Machine Learning and Artificial Intelligence Systems Based on the Optical Spectral Analysis in Neuro-Oncology
Decision support systems based on machine learning (ML) techniques are already empowering neuro-oncologists. These systems provide comprehensive diagnostics, offer a deeper understanding of diseases, predict outcomes, and assist in customizing treatment plans to individual patient needs. Collectivel...
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| Format: | Article |
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MDPI AG
2025-01-01
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| Series: | Photonics |
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| Online Access: | https://www.mdpi.com/2304-6732/12/1/37 |
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| author | Tatiana Savelieva Igor Romanishkin Anuar Ospanov Sergey Goryaynov Galina Pavlova Igor Pronin Victor Loschenov |
| author_facet | Tatiana Savelieva Igor Romanishkin Anuar Ospanov Sergey Goryaynov Galina Pavlova Igor Pronin Victor Loschenov |
| author_sort | Tatiana Savelieva |
| collection | DOAJ |
| description | Decision support systems based on machine learning (ML) techniques are already empowering neuro-oncologists. These systems provide comprehensive diagnostics, offer a deeper understanding of diseases, predict outcomes, and assist in customizing treatment plans to individual patient needs. Collectively, these elements represent artificial intelligence (AI) in neuro-oncology. This paper reviews recent studies which apply machine learning algorithms to optical spectroscopy data from central nervous system (CNS) tumors, both ex vivo and in vivo. We first cover general issues such as the physical basis of the optical-spectral methods used in neuro-oncology, and the basic algorithms used in spectral signal preprocessing, feature extraction, data clustering, and supervised classification methods. Then, we review in more detail the methodology and results of applying ML techniques to fluorescence, elastic and inelastic scattering, and IR spectroscopy. |
| format | Article |
| id | doaj-art-04af9dfe32204c8495a1acfe5ef484ae |
| institution | Kabale University |
| issn | 2304-6732 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Photonics |
| spelling | doaj-art-04af9dfe32204c8495a1acfe5ef484ae2025-01-24T13:46:17ZengMDPI AGPhotonics2304-67322025-01-011213710.3390/photonics12010037Machine Learning and Artificial Intelligence Systems Based on the Optical Spectral Analysis in Neuro-OncologyTatiana Savelieva0Igor Romanishkin1Anuar Ospanov2Sergey Goryaynov3Galina Pavlova4Igor Pronin5Victor Loschenov6Prokhorov General Physics Institute of the Russian Academy of Sciences, 119991 Moscow, RussiaProkhorov General Physics Institute of the Russian Academy of Sciences, 119991 Moscow, RussiaEngineering Physics Institute of Biomedicine, National Research Nuclear University MEPhI (Moscow Engineering Physics Institute), 115409 Moscow, RussiaN.N. Burdenko National Medical Research Center of Neurosurgery, 125047 Moscow, RussiaN.N. Burdenko National Medical Research Center of Neurosurgery, 125047 Moscow, RussiaN.N. Burdenko National Medical Research Center of Neurosurgery, 125047 Moscow, RussiaProkhorov General Physics Institute of the Russian Academy of Sciences, 119991 Moscow, RussiaDecision support systems based on machine learning (ML) techniques are already empowering neuro-oncologists. These systems provide comprehensive diagnostics, offer a deeper understanding of diseases, predict outcomes, and assist in customizing treatment plans to individual patient needs. Collectively, these elements represent artificial intelligence (AI) in neuro-oncology. This paper reviews recent studies which apply machine learning algorithms to optical spectroscopy data from central nervous system (CNS) tumors, both ex vivo and in vivo. We first cover general issues such as the physical basis of the optical-spectral methods used in neuro-oncology, and the basic algorithms used in spectral signal preprocessing, feature extraction, data clustering, and supervised classification methods. Then, we review in more detail the methodology and results of applying ML techniques to fluorescence, elastic and inelastic scattering, and IR spectroscopy.https://www.mdpi.com/2304-6732/12/1/37optical spectroscopyfluorescencediffuse reflectanceRaman scatteringFourier transform infrared spectroscopymachine learning |
| spellingShingle | Tatiana Savelieva Igor Romanishkin Anuar Ospanov Sergey Goryaynov Galina Pavlova Igor Pronin Victor Loschenov Machine Learning and Artificial Intelligence Systems Based on the Optical Spectral Analysis in Neuro-Oncology Photonics optical spectroscopy fluorescence diffuse reflectance Raman scattering Fourier transform infrared spectroscopy machine learning |
| title | Machine Learning and Artificial Intelligence Systems Based on the Optical Spectral Analysis in Neuro-Oncology |
| title_full | Machine Learning and Artificial Intelligence Systems Based on the Optical Spectral Analysis in Neuro-Oncology |
| title_fullStr | Machine Learning and Artificial Intelligence Systems Based on the Optical Spectral Analysis in Neuro-Oncology |
| title_full_unstemmed | Machine Learning and Artificial Intelligence Systems Based on the Optical Spectral Analysis in Neuro-Oncology |
| title_short | Machine Learning and Artificial Intelligence Systems Based on the Optical Spectral Analysis in Neuro-Oncology |
| title_sort | machine learning and artificial intelligence systems based on the optical spectral analysis in neuro oncology |
| topic | optical spectroscopy fluorescence diffuse reflectance Raman scattering Fourier transform infrared spectroscopy machine learning |
| url | https://www.mdpi.com/2304-6732/12/1/37 |
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