Current Status and Future of Artificial Intelligence in MM Imaging: A Systematic Review
Artificial intelligence (AI) has attracted increasing attention as a tool for the detection and management of several medical conditions. Multiple myeloma (MM), a malignancy characterized by uncontrolled proliferation of plasma cells, is one of the most common hematologic malignancies, which relies...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/13/21/3372 |
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| author | Ehsan Alipour Atefe Pooyan Firoozeh Shomal Zadeh Azad Duke Darbandi Pietro Andrea Bonaffini Majid Chalian |
| author_facet | Ehsan Alipour Atefe Pooyan Firoozeh Shomal Zadeh Azad Duke Darbandi Pietro Andrea Bonaffini Majid Chalian |
| author_sort | Ehsan Alipour |
| collection | DOAJ |
| description | Artificial intelligence (AI) has attracted increasing attention as a tool for the detection and management of several medical conditions. Multiple myeloma (MM), a malignancy characterized by uncontrolled proliferation of plasma cells, is one of the most common hematologic malignancies, which relies on imaging for diagnosis and management. We aimed to review the current literature and trends in AI research of MM imaging. This study was performed according to the PRISMA guidelines. Three main concepts were used in the search algorithm, including “artificial intelligence” in “radiologic examinations” of patients with “multiple myeloma”. The algorithm was used to search the PubMed, Embase, and Web of Science databases. Articles were screened based on the inclusion and exclusion criteria. In the end, we used the checklist for Artificial Intelligence in Medical Imaging (CLAIM) criteria to evaluate the manuscripts. We provided the percentage of studies that were compliant with each criterion as a measure of the quality of AI research on MM. The initial search yielded 977 results. After reviewing them, 14 final studies were selected. The studies used a wide array of imaging modalities. Radiomics analysis and segmentation tasks were the most popular studies (10/14 studies). The common purposes of radiomics studies included the differentiation of MM bone lesions from other lesions and the prediction of relapse. The goal of the segmentation studies was to develop algorithms for the automatic segmentation of important structures in MM. Dice score was the most common assessment tool in segmentation studies, which ranged from 0.80 to 0.97. These studies show that imaging is a valuable data source for medical AI models and plays an even greater role in the management of MM. |
| first_indexed | 2024-03-11T11:32:12Z |
| format | Article |
| id | doaj.art-f6f5d8ef333a496db1b9909501a1d8b1 |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-11T11:32:12Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-f6f5d8ef333a496db1b9909501a1d8b12023-11-10T15:01:08ZengMDPI AGDiagnostics2075-44182023-11-011321337210.3390/diagnostics13213372Current Status and Future of Artificial Intelligence in MM Imaging: A Systematic ReviewEhsan Alipour0Atefe Pooyan1Firoozeh Shomal Zadeh2Azad Duke Darbandi3Pietro Andrea Bonaffini4Majid Chalian5Department of Radiology, Division of Musculoskeletal Imaging and Intervention, University of Washington, Seattle, WA 98195, USADepartment of Radiology, Division of Musculoskeletal Imaging and Intervention, University of Washington, Seattle, WA 98195, USADepartment of Radiology, Division of Musculoskeletal Imaging and Intervention, University of Washington, Seattle, WA 98195, USAChicago Medical School, Rosalind Franklin University of Medicine and Science, North Chicago, IL 60064, USADepartment of Radiology, Papa Giovanni XXIII Hospital, 24127 Bergamo, ItalyDepartment of Radiology, Division of Musculoskeletal Imaging and Intervention, University of Washington, Seattle, WA 98195, USAArtificial intelligence (AI) has attracted increasing attention as a tool for the detection and management of several medical conditions. Multiple myeloma (MM), a malignancy characterized by uncontrolled proliferation of plasma cells, is one of the most common hematologic malignancies, which relies on imaging for diagnosis and management. We aimed to review the current literature and trends in AI research of MM imaging. This study was performed according to the PRISMA guidelines. Three main concepts were used in the search algorithm, including “artificial intelligence” in “radiologic examinations” of patients with “multiple myeloma”. The algorithm was used to search the PubMed, Embase, and Web of Science databases. Articles were screened based on the inclusion and exclusion criteria. In the end, we used the checklist for Artificial Intelligence in Medical Imaging (CLAIM) criteria to evaluate the manuscripts. We provided the percentage of studies that were compliant with each criterion as a measure of the quality of AI research on MM. The initial search yielded 977 results. After reviewing them, 14 final studies were selected. The studies used a wide array of imaging modalities. Radiomics analysis and segmentation tasks were the most popular studies (10/14 studies). The common purposes of radiomics studies included the differentiation of MM bone lesions from other lesions and the prediction of relapse. The goal of the segmentation studies was to develop algorithms for the automatic segmentation of important structures in MM. Dice score was the most common assessment tool in segmentation studies, which ranged from 0.80 to 0.97. These studies show that imaging is a valuable data source for medical AI models and plays an even greater role in the management of MM.https://www.mdpi.com/2075-4418/13/21/3372multiple myelomaradiologyartificial intelligencemachine learningradiomicssegmentation |
| spellingShingle | Ehsan Alipour Atefe Pooyan Firoozeh Shomal Zadeh Azad Duke Darbandi Pietro Andrea Bonaffini Majid Chalian Current Status and Future of Artificial Intelligence in MM Imaging: A Systematic Review Diagnostics multiple myeloma radiology artificial intelligence machine learning radiomics segmentation |
| title | Current Status and Future of Artificial Intelligence in MM Imaging: A Systematic Review |
| title_full | Current Status and Future of Artificial Intelligence in MM Imaging: A Systematic Review |
| title_fullStr | Current Status and Future of Artificial Intelligence in MM Imaging: A Systematic Review |
| title_full_unstemmed | Current Status and Future of Artificial Intelligence in MM Imaging: A Systematic Review |
| title_short | Current Status and Future of Artificial Intelligence in MM Imaging: A Systematic Review |
| title_sort | current status and future of artificial intelligence in mm imaging a systematic review |
| topic | multiple myeloma radiology artificial intelligence machine learning radiomics segmentation |
| url | https://www.mdpi.com/2075-4418/13/21/3372 |
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