Textural analysis and artificial intelligence as decision support tools in the diagnosis of multiple sclerosis – a systematic review
IntroductionMagnetic resonance imaging (MRI) is conventionally used for the detection and diagnosis of multiple sclerosis (MS), often complemented by lumbar puncture—a highly invasive method—to validate the diagnosis. Additionally, MRI is periodically repeated to monitor disease progression and trea...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-01-01
|
| Series: | Frontiers in Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2024.1457420/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832592432419569664 |
|---|---|
| author | Filip Orzan Ştefania D. Iancu Laura Dioşan Zoltán Bálint |
| author_facet | Filip Orzan Ştefania D. Iancu Laura Dioşan Zoltán Bálint |
| author_sort | Filip Orzan |
| collection | DOAJ |
| description | IntroductionMagnetic resonance imaging (MRI) is conventionally used for the detection and diagnosis of multiple sclerosis (MS), often complemented by lumbar puncture—a highly invasive method—to validate the diagnosis. Additionally, MRI is periodically repeated to monitor disease progression and treatment efficacy. Recent research has focused on the application of artificial intelligence (AI) and radiomics in medical image processing, diagnosis, and treatment planning.MethodsA review of the current literature was conducted, analyzing the use of AI models and texture analysis for MS lesion segmentation and classification. The study emphasizes common models, including U-Net, Support Vector Machine, Random Forest, and K-Nearest Neighbors, alongside their evaluation metrics.ResultsThe analysis revealed a fragmented research landscape, with significant variation in model architectures and performance. Evaluation metrics such as Accuracy, Dice score, and Sensitivity are commonly employed, with some models demonstrating robustness across multi-center datasets. However, most studies lack validation in clinical scenarios.DiscussionThe absence of consensus on the optimal model for MS lesion segmentation highlights the need for standardized methodologies and clinical validation. Future research should prioritize clinical trials to establish the real-world applicability of AI-driven decision support tools. This review provides a comprehensive overview of contemporary advancements in AI and radiomics for analyzing and monitoring emerging MS lesions in MRI. |
| format | Article |
| id | doaj-art-b94b063aa1624d5eaa9a223659dfa35e |
| institution | Kabale University |
| issn | 1662-453X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj-art-b94b063aa1624d5eaa9a223659dfa35e2025-01-21T08:36:34ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2025-01-011810.3389/fnins.2024.14574201457420Textural analysis and artificial intelligence as decision support tools in the diagnosis of multiple sclerosis – a systematic reviewFilip Orzan0Ştefania D. Iancu1Laura Dioşan2Zoltán Bálint3Department of Biomedical Physics, Faculty of Physics, Babeş-Bolyai University, Cluj-Napoca, RomaniaDepartment of Biomedical Physics, Faculty of Physics, Babeş-Bolyai University, Cluj-Napoca, RomaniaFaculty of Mathematics and Computer Science, Babeş-Bolyai University, Cluj-Napoca, RomaniaDepartment of Biomedical Physics, Faculty of Physics, Babeş-Bolyai University, Cluj-Napoca, RomaniaIntroductionMagnetic resonance imaging (MRI) is conventionally used for the detection and diagnosis of multiple sclerosis (MS), often complemented by lumbar puncture—a highly invasive method—to validate the diagnosis. Additionally, MRI is periodically repeated to monitor disease progression and treatment efficacy. Recent research has focused on the application of artificial intelligence (AI) and radiomics in medical image processing, diagnosis, and treatment planning.MethodsA review of the current literature was conducted, analyzing the use of AI models and texture analysis for MS lesion segmentation and classification. The study emphasizes common models, including U-Net, Support Vector Machine, Random Forest, and K-Nearest Neighbors, alongside their evaluation metrics.ResultsThe analysis revealed a fragmented research landscape, with significant variation in model architectures and performance. Evaluation metrics such as Accuracy, Dice score, and Sensitivity are commonly employed, with some models demonstrating robustness across multi-center datasets. However, most studies lack validation in clinical scenarios.DiscussionThe absence of consensus on the optimal model for MS lesion segmentation highlights the need for standardized methodologies and clinical validation. Future research should prioritize clinical trials to establish the real-world applicability of AI-driven decision support tools. This review provides a comprehensive overview of contemporary advancements in AI and radiomics for analyzing and monitoring emerging MS lesions in MRI.https://www.frontiersin.org/articles/10.3389/fnins.2024.1457420/fullmultiple sclerosisMRIartificial intelligencecomputer assisted diagnosisU-Netradiomics |
| spellingShingle | Filip Orzan Ştefania D. Iancu Laura Dioşan Zoltán Bálint Textural analysis and artificial intelligence as decision support tools in the diagnosis of multiple sclerosis – a systematic review Frontiers in Neuroscience multiple sclerosis MRI artificial intelligence computer assisted diagnosis U-Net radiomics |
| title | Textural analysis and artificial intelligence as decision support tools in the diagnosis of multiple sclerosis – a systematic review |
| title_full | Textural analysis and artificial intelligence as decision support tools in the diagnosis of multiple sclerosis – a systematic review |
| title_fullStr | Textural analysis and artificial intelligence as decision support tools in the diagnosis of multiple sclerosis – a systematic review |
| title_full_unstemmed | Textural analysis and artificial intelligence as decision support tools in the diagnosis of multiple sclerosis – a systematic review |
| title_short | Textural analysis and artificial intelligence as decision support tools in the diagnosis of multiple sclerosis – a systematic review |
| title_sort | textural analysis and artificial intelligence as decision support tools in the diagnosis of multiple sclerosis a systematic review |
| topic | multiple sclerosis MRI artificial intelligence computer assisted diagnosis U-Net radiomics |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2024.1457420/full |
| work_keys_str_mv | AT filiporzan texturalanalysisandartificialintelligenceasdecisionsupporttoolsinthediagnosisofmultiplesclerosisasystematicreview AT stefaniadiancu texturalanalysisandartificialintelligenceasdecisionsupporttoolsinthediagnosisofmultiplesclerosisasystematicreview AT lauradiosan texturalanalysisandartificialintelligenceasdecisionsupporttoolsinthediagnosisofmultiplesclerosisasystematicreview AT zoltanbalint texturalanalysisandartificialintelligenceasdecisionsupporttoolsinthediagnosisofmultiplesclerosisasystematicreview |