Machine Learning and Artificial Intelligence Systems Based on the Optical Spectral Analysis in Neuro-Oncology - Tatiana Savelieva; Igor Romanishkin; Anuar Ospanov; Sergey Goryaynov; Galina Pavlova; Igor Pronin; Victor Loschenov (Photonics)
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.