Clinical applications of artificial intelligence and radiomics in neuro-oncology imaging

AAK Abdel Razek, A Alksas, M Shehata… - Insights into …, 2021 - Springer
This article is a comprehensive review of the basic background, technique, and clinical
applications of artificial intelligence (AI) and radiomics in the field of neuro-oncology. A …

Convergence of artificial intelligence and neuroscience towards the diagnosis of neurological disorders—a scoping review

C Surianarayanan, JJ Lawrence, PR Chelliah… - Sensors, 2023 - mdpi.com
Artificial intelligence (AI) is a field of computer science that deals with the simulation of
human intelligence using machines so that such machines gain problem-solving and …

Advancements in oncology with artificial intelligence—a review article

N Vobugari, V Raja, U Sethi, K Gandhi, K Raja… - Cancers, 2022 - mdpi.com
Simple Summary With the advancement of artificial intelligence, including machine learning,
the field of oncology has seen promising results in cancer detection and classification …

Large language models in neurology research and future practice

MF Romano, LC Shih, IC Paschalidis, R Au… - Neurology, 2023 - AAN Enterprises
Recent advancements in generative artificial intelligence, particularly using large language
models (LLMs), are gaining increased public attention. We provide a perspective on the …

Artificial intelligence and deep learning in neuroradiology: exploring the new frontier

H Kaka, E Zhang, N Khan - Canadian Association of …, 2021 - journals.sagepub.com
There have been many recently published studies exploring machine learning (ML) and
deep learning applications within neuroradiology. The improvement in performance of these …

Topics and trends in artificial intelligence assisted human brain research

X Chen, J Chen, G Cheng, T Gong - PloS one, 2020 - journals.plos.org
Artificial intelligence (AI) assisted human brain research is a dynamic interdisciplinary field
with great interest, rich literature, and huge diversity. The diversity in research topics and …

Using adversarial images to assess the robustness of deep learning models trained on diagnostic images in oncology

MZ Joel, S Umrao, E Chang, R Choi… - JCO Clinical Cancer …, 2022 - ascopubs.org
PURPOSE Deep learning (DL) models have rapidly become a popular and cost-effective
tool for image classification within oncology. A major limitation of DL models is their …

Machine learning using multiparametric magnetic resonance imaging radiomic feature analysis to predict Ki-67 in World Health Organization grade I meningiomas

O Khanna, AF Kazerooni, CJ Farrell… - …, 2021 - journals.lww.com
BACKGROUND Although World Health Organization (WHO) grade I meningiomas are
considered “benign” tumors, an elevated Ki-67 is one crucial factor that has been shown to …

Application of artificial intelligence methods for imaging of spinal metastasis

W Ong, L Zhu, W Zhang, T Kuah, DSW Lim, XZ Low… - Cancers, 2022 - mdpi.com
Simple Summary Spinal metastasis is the most common malignant disease of the spine, and
its early diagnosis and treatment is important to prevent complications and improve quality of …

Molecular MRI-Based Monitoring of Cancer Immunotherapy Treatment Response

N Vladimirov, O Perlman - International Journal of Molecular Sciences, 2023 - mdpi.com
Immunotherapy constitutes a paradigm shift in cancer treatment. Its FDA approval for several
indications has yielded improved prognosis for cases where traditional therapy has shown …