Artificial Intelligence in Brain Tumour Surgery-An Emerging Paradigm

被引:38
作者
Williams, Simon [1 ,2 ]
Layard Horsfall, Hugo [1 ,2 ]
Funnell, Jonathan P. [1 ,2 ]
Hanrahan, John G. [1 ,2 ]
Khan, Danyal Z. [1 ,2 ]
Muirhead, William [1 ,2 ]
Stoyanov, Danail [2 ]
Marcus, Hani J. [1 ,2 ]
机构
[1] Natl Hosp Neurol & Neurosurg, Dept Neurosurg, London WC1N 3BG, England
[2] Ctr Intervent & Surg Sci WEISS, Wellcome Engn & Phys Sci Res Council EPSRC, London W1W 7TY, England
基金
英国工程与自然科学研究理事会; 英国惠康基金;
关键词
artificial intelligence; AI; neurosurgery; brain tumour; machine learning; deep learning; surgery; oncology; CONVOLUTIONAL NEURAL-NETWORKS; QUANTITATIVE RADIOMICS APPROACH; COMPUTER-AIDED DETECTION; MACHINE LEARNING-METHODS; MR-IMAGES; AUTOMATIC SEGMENTATION; KEYHOLE NEUROSURGERY; PROTOCOL SELECTION; PRIMARY DIAGNOSIS; WORKFLOW ANALYSIS;
D O I
10.3390/cancers13195010
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
Artificial intelligence (AI) is the branch of computer science that enables machines to learn, reason, and problem solve. In recent decades, AI has been developed with the aim of improving the management of patients with brain tumours. This review article explores the role AI currently plays in managing patients undergoing brain tumour surgery, and explores how AI may impact this field in the future. Artificial intelligence (AI) platforms have the potential to cause a paradigm shift in brain tumour surgery. Brain tumour surgery augmented with AI can result in safer and more effective treatment. In this review article, we explore the current and future role of AI in patients undergoing brain tumour surgery, including aiding diagnosis, optimising the surgical plan, providing support during the operation, and better predicting the prognosis. Finally, we discuss barriers to the successful clinical implementation, the ethical concerns, and we provide our perspective on how the field could be advanced.
引用
收藏
页数:25
相关论文
共 225 条
[1]   A review on brain tumor diagnosis from MRI images: Practical implications, key achievements, and lessons learned [J].
Abd-Ellah, Mahmoud Khaled ;
Awad, Ali Ismail ;
Khalaf, Ashraf A. M. ;
Hamed, Hesham F. A. .
MAGNETIC RESONANCE IMAGING, 2019, 61 :300-318
[2]   The cyberknife: A frameless robotic system for radiosurgery [J].
Adler, JR ;
Chang, SD ;
Murphy, MJ ;
Doty, J ;
Geis, P ;
Hancock, SL .
STEREOTACTIC AND FUNCTIONAL NEUROSURGERY, 1997, 69 (1-4) :124-128
[3]   Hyperspectral imaging and quantitative analysis for prostate cancer detection [J].
Akbari, Hamed ;
Halig, Luma V. ;
Schuster, David M. ;
Osunkoya, Adeboye ;
Master, Viraj ;
Nieh, Peter T. ;
Chen, Georgia Z. ;
Fei, Baowei .
JOURNAL OF BIOMEDICAL OPTICS, 2012, 17 (07)
[4]   Predicting Deletion of Chromosomal Arms 1p/19q in Low-Grade Gliomas from MR Images Using Machine Intelligence [J].
Akkus, Zeynettin ;
Ali, Issa ;
Sedlar, Jiri ;
Agrawal, Jay P. ;
Parney, Ian F. ;
Giannini, Caterina ;
Erickson, Bradley J. .
JOURNAL OF DIGITAL IMAGING, 2017, 30 (04) :469-476
[5]   Computer-Aided Detection of Metastatic Brain Tumors Using Automated Three-Dimensional Template Matching [J].
Ambrosini, Robert D. ;
Wang, Peng ;
O'Dell, Walter G. .
JOURNAL OF MAGNETIC RESONANCE IMAGING, 2010, 31 (01) :85-93
[6]  
[Anonymous], IBMS WATSON RECOMMEN
[7]   The Moral Machine experiment [J].
Awad, Edmond ;
Dsouza, Sohan ;
Kim, Richard ;
Schulz, Jonathan ;
Henrich, Joseph ;
Shariff, Azim ;
Bonnefon, Jean-Francois ;
Rahwan, Iyad .
NATURE, 2018, 563 (7729) :59-+
[8]   Automated classification of brain tumor type in whole-slide digital pathology images using local representative tiles [J].
Barker, Jocelyn ;
Hoogi, Assaf ;
Depeursinge, Adrien ;
Rubin, Daniel L. .
MEDICAL IMAGE ANALYSIS, 2016, 30 :60-71
[9]   The potential of artificial intelligence to improve patient safety: a scoping review [J].
Bates, David W. ;
Levine, David ;
Syrowatka, Ania ;
Kuznetsova, Masha ;
Craig, Kelly Jean Thomas ;
Rui, Angela ;
Jackson, Gretchen Purcell ;
Rhee, Kyu .
NPJ DIGITAL MEDICINE, 2021, 4 (01)
[10]   Validation of Whole Slide Imaging for Primary Diagnosis in Surgical Pathology [J].
Bauer, Thomas W. ;
Schoenfield, Lynn ;
Slaw, Renee J. ;
Yerian, Lisa ;
Sun, Zhiyuan ;
Henricks, Walter H. .
ARCHIVES OF PATHOLOGY & LABORATORY MEDICINE, 2013, 137 (04) :518-524