@inbook{ae136fa936494d91864868fe675c2258,
title = "Computational Modeling and AI in Radiation Neuro-Oncology and Radiosurgery",
abstract = "The chapter explores the extensive integration of artificial intelligence (AI) in healthcare systems, with a specific focus on its application in stereotactic radiosurgery. The rapid evolution of AI technology has led to promising developments in this field, particularly through the utilization of machine learning and deep learning models. The diverse implementation of AI algorithms was developed from various aspects of radiosurgery, including the successful detection of spontaneous tumors and the automated delineation or segmentation of lesions. These applications show potential for extension to longitudinal treatment follow-up. Additionally, the chapter highlights the established use of machine learning algorithms, particularly those incorporating radiomic-based analysis, in predicting treatment outcomes. The discussion encompasses current achievements, existing limitations, and the need for further investigation in the dynamic intersection of AI and radiosurgery.",
keywords = "Artificial intelligence, Deep learning, Detection, Gamma Knife, Machine learning, Prediction, Radiomics, Radiosurgery, Segmentation, Stereotactic",
author = "Lee, {Cheng Chia} and Yang, {Huai Che} and Wu, {Hsiu Mei} and Lin, {Yen Yu} and Lu, {Chia Feng} and Peng, {Syu Jyun} and Wu, {Yu Te} and Sheehan, {Jason P.} and Guo, {Wan Yuo}",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.",
year = "2024",
doi = "10.1007/978-3-031-64892-2_18",
language = "English",
series = "Advances in Experimental Medicine and Biology",
publisher = "Springer",
pages = "307--322",
booktitle = "Advances in Experimental Medicine and Biology",
address = "Germany",
}