Artificial intelligence for brain diseases: A systematic review

A Segato, A Marzullo, F Calimeri, E De Momi - APL bioengineering, 2020 - pubs.aip.org
Artificial intelligence (AI) is a major branch of computer science that is fruitfully used for
analyzing complex medical data and extracting meaningful relationships in datasets, for …

Machine learning applications to neuroimaging for glioma detection and classification: An artificial intelligence augmented systematic review

QD Buchlak, N Esmaili, JC Leveque, C Bennett… - Journal of Clinical …, 2021 - Elsevier
Glioma is the most common primary intraparenchymal tumor of the brain and the 5-year
survival rate of high-grade glioma is poor. Magnetic resonance imaging (MRI) is essential for …

Machine learning for adaptive deep brain stimulation in Parkinson's disease: closing the loop

AM Oliveira, L Coelho, E Carvalho… - Journal of …, 2023 - Springer
Parkinson's disease (PD) is the second most common neurodegenerative disease bearing a
severe social and economic impact. So far, there is no known disease modifying therapy …

Machine learning and surgical outcomes prediction: a systematic review

O Elfanagely, Y Toyoda, S Othman, JA Mellia… - Journal of Surgical …, 2021 - Elsevier
Background Machine learning (ML) has garnered increasing attention as a means to
quantitatively analyze the growing and complex medical data to improve individualized …

[HTML][HTML] Clinical applications of magnetic resonance imaging based functional and structural connectivity

C Wu, F Ferreira, M Fox, N Harel, J Hattangadi-Gluth… - Neuroimage, 2021 - Elsevier
Advances in computational neuroimaging techniques have expanded the armamentarium of
imaging tools available for clinical applications in clinical neuroscience. Non-invasive, in …

Artificial intelligence and machine learning in prediction of surgical complications: current state, applications, and implications

AM Hassan, A Rajesh, M Asaad… - The American …, 2023 - journals.sagepub.com
Surgical complications pose significant challenges for surgeons, patients, and health care
systems as they may result in patient distress, suboptimal outcomes, and higher health care …

Machine learning's application in deep brain stimulation for Parkinson's disease: A review

J Watts, A Khojandi, O Shylo, RA Ramdhani - Brain Sciences, 2020 - mdpi.com
Deep brain stimulation (DBS) is a surgical treatment for advanced Parkinson's disease (PD)
that has undergone technological evolution that parallels an expansion in clinical …

Machine learning in deep brain stimulation: A systematic review

M Peralta, P Jannin, JSH Baxter - Artificial Intelligence in Medicine, 2021 - Elsevier
Abstract Deep Brain Stimulation (DBS) is an increasingly common therapy for a large range
of neurological disorders, such as abnormal movement disorders. The effectiveness of DBS …

Evaluation of machine learning algorithms for trabeculectomy outcome prediction in patients with glaucoma

HU Banna, A Zanabli, B McMillan, M Lehmann… - Scientific Reports, 2022 - nature.com
The purpose of this study was to evaluate the performance of machine learning algorithms to
predict trabeculectomy surgical outcomes. Preoperative systemic, demographic and ocular …

A novel deep learning model for STN localization from LFPs in Parkinson's disease

M Hosny, M Zhu, W Gao, Y Fu - Biomedical Signal Processing and Control, 2022 - Elsevier
Deep brain stimulation (DBS) is a common treatment for the neurological disorder,
Parkinson's disease (PD). DBS encompasses accurate implantation of stimulated electrodes …