial Intelligence for the Diagnosis and Monitoring of Alzheimer’s Disease: A Systematic Review of Studies in the Field
The use of radiomics and artificial intelligence applied for the diagnosis and monitoring of Alzheimer’s disease has developed in recent years. However, this approach is not yet completely applicable in clinical practice. The aim of this paper is to provide a systematic analysis of the studies that...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Journal of Clinical Medicine |
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| Online Access: | https://www.mdpi.com/2077-0383/12/16/5432 |
| _version_ | 1797584306430803968 |
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| author | Roberta Bevilacqua Federico Barbarossa Lorenzo Fantechi Daniela Fornarelli Enrico Paci Silvia Bolognini Cinzia Giammarchi Fabrizia Lattanzio Lucia Paciaroni Giovanni Renato Riccardi Giuseppe Pelliccioni Leonardo Biscetti Elvira Maranesi |
| author_facet | Roberta Bevilacqua Federico Barbarossa Lorenzo Fantechi Daniela Fornarelli Enrico Paci Silvia Bolognini Cinzia Giammarchi Fabrizia Lattanzio Lucia Paciaroni Giovanni Renato Riccardi Giuseppe Pelliccioni Leonardo Biscetti Elvira Maranesi |
| author_sort | Roberta Bevilacqua |
| collection | DOAJ |
| description | The use of radiomics and artificial intelligence applied for the diagnosis and monitoring of Alzheimer’s disease has developed in recent years. However, this approach is not yet completely applicable in clinical practice. The aim of this paper is to provide a systematic analysis of the studies that have included the use of radiomics from different imaging techniques and artificial intelligence for the diagnosis and monitoring of Alzheimer’s disease in order to improve the clinical outcomes and quality of life of older patients. A systematic review of the literature was conducted in February 2023, analyzing manuscripts and articles of the last 5 years from the PubMed, Scopus and Embase databases. All studies concerning discrimination among Alzheimer’s disease, Mild Cognitive Impairment and healthy older people performing radiomics analysis through machine and deep learning were included. A total of 15 papers were included. The results showed a very good performance of this approach in the differentiating Alzheimer’s disease patients—both at the dementia and pre-dementia phases of the disease—from healthy older people. In summary, radiomics and AI can be valuable tools for diagnosing and monitoring the progression of Alzheimer’s disease, potentially leading to earlier and more accurate diagnosis and treatment. However, the results reported by this review should be read with great caution, keeping in mind that imaging alone is not enough to identify dementia due to Alzheimer’s. |
| first_indexed | 2024-03-10T23:51:00Z |
| format | Article |
| id | doaj.art-f14c2a3fc5194be6bf265d878dc58f43 |
| institution | Directory Open Access Journal |
| issn | 2077-0383 |
| language | English |
| last_indexed | 2024-03-10T23:51:00Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Clinical Medicine |
| spelling | doaj.art-f14c2a3fc5194be6bf265d878dc58f432023-11-19T01:42:12ZengMDPI AGJournal of Clinical Medicine2077-03832023-08-011216543210.3390/jcm12165432Radiomics and Artificial Intelligence for the Diagnosis and Monitoring of Alzheimer’s Disease: A Systematic Review of Studies in the FieldRoberta Bevilacqua0Federico Barbarossa1Lorenzo Fantechi2Daniela Fornarelli3Enrico Paci4Silvia Bolognini5Cinzia Giammarchi6Fabrizia Lattanzio7Lucia Paciaroni8Giovanni Renato Riccardi9Giuseppe Pelliccioni10Leonardo Biscetti11Elvira Maranesi12Scientific Direction, IRCCS INRCA, 60124 Ancona, ItalyScientific Direction, IRCCS INRCA, 60124 Ancona, ItalyUnit of Nuclear Medicine, IRCCS INRCA, 60127 Ancona, ItalyUnit of Nuclear Medicine, IRCCS INRCA, 60127 Ancona, ItalyUnit of Radiology, IRCCS INRCA, 60127 Ancona, ItalyScientific Direction, IRCCS INRCA, 60124 Ancona, ItalyScientific Direction, IRCCS INRCA, 60124 Ancona, ItalyScientific Direction, IRCCS INRCA, 60124 Ancona, ItalyUnit of Neurology, IRCCS INRCA, 60127 Ancona, ItalyClinical Unit of Physical Rehabilitation, IRCCS INRCA, 60127 Ancona, ItalyUnit of Neurology, IRCCS INRCA, 60127 Ancona, ItalyUnit of Neurology, IRCCS INRCA, 60127 Ancona, ItalyScientific Direction, IRCCS INRCA, 60124 Ancona, ItalyThe use of radiomics and artificial intelligence applied for the diagnosis and monitoring of Alzheimer’s disease has developed in recent years. However, this approach is not yet completely applicable in clinical practice. The aim of this paper is to provide a systematic analysis of the studies that have included the use of radiomics from different imaging techniques and artificial intelligence for the diagnosis and monitoring of Alzheimer’s disease in order to improve the clinical outcomes and quality of life of older patients. A systematic review of the literature was conducted in February 2023, analyzing manuscripts and articles of the last 5 years from the PubMed, Scopus and Embase databases. All studies concerning discrimination among Alzheimer’s disease, Mild Cognitive Impairment and healthy older people performing radiomics analysis through machine and deep learning were included. A total of 15 papers were included. The results showed a very good performance of this approach in the differentiating Alzheimer’s disease patients—both at the dementia and pre-dementia phases of the disease—from healthy older people. In summary, radiomics and AI can be valuable tools for diagnosing and monitoring the progression of Alzheimer’s disease, potentially leading to earlier and more accurate diagnosis and treatment. However, the results reported by this review should be read with great caution, keeping in mind that imaging alone is not enough to identify dementia due to Alzheimer’s.https://www.mdpi.com/2077-0383/12/16/5432radiomicsartificial intelligenceolder peopleAlzheimersystematic reviewdiagnosis |
| spellingShingle | Roberta Bevilacqua Federico Barbarossa Lorenzo Fantechi Daniela Fornarelli Enrico Paci Silvia Bolognini Cinzia Giammarchi Fabrizia Lattanzio Lucia Paciaroni Giovanni Renato Riccardi Giuseppe Pelliccioni Leonardo Biscetti Elvira Maranesi Radiomics and Artificial Intelligence for the Diagnosis and Monitoring of Alzheimer’s Disease: A Systematic Review of Studies in the Field Journal of Clinical Medicine radiomics artificial intelligence older people Alzheimer systematic review diagnosis |
| title | Radiomics and Artificial Intelligence for the Diagnosis and Monitoring of Alzheimer’s Disease: A Systematic Review of Studies in the Field |
| title_full | Radiomics and Artificial Intelligence for the Diagnosis and Monitoring of Alzheimer’s Disease: A Systematic Review of Studies in the Field |
| title_fullStr | Radiomics and Artificial Intelligence for the Diagnosis and Monitoring of Alzheimer’s Disease: A Systematic Review of Studies in the Field |
| title_full_unstemmed | Radiomics and Artificial Intelligence for the Diagnosis and Monitoring of Alzheimer’s Disease: A Systematic Review of Studies in the Field |
| title_short | Radiomics and Artificial Intelligence for the Diagnosis and Monitoring of Alzheimer’s Disease: A Systematic Review of Studies in the Field |
| title_sort | radiomics and artificial intelligence for the diagnosis and monitoring of alzheimer s disease a systematic review of studies in the field |
| topic | radiomics artificial intelligence older people Alzheimer systematic review diagnosis |
| url | https://www.mdpi.com/2077-0383/12/16/5432 |
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