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Awuah WA, Adebusoye FT, Wellington J, David L, Salam A, Weng Yee AL, Lansiaux E, Yarlagadda R, Garg T, Abdul-Rahman T, Kalmanovich J, Miteu GD, Kundu M, Mykolaivna NI. Recent Outcomes and Challenges of Artificial Intelligence, Machine Learning, and Deep Learning in Neurosurgery. World Neurosurg X 2024; 23:100301. [PMID: 38577317 PMCID: PMC10992893 DOI: 10.1016/j.wnsx.2024.100301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 07/23/2023] [Accepted: 02/21/2024] [Indexed: 04/06/2024] Open
Abstract
Neurosurgeons receive extensive technical training, which equips them with the knowledge and skills to specialise in various fields and manage the massive amounts of information and decision-making required throughout the various stages of neurosurgery, including preoperative, intraoperative, and postoperative care and recovery. Over the past few years, artificial intelligence (AI) has become more useful in neurosurgery. AI has the potential to improve patient outcomes by augmenting the capabilities of neurosurgeons and ultimately improving diagnostic and prognostic outcomes as well as decision-making during surgical procedures. By incorporating AI into both interventional and non-interventional therapies, neurosurgeons may provide the best care for their patients. AI, machine learning (ML), and deep learning (DL) have made significant progress in the field of neurosurgery. These cutting-edge methods have enhanced patient outcomes, reduced complications, and improved surgical planning.
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Affiliation(s)
| | | | - Jack Wellington
- Cardiff University School of Medicine, Cardiff University, Wales, United Kingdom
| | - Lian David
- Norwich Medical School, University of East Anglia, United Kingdom
| | - Abdus Salam
- Department of Surgery, Khyber Teaching Hospital, Peshawar, Pakistan
| | | | | | - Rohan Yarlagadda
- Rowan University School of Osteopathic Medicine, Stratford, NJ, USA
| | - Tulika Garg
- Government Medical College and Hospital Chandigarh, India
| | | | | | | | - Mrinmoy Kundu
- Institute of Medical Sciences and SUM Hospital, Bhubaneswar, India
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2
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Shlobin NA, Rosseau G. Opportunities and Considerations for the Incorporation of Artificial Intelligence into Global Neurosurgery: A Generative Pretrained Transformer Chatbot-Based Approach. World Neurosurg 2024:S1878-8750(24)00535-7. [PMID: 38561032 DOI: 10.1016/j.wneu.2024.03.149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 03/25/2024] [Accepted: 03/26/2024] [Indexed: 04/04/2024]
Abstract
OBJECTIVE Global neurosurgery is a public health focus in neurosurgery that seeks to ensure safe, timely, and affordable neurosurgical care to all individuals worldwide. Although investigators have begun to explore the promise of artificial intelligence (AI) for neurosurgery, its applicability to global neurosurgery has been largely hypothetical. We characterize opportunities and considerations for the incorporation of AI into global neurosurgery by synthesizing key themes yielded from a series of generative pretrained transformers (GPTs), discuss important limitations of GPTs and cautions when using AI in neurosurgery, and develop a framework for the equitable incorporation of AI into global neurosurgery. METHODS ChatGPT, Bing Chat/Copilot, You, Perplexity.ai, and Google Bard were queried with the prompt "How can AI be incorporated into global neurosurgery?" A layered ChatGPT-based thematic analysis was performed. The authors synthesized the results into opportunities and considerations for the incorporation of AI in global neurosurgery. A Pareto analysis was conducted to determine common themes. RESULTS Eight opportunities and 14 important considerations were synthesized. Six opportunities related to patient care, 1 to education, and another to public health planning. Four of the important considerations were deemed specific to global neurosurgery. The Pareto analysis included all 8 opportunities and 5 considerations. CONCLUSIONS AI may be incorporated into global neurosurgery in a variety of capacities requiring numerous considerations. The framework presented in this manuscript may facilitate the incorporation of AI into global neurosurgery initiatives while balancing contextual factors and the reality of limited resources.
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Affiliation(s)
- Nathan A Shlobin
- Department of Neurological Surgery, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA.
| | - Gail Rosseau
- Department of Neurosurgery, George Washington University School of Medicine and Health Sciences, Washington, District of Columbia, USA; Barrow Global, Barrow Neurological Institute, Phoenix, Arizona, USA
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3
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Li X, Xiang S, Li G. Application of artificial intelligence in brain arteriovenous malformations: Angioarchitectures, clinical symptoms and prognosis prediction. Interv Neuroradiol 2024:15910199241238798. [PMID: 38515371 DOI: 10.1177/15910199241238798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/23/2024] Open
Abstract
BACKGROUND Artificial intelligence (AI) has rapidly advanced in the medical field, leveraging its intelligence and automation for the management of various diseases. Brain arteriovenous malformations (AVM) are particularly noteworthy, experiencing rapid development in recent years and yielding remarkable results. This paper aims to summarize the applications of AI in the management of AVMs management. METHODS Literatures published in PubMed during 1999-2022, discussing AI application in AVMs management were reviewed. RESULTS AI algorithms have been applied in various aspects of AVM management, particularly in machine learning and deep learning models. Automatic lesion segmentation or delineation is a promising application that can be further developed and verified. Prognosis prediction using machine learning algorithms with radiomic-based analysis is another meaningful application. CONCLUSIONS AI has been widely used in AVMs management. This article summarizes the current research progress, limitations and future research directions.
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Affiliation(s)
- Xiangyu Li
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Sishi Xiang
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Guilin Li
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
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4
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Qian Y, Alhaskawi A, Dong Y, Ni J, Abdalbary S, Lu H. Transforming medicine: artificial intelligence integration in the peripheral nervous system. Front Neurol 2024; 15:1332048. [PMID: 38419700 PMCID: PMC10899496 DOI: 10.3389/fneur.2024.1332048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Accepted: 02/01/2024] [Indexed: 03/02/2024] Open
Abstract
In recent years, artificial intelligence (AI) has undergone remarkable advancements, exerting a significant influence across a multitude of fields. One area that has particularly garnered attention and witnessed substantial progress is its integration into the realm of the nervous system. This article provides a comprehensive examination of AI's applications within the peripheral nervous system, with a specific focus on AI-enhanced diagnostics for peripheral nervous system disorders, AI-driven pain management, advancements in neuroprosthetics, and the development of neural network models. By illuminating these facets, we unveil the burgeoning opportunities for revolutionary medical interventions and the enhancement of human capabilities, thus paving the way for a future in which AI becomes an integral component of our nervous system's interface.
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Affiliation(s)
- Yue Qian
- Rehabilitation Center, Hangzhou Wuyunshan Hospital (Hangzhou Institute of Health Promotion), Hangzhou, China
| | - Ahmad Alhaskawi
- Department of Orthopedics, The First Affiliated Hospital, Zhejiang University, Hangzhou, China
| | - Yanzhao Dong
- Department of Orthopedics, The First Affiliated Hospital, Zhejiang University, Hangzhou, China
| | - Juemin Ni
- Rehabilitation Center, Hangzhou Wuyunshan Hospital (Hangzhou Institute of Health Promotion), Hangzhou, China
| | - Sahar Abdalbary
- Department of Orthopedic Physical Therapy, Faculty of Physical Therapy, Nahda University in Beni Suef, Beni Suef, Egypt
| | - Hui Lu
- Department of Orthopedics, The First Affiliated Hospital, Zhejiang University, Hangzhou, China
- Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Zhejiang University, Hangzhou, China
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Rahman MA, Victoros E, Ernest J, Davis R, Shanjana Y, Islam MR. Impact of Artificial Intelligence (AI) Technology in Healthcare Sector: A Critical Evaluation of Both Sides of the Coin. Clin Pathol 2024; 17:2632010X241226887. [PMID: 38264676 PMCID: PMC10804900 DOI: 10.1177/2632010x241226887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 12/27/2023] [Indexed: 01/25/2024]
Abstract
The influence of artificial intelligence (AI) has drastically risen in recent years, especially in the field of medicine. Its influence has spread so greatly that it is determined to become a pillar in the future medical world. A comprehensive literature search related to AI in healthcare was performed in the PubMed database and retrieved the relevant information from suitable ones. AI excels in aspects such as rapid adaptation, high diagnostic accuracy, and data management that can help improve workforce productivity. With this potential in sight, the FDA has continuously approved more machine learning (ML) software to be used by medical workers and scientists. However, there are few controversies such as increased chances of data breaches, concern for clinical implementation, and potential healthcare dilemmas. In this article, the positive and negative aspects of AI implementation in healthcare are discussed, as well as recommended some potential solutions to the potential issues at hand.
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Affiliation(s)
| | | | - Julianne Ernest
- Nesbitt School of Pharmacy Wilkes University, Wilkes-Barre, PA, USA
| | - Rob Davis
- Nesbitt School of Pharmacy Wilkes University, Wilkes-Barre, PA, USA
| | - Yeasna Shanjana
- Department of Environmental Sciences, North South University, Bashundhara, Dhaka, Bangladesh
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Meade SM, Salas-Vega S, Nagy MR, Sundar SJ, Steinmetz MP, Benzel EC, Habboub G. A Pilot Remote Curriculum to Enhance Resident and Medical Student Understanding of Machine Learning in Healthcare. World Neurosurg 2023; 180:e142-e148. [PMID: 37696433 DOI: 10.1016/j.wneu.2023.09.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 08/22/2023] [Accepted: 09/04/2023] [Indexed: 09/13/2023]
Abstract
BACKGROUND Despite the expanding role of machine learning (ML) in health care and patient expectations for clinicians to understand ML-based tools, few for-credit curricula exist specifically for neurosurgical trainees to learn basic principles and implications of ML for medical research and clinical practice. We implemented a novel, remotely delivered curriculum designed to develop literacy in ML for neurosurgical trainees. METHODS A 4-week pilot medical elective was designed specifically for trainees to build literacy in basic ML concepts. Qualitative feedback from interested and enrolled students was collected to assess students' and trainees' reactions, learning, and future application of course content. RESULTS Despite 15 interested learners, only 3 medical students and 1 neurosurgical resident completed the course. Enrollment included students and trainees from 3 different institutions. All learners who completed the course found the lectures relevant to their future practice as clinicians and researchers and reported improved confidence in applying and understanding published literature applying ML techniques in health care. Barriers to ample enrollment and retention (e.g., balancing clinical responsibilities) were identified. CONCLUSIONS This pilot elective demonstrated the interest, value, and feasibility of a remote elective to establish ML literacy; however, feedback to increase accessibility and flexibility of the course encouraged our team to implement changes. Future elective iterations will have a semiannual, 2-week format, splitting lectures more clearly between theory (the method and its value) and application (coding instructions) and will make lectures open-source prerequisites to allow tailoring of student learning to their planned application of these methods in their practice and research.
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Affiliation(s)
- Seth M Meade
- Department of Neurosurgery, Cleveland Clinic Lerner College of Medicine, Cleveland, Ohio, USA; Case Western School of Medicine, Case Western Reserve University, Cleveland, Ohio, USA; Department of Neurosurgery, Neurologic Institute, Center for Spine Health, Cleveland Clinic Foundation, Cleveland, Ohio, USA.
| | - Sebastian Salas-Vega
- Case Western School of Medicine, Case Western Reserve University, Cleveland, Ohio, USA; Department of Neurosurgery, Neurologic Institute, Center for Spine Health, Cleveland Clinic Foundation, Cleveland, Ohio, USA; Department of Neurosurgery, Inova Health System, Falls Church, Virginia, USA
| | - Matthew R Nagy
- Department of Neurosurgery, Cleveland Clinic Lerner College of Medicine, Cleveland, Ohio, USA; Case Western School of Medicine, Case Western Reserve University, Cleveland, Ohio, USA
| | - Swetha J Sundar
- Department of Neurosurgery, Cleveland Clinic Lerner College of Medicine, Cleveland, Ohio, USA; Department of Neurosurgery, Neurologic Institute, Center for Spine Health, Cleveland Clinic Foundation, Cleveland, Ohio, USA
| | - Michael P Steinmetz
- Department of Neurosurgery, Cleveland Clinic Lerner College of Medicine, Cleveland, Ohio, USA; Department of Neurosurgery, Neurologic Institute, Center for Spine Health, Cleveland Clinic Foundation, Cleveland, Ohio, USA
| | - Edward C Benzel
- Department of Neurosurgery, Cleveland Clinic Lerner College of Medicine, Cleveland, Ohio, USA; Department of Neurosurgery, Neurologic Institute, Center for Spine Health, Cleveland Clinic Foundation, Cleveland, Ohio, USA
| | - Ghaith Habboub
- Department of Neurosurgery, Cleveland Clinic Lerner College of Medicine, Cleveland, Ohio, USA; Department of Neurosurgery, Neurologic Institute, Center for Spine Health, Cleveland Clinic Foundation, Cleveland, Ohio, USA
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Buyck F, Vandemeulebroucke J, Ceranka J, Van Gestel F, Cornelius JF, Duerinck J, Bruneau M. Computer-vision based analysis of the neurosurgical scene - A systematic review. Brain Spine 2023; 3:102706. [PMID: 38020988 PMCID: PMC10668095 DOI: 10.1016/j.bas.2023.102706] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 10/23/2023] [Accepted: 10/29/2023] [Indexed: 12/01/2023]
Abstract
Introduction With increasing use of robotic surgical adjuncts, artificial intelligence and augmented reality in neurosurgery, the automated analysis of digital images and videos acquired over various procedures becomes a subject of increased interest. While several computer vision (CV) methods have been developed and implemented for analyzing surgical scenes, few studies have been dedicated to neurosurgery. Research question In this work, we present a systematic literature review focusing on CV methodologies specifically applied to the analysis of neurosurgical procedures based on intra-operative images and videos. Additionally, we provide recommendations for the future developments of CV models in neurosurgery. Material and methods We conducted a systematic literature search in multiple databases until January 17, 2023, including Web of Science, PubMed, IEEE Xplore, Embase, and SpringerLink. Results We identified 17 studies employing CV algorithms on neurosurgical videos/images. The most common applications of CV were tool and neuroanatomical structure detection or characterization, and to a lesser extent, surgical workflow analysis. Convolutional neural networks (CNN) were the most frequently utilized architecture for CV models (65%), demonstrating superior performances in tool detection and segmentation. In particular, mask recurrent-CNN manifested most robust performance outcomes across different modalities. Discussion and conclusion Our systematic review demonstrates that CV models have been reported that can effectively detect and differentiate tools, surgical phases, neuroanatomical structures, as well as critical events in complex neurosurgical scenes with accuracies above 95%. Automated tool recognition contributes to objective characterization and assessment of surgical performance, with potential applications in neurosurgical training and intra-operative safety management.
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Affiliation(s)
- Félix Buyck
- Department of Neurosurgery, Universitair Ziekenhuis Brussel (UZ Brussel), 1090, Brussels, Belgium
- Vrije Universiteit Brussel (VUB), Research group Center For Neurosciences (C4N-NEUR), 1090, Brussels, Belgium
| | - Jef Vandemeulebroucke
- Vrije Universiteit Brussel (VUB), Department of Electronics and Informatics (ETRO), 1050, Brussels, Belgium
- Department of Radiology, Universitair Ziekenhuis Brussel (UZ Brussel), 1090, Brussels, Belgium
- imec, 3001, Leuven, Belgium
| | - Jakub Ceranka
- Vrije Universiteit Brussel (VUB), Department of Electronics and Informatics (ETRO), 1050, Brussels, Belgium
- imec, 3001, Leuven, Belgium
| | - Frederick Van Gestel
- Department of Neurosurgery, Universitair Ziekenhuis Brussel (UZ Brussel), 1090, Brussels, Belgium
- Vrije Universiteit Brussel (VUB), Research group Center For Neurosciences (C4N-NEUR), 1090, Brussels, Belgium
| | - Jan Frederick Cornelius
- Department of Neurosurgery, Medical Faculty, Heinrich-Heine-University, 40225, Düsseldorf, Germany
| | - Johnny Duerinck
- Department of Neurosurgery, Universitair Ziekenhuis Brussel (UZ Brussel), 1090, Brussels, Belgium
- Vrije Universiteit Brussel (VUB), Research group Center For Neurosciences (C4N-NEUR), 1090, Brussels, Belgium
| | - Michaël Bruneau
- Department of Neurosurgery, Universitair Ziekenhuis Brussel (UZ Brussel), 1090, Brussels, Belgium
- Vrije Universiteit Brussel (VUB), Research group Center For Neurosciences (C4N-NEUR), 1090, Brussels, Belgium
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Planells H, Parmar V, Marcus HJ, Pandit AS. From theory to practice: what is the potential of artificial intelligence in the future of neurosurgery? Expert Rev Neurother 2023; 23:1041-1046. [PMID: 37997765 DOI: 10.1080/14737175.2023.2285432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 11/15/2023] [Indexed: 11/25/2023]
Affiliation(s)
- Hannah Planells
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, UK
| | - Viraj Parmar
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, UK
| | - Hani J Marcus
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, UK
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK
| | - Anand S Pandit
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, UK
- High-dimensional Neurology, Institute of Neurology, London, UK
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Abstract
Artificial intelligence (AI) is an emerging technology in healthcare and holds the potential to disrupt many arms in medical care. In particular, disciplines using medical imaging modalities, including e.g. radiology but ophthalmology as well, are already confronted with a wide variety of AI implications. In ophthalmologic research, AI has demonstrated promising results limited to specific diseases and imaging tools, respectively. Yet, implementation of AI in clinical routine is not widely spread due to availability, heterogeneity in imaging techniques and AI methods. In order to describe the status quo, this narrational review provides a brief introduction to AI ("what the ophthalmologist needs to know"), followed by an overview of different AI-based applications in ophthalmology and a discussion on future challenges.Abbreviations: Age-related macular degeneration, AMD; Artificial intelligence, AI; Anterior segment OCT, AS-OCT; Coronary artery calcium score, CACS; Convolutional neural network, CNN; Deep convolutional neural network, DCNN; Diabetic retinopathy, DR; Machine learning, ML; Optical coherence tomography, OCT; Retinopathy of prematurity, ROP; Support vector machine, SVM; Thyroid-associated ophthalmopathy, TAO.
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Affiliation(s)
| | - Robert P Reimer
- Department of Diagnostic and Interventional Radiology, University Hospital of Cologne, Köln, Germany
| | - Alexander C Rokohl
- Department of Ophthalmology, University Hospital of Cologne, Köln, Germany
| | - Liliana Caldeira
- Department of Diagnostic and Interventional Radiology, University Hospital of Cologne, Köln, Germany
| | - Ludwig M Heindl
- Department of Ophthalmology, University Hospital of Cologne, Köln, Germany
| | - Nils Große Hokamp
- Department of Diagnostic and Interventional Radiology, University Hospital of Cologne, Köln, Germany
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El-Hajj VG, Gharios M, Edström E, Elmi-Terander A. Artificial Intelligence in Neurosurgery: A Bibliometric Analysis. World Neurosurg 2023; 171:152-158.e4. [PMID: 36566978 DOI: 10.1016/j.wneu.2022.12.087] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 12/16/2022] [Indexed: 12/24/2022]
Abstract
BACKGROUND Artificial intelligence (AI) has the potential to augment clinicians' diagnostic and decision-making capabilities. It is well suited to identify patterns and correlations within data sets and may be applied to identify elements of importance in complex and data-laden areas such as patient selection, diagnostics, treatment, and outcome prediction. The development of modern neurosurgery has been dependent on major technological advances. In line with this, a growing interest is seen in the use of AI to assist in neurosurgical research and enhance neurosurgical practices. METHODS A bibliometric analysis of the 50 most-cited articles alluding to the use of AI in neurosurgery, from inception until July of 2022, was undertaken using the Web of Science database. Statistical analyses were performed on R. RESULTS The citation count ranged from 29 to 159 (mean: 51.9, standard deviation: 24.8), and the top-cited article was a 2018 systematic review published in World Neurosurgery. Most articles were published after 2015 (85%). The United States was the largest contributing country on the list with 22 articles. Four first and last authors, each, had 2 or more publications. Female first and last authorship was attributed to 18% and 0% of the articles, respectively. CONCLUSIONS This review highlights the most-impactful articles pertaining to AI in the field of neurosurgery. Although female authors were significantly underrepresented on the list, their work was at least as impactful as their male peers. Finally, the striking dominance of articles originating from the developed world raises concerns as to the future of AI in attending to the global health crisis.
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Lin YY, Guo WY, Lu CF, Peng SJ, Wu YT, Lee CC. Application of artificial intelligence to stereotactic radiosurgery for intracranial lesions: detection, segmentation, and outcome prediction. J Neurooncol 2023; 161:441-50. [PMID: 36635582 DOI: 10.1007/s11060-022-04234-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 12/30/2022] [Indexed: 01/14/2023]
Abstract
BACKGROUND Rapid evolution of artificial intelligence (AI) prompted its wide application in healthcare systems. Stereotactic radiosurgery served as a good candidate for AI model development and achieved encouraging result in recent years. This article aimed at demonstrating current AI application in radiosurgery. METHODS Literatures published in PubMed during 2010-2022, discussing AI application in stereotactic radiosurgery were reviewed. RESULTS AI algorithms, especially machine learning/deep learning models, have been administered to different aspect of stereotactic radiosurgery. Spontaneous tumor detection and automated lesion delineation or segmentation were two of the promising application, which could be further extended to longitudinal treatment follow-up. Outcome prediction utilized machine learning algorithms with radiomic-based analysis was another well-established application. CONCLUSIONS Stereotactic radiosurgery has taken a lead role in AI development. Current achievement, limitation, and further investigation was summarized in this article.
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Amann J, Vayena E, Ormond KE, Frey D, Madai VI, Blasimme A. Expectations and attitudes towards medical artificial intelligence: A qualitative study in the field of stroke. PLoS One 2023; 18:e0279088. [PMID: 36630325 PMCID: PMC9833517 DOI: 10.1371/journal.pone.0279088] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Accepted: 11/01/2022] [Indexed: 01/12/2023] Open
Abstract
INTRODUCTION Artificial intelligence (AI) has the potential to transform clinical decision-making as we know it. Powered by sophisticated machine learning algorithms, clinical decision support systems (CDSS) can generate unprecedented amounts of predictive information about individuals' health. Yet, despite the potential of these systems to promote proactive decision-making and improve health outcomes, their utility and impact remain poorly understood due to their still rare application in clinical practice. Taking the example of AI-powered CDSS in stroke medicine as a case in point, this paper provides a nuanced account of stroke survivors', family members', and healthcare professionals' expectations and attitudes towards medical AI. METHODS We followed a qualitative research design informed by the sociology of expectations, which recognizes the generative role of individuals' expectations in shaping scientific and technological change. Semi-structured interviews were conducted with stroke survivors, family members, and healthcare professionals specialized in stroke based in Germany and Switzerland. Data was analyzed using a combination of inductive and deductive thematic analysis. RESULTS Based on the participants' deliberations, we identified four presumed roles that medical AI could play in stroke medicine, including an administrative, assistive, advisory, and autonomous role AI. While most participants held positive attitudes towards medical AI and its potential to increase accuracy, speed, and efficiency in medical decision making, they also cautioned that it is not a stand-alone solution and may even lead to new problems. Participants particularly emphasized the importance of relational aspects and raised questions regarding the impact of AI on roles and responsibilities and patients' rights to information and decision-making. These findings shed light on the potential impact of medical AI on professional identities, role perceptions, and the doctor-patient relationship. CONCLUSION Our findings highlight the need for a more differentiated approach to identifying and tackling pertinent ethical and legal issues in the context of medical AI. We advocate for stakeholder and public involvement in the development of AI and AI governance to ensure that medical AI offers solutions to the most pressing challenges patients and clinicians face in clinical care.
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Affiliation(s)
- Julia Amann
- Health Ethics and Policy Lab, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | - Effy Vayena
- Health Ethics and Policy Lab, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | - Kelly E. Ormond
- Health Ethics and Policy Lab, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | - Dietmar Frey
- CLAIM—Charité Lab for AI in Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Vince I. Madai
- CLAIM—Charité Lab for AI in Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany
- QUEST Center for Responsible Research, Berlin Institute of Health (BIH), Charité Universitätsmedizin Berlin, Berlin, Germany
- School of Computing and Digital Technology, Faculty of Computing, Engineering and the Built Environment, Birmingham City University, Birmingham, United Kingdom
| | - Alessandro Blasimme
- Health Ethics and Policy Lab, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
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Kumar S. Neurosurgery in India: Perspective of a Veteran Neurosurgeon. Indian Journal of Neurosurgery 2022. [DOI: 10.1055/s-0042-1760341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Affiliation(s)
- Sushil Kumar
- Ex-Dean Maulana Azad Medical College, New Delhi, India
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14
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Iqbal J, Jahangir K, Mashkoor Y, Sultana N, Mehmood D, Ashraf M, Iqbal A, Hafeez MH. The future of artificial intelligence in neurosurgery: A narrative review. Surg Neurol Int 2022; 13:536. [DOI: 10.25259/sni_877_2022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Accepted: 10/27/2022] [Indexed: 11/19/2022] Open
Abstract
Background:
Artificial intelligence (AI) and machine learning (ML) algorithms are on the tremendous rise for being incorporated into the field of neurosurgery. AI and ML algorithms are different from other technological advances as giving the capability for the computer to learn, reason, and problem-solving skills that a human inherits. This review summarizes the current use of AI in neurosurgery, the challenges that need to be addressed, and what the future holds.
Methods:
A literature review was carried out with a focus on the use of AI in the field of neurosurgery and its future implication in neurosurgical research.
Results:
The online literature on the use of AI in the field of neurosurgery shows the diversity of topics in terms of its current and future implications. The main areas that are being studied are diagnostic, outcomes, and treatment models.
Conclusion:
Wonders of AI in the field of medicine and neurosurgery hold true, yet there are a lot of challenges that need to be addressed before its implications can be seen in the field of neurosurgery from patient privacy, to access to high-quality data and overreliance on surgeons on AI. The future of AI in neurosurgery is pointed toward a patient-centric approach, managing clinical tasks, and helping in diagnosing and preoperative assessment of the patients.
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Affiliation(s)
- Javed Iqbal
- School of Medicine, King Edward Medical University Lahore, Punjab, Pakistan,
| | - Kainat Jahangir
- School of Medicine, Dow University of Health Sciences, Karachi, Sindh, Pakistan,
| | - Yusra Mashkoor
- Department of Internal Medicine, Dow University of Health Sciences, Karachi, Sindh, Pakistan,
| | - Nazia Sultana
- School of Medicine, Government Medical College, Siddipet, Telangana, India,
| | - Dalia Mehmood
- Department of Community Medicine, Fatima Jinnah Medical University, Lahore, Punjab, Pakistan,
| | - Mohammad Ashraf
- Wolfson School of Medicine, University of Glasgow, Scotland, United Kingdom,
| | - Ather Iqbal
- House Officer, Holy Family Hospital Rawalpindi, Punjab, Pakistan,
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15
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Huang BB, Huang J, Swong KN. Natural Language Processing in Spine Surgery: A Systematic Review of Applications, Bias, and Reporting Transparency. World Neurosurg 2022; 167:156-164.e6. [PMID: 36049723 DOI: 10.1016/j.wneu.2022.08.109] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 08/22/2022] [Accepted: 08/23/2022] [Indexed: 10/31/2022]
Abstract
BACKGROUND Natural language processing (NLP) is a discipline of machine learning concerned with the analysis of language and text. Although NLP has been applied to various forms of clinical text, the applications and utility of NLP in spine surgery remain poorly characterized. Here, we systematically reviewed studies that use NLP for spine surgery applications, and analyzed applications, bias, and reporting transparency of the studies. METHODS We performed a literature search using the PubMed, Scopus, and Embase databases. Data extraction was performed after appropriate screening. The risk of bias and reporting quality were assessed using the PROBAST and TRIPOD tools. RESULTS A total of 12 full-text articles were included. The most common diseases represented include spondylolisthesis (25%), scoliosis (17%), and lumbar disk herniation (17%). The most common procedures included spinal fusion (42%), imaging (e.g. magnetic resonance, X-ray) (25%), and scoliosis correction (17%). Reported outcomes were diverse and included incidental durotomy, venous thromboembolism, and the tone of social media posts regarding scoliosis surgery. Common sources of bias identified included the use of older methods that do not capture the nuance of a text, and not using a prespecified or standard outcome measure when evaluating NLP methods. CONCLUSIONS Although the application of NLP to spine surgery is expanding, current studies face limitations and none are indicated as ready for clinical use. Thus, for future studies we recommend an emphasis on transparent reporting and collaboration with NLP experts to incorporate the latest developments to improve models and contribute to further innovation.
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Affiliation(s)
- Bonnie B Huang
- Department of Neurological Surgery, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Jonathan Huang
- Department of Neurological Surgery, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Kevin N Swong
- Department of Neurological Surgery, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA.
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16
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Ghannam MM, Davies JM. Application of Big Data in Vascular Neurosurgery. Neurosurg Clin N Am 2022; 33:469-482. [DOI: 10.1016/j.nec.2022.06.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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17
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Singh R, Wang K, Qureshi MB, Rangel IC, Brown NJ, Shahrestani S, Gottfried ON, Patel NP, Bydon M. Robotics in neurosurgery: Current prevalence and future directions. Surg Neurol Int 2022; 13:373. [PMID: 36128120 PMCID: PMC9479589 DOI: 10.25259/sni_522_2022] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 07/31/2022] [Indexed: 12/03/2022] Open
Abstract
Background: The first instance of a robotic-assisted surgery occurred in neurosurgery; however, it is now more common in other fields such as urology and gynecology. This study aims to characterize the prevalence of robotic surgery among current neurosurgery programs as well as identify trends in clinical trials pertaining to robotic neurosurgery. Methods: Each institution’s website was analyzed for the mention of a robotic neurosurgery program and procedures. The future potential of robotics in neurosurgery was assessed by searching for current clinical trials pertaining to neurosurgical robotic surgery. Results: Of the top 100 programs, 30 offer robotic cranial and 40 offer robotic spinal surgery. No significant differences were observed with robotic surgical offerings between geographic regions in the US. Larger programs (faculty size 16 or over) had 20 of the 30 robotic cranial programs (66.6%), whereas 21 of the 40 robotic spinal programs (52.5%) were at larger programs. An initial search of clinical trials revealed 223 studies, of which only 13 pertained to robotic neurosurgery. Spinal fixation was the most common intervention (six studies), followed by Deep Brain Stimulation (DBS, two studies), Cochlear implants (two studies), laser ablation (LITT, one study), and endovascular embolization (one study). Most studies had industry sponsors (9/13 studies), while only five studies had hospital sponsors. Conclusion: Robotic neurosurgery is still in its infancy with less than half of the top programs offering robotic procedures. Future directions for robotics in neurosurgery appear to be focused on increased automation of stereotactic procedures such as DBS and LITT and robot-assisted spinal surgery.
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Affiliation(s)
- Rohin Singh
- Alix School of Medicine, Mayo Clinic, Scottsdale,
| | - Kendra Wang
- Department of Osteopathic Medicine, A. T. Still University, Mesa,
| | | | | | | | | | | | | | - Mohamad Bydon
- Mayo Clinic Neuro-Informatics Laboratory, Rochester, United States
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18
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Dundar TT, Yurtsever I, Pehlivanoglu MK, Yildiz U, Eker A, Demir MA, Mutluer AS, Tektaş R, Kazan MS, Kitis S, Gokoglu A, Dogan I, Duru N. Machine Learning-Based Surgical Planning for Neurosurgery: Artificial Intelligent Approaches to the Cranium. Front Surg 2022; 9:863633. [PMID: 35574559 PMCID: PMC9099011 DOI: 10.3389/fsurg.2022.863633] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Accepted: 03/28/2022] [Indexed: 01/22/2023] Open
Abstract
ObjectivesArtificial intelligence (AI) applications in neurosurgery have an increasing momentum as well as the growing number of implementations in the medical literature. In recent years, AI research define a link between neuroscience and AI. It is a connection between knowing and understanding the brain and how to simulate the brain. The machine learning algorithms, as a subset of AI, are able to learn with experiences, perform big data analysis, and fulfill human-like tasks. Intracranial surgical approaches that have been defined, disciplined, and developed in the last century have become more effective with technological developments. We aimed to define individual-safe, intracranial approaches by introducing functional anatomical structures and pathological areas to artificial intelligence.MethodsPreoperative MR images of patients with deeply located brain tumors were used for planning. Intracranial arteries, veins, and neural tracts are listed and numbered. Voxel values of these selected regions in cranial MR sequences were extracted and labeled. Tumor tissue was segmented as the target. Q-learning algorithm which is a model-free reinforcement learning algorithm was run on labeled voxel values (on optimal paths extracted from the new heuristic-based path planning algorithm), then the algorithm was assigned to list the cortico-tumoral pathways that aim to remove the maximum tumor tissue and in the meantime that functional anatomical tissues will be least affected.ResultsThe most suitable cranial entry areas were found with the artificial intelligence algorithm. Cortico-tumoral pathways were revealed using Q-learning from these optimal points.ConclusionsAI will make a significant contribution to the positive outcomes as its use in both preoperative surgical planning and intraoperative technique equipment assisted neurosurgery, its use increased.
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Affiliation(s)
- Tolga Turan Dundar
- Bezmiâlem Vakif Üniversitesi, Istanbul, Turkey
- *Correspondence: Tolga Turan Dundar
| | | | | | | | | | | | | | | | | | | | | | | | - Nevcihan Duru
- Kocaeli Health and Technology University, Başiskele, Turkey
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19
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Zoli M, Daniele B, Giovanni R, Teresa S, Cesare Z, Giuseppe Maria DP. Young Neurosurgeons and Technology: Survey of Young Neurosurgeons Section of Italian Society of Neurosurgery (SINch). World Neurosurg 2022; 162:e436-e456. [DOI: 10.1016/j.wneu.2022.03.022] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Revised: 03/05/2022] [Accepted: 03/07/2022] [Indexed: 11/25/2022]
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20
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Kuribara T, Akiyama Y, Mikami T, Komatsu K, Kimura Y, Takahashi Y, Sakashita K, Chiba R, Mikuni N. Macrohistory of Moyamoya Disease Analyzed Using Artificial Intelligence. Cerebrovasc Dis 2022; 51:413-426. [PMID: 35104814 DOI: 10.1159/000520099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Accepted: 10/06/2021] [Indexed: 11/19/2022] Open
Abstract
INTRODUCTION Moyamoya disease is characterized by progressive stenotic changes in the terminal segment of the internal carotid artery and the development of abnormal vascular networks called moyamoya vessels. The objective of this review was to provide a holistic view of the epidemiology, etiology, clinical findings, treatment, and pathogenesis of moyamoya disease. A literature search was performed in PubMed using the term "moyamoya disease," for articles published until 2021. RESULTS Artificial intelligence (AI) clustering was used to classify the articles into 5 clusters: (1) pathophysiology (23.5%); (2) clinical background (37.3%); (3) imaging (13.2%); (4) treatment (17.3%); and (5) genetics (8.7%). Many articles in the "clinical background" cluster were published from the 1970s. However, in the "treatment" and "genetics" clusters, the articles were published from the 2010s through 2021. In 2011, it was confirmed that a gene called Ringin protein 213 (RNF213) is a susceptibility gene for moyamoya disease. Since then, tremendous progress in genomic, transcriptomic, and epigenetic profiling (e.g., methylation profiling) has resulted in new concepts for classifying moyamoya disease. Our literature survey revealed that the pathogenesis involves aberrations of multiple signaling pathways through genetic mutations and altered gene expression. CONCLUSION We analyzed the content vectors in abstracts using AI, and reviewed the pathophysiology, clinical background, radiological features, treatments, and genetic peculiarity of moyamoya disease.
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Affiliation(s)
| | - Yukinori Akiyama
- Department of Neurosurgery, Sapporo Medical University, Sapporo, Japan
| | - Takeshi Mikami
- Department of Neurosurgery, Sapporo Medical University, Sapporo, Japan
| | - Katsuya Komatsu
- Department of Neurosurgery, Sapporo Medical University, Sapporo, Japan
| | - Yusuke Kimura
- Department of Neurosurgery, Sapporo Medical University, Sapporo, Japan
| | | | - Kyoya Sakashita
- Department of Neurosurgery, Sapporo Medical University, Sapporo, Japan
| | - Ryohei Chiba
- Department of Neurosurgery, Sapporo Medical University, Sapporo, Japan
| | - Nobuhiro Mikuni
- Department of Neurosurgery, Sapporo Medical University, Sapporo, Japan
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21
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Mensah E, Pringle C, Roberts G, Gurusinghe N, Golash A, Alalade AF. Deep Learning in the Management of Intracranial Aneurysms and Cerebrovascular Diseases: A Review of the Current Literature. World Neurosurg 2022; 161:39-45. [DOI: 10.1016/j.wneu.2022.02.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 01/31/2022] [Accepted: 02/01/2022] [Indexed: 01/10/2023]
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22
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Chen W, Li X, Ma L, Li D. Enhancing Robustness of Machine Learning Integration With Routine Laboratory Blood Tests to Predict Inpatient Mortality After Intracerebral Hemorrhage. Front Neurol 2022; 12:790682. [PMID: 35046885 PMCID: PMC8761736 DOI: 10.3389/fneur.2021.790682] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Accepted: 12/07/2021] [Indexed: 02/05/2023] Open
Abstract
Objective: The accurate evaluation of outcomes at a personalized level in patients with intracerebral hemorrhage (ICH) is critical clinical implications. This study aims to evaluate how machine learning integrates with routine laboratory tests and electronic health records (EHRs) data to predict inpatient mortality after ICH. Methods: In this machine learning-based prognostic study, we included 1,835 consecutive patients with acute ICH between October 2010 and December 2018. The model building process incorporated five pre-implant ICH score variables (clinical features) and 13 out of 59 available routine laboratory parameters. We assessed model performance according to a range of learning metrics, such as the mean area under the receiver operating characteristic curve [AUROC]. We also used the Shapley additive explanation algorithm to explain the prediction model. Results: Machine learning models using laboratory data achieved AUROCs of 0.71–0.82 in a split-by-year development/testing scheme. The non-linear eXtreme Gradient Boosting model yielded the highest prediction accuracy. In the held-out validation set of development cohort, the predictive model using comprehensive clinical and laboratory parameters outperformed those using clinical alone in predicting in-hospital mortality (AUROC [95% bootstrap confidence interval], 0.899 [0.897–0.901] vs. 0.875 [0.872–0.877]; P <0.001), with over 81% accuracy, sensitivity, and specificity. We observed similar performance in the testing set. Conclusions: Machine learning integrated with routine laboratory tests and EHRs could significantly promote the accuracy of inpatient ICH mortality prediction. This multidimensional composite prediction strategy might become an intelligent assistive prediction for ICH risk reclassification and offer an example for precision medicine.
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Affiliation(s)
- Wei Chen
- Department of Neurosurgery, West China Hospital of Sichuan University, Chengdu, China.,West China Biomedical Big Data Center, West China Hospital of Sichuan University, Chengdu, China.,Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Xiangkui Li
- West China Biomedical Big Data Center, West China Hospital of Sichuan University, Chengdu, China.,Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Lu Ma
- Department of Neurosurgery, West China Hospital of Sichuan University, Chengdu, China
| | - Dong Li
- West China Biomedical Big Data Center, West China Hospital of Sichuan University, Chengdu, China.,Med-X Center for Informatics, Sichuan University, Chengdu, China.,Division of Hospital Medicine, Emory School of Medicine, Atlanta, GA, United States
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23
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Bibi Farouk ZI, Jiang S, Yang Z, Umar A. A Brief Insight on Magnetic Resonance Conditional Neurosurgery Robots. Ann Biomed Eng 2022; 50:138-156. [PMID: 34993701 DOI: 10.1007/s10439-021-02891-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 11/08/2021] [Indexed: 12/19/2022]
Abstract
The brain is a delicate organ in the human body that requires extreme care. Brain-related diseases are unavoidable. Perse, neurosurgery is a complicated procedure that demands high precision and accuracy. Developing a surgical robot is a complex task. To date, there are only a handful of neurosurgery robots in the market that distinctly undergo clinical procedures. These robots have exorbitant cost that hinders the utmost care progress in the area as they are unaffordable. This paper looked at the historical perspective and presented insight literature of the magnetic resonance conditional stereotactic neurosurgery robots that find their ways in clinics, abandoning research projects and promising research yet to undergo clinical use. In addition, the study also gives a thorough insight into the advantage of magnetic resonance imaging modalities and magnetic resonance conditional robots and the future challenges in automation use. Image compatibility test data and accuracy results are also examined because they guarantee that these systems work correctly in particular imaging settings. The primary differences between these systems include actuation and control technologies, construction materials, and the degree of freedom. Thus, one system has an advantage over the other.
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Affiliation(s)
- Z I Bibi Farouk
- Mechanical Engineering Department, Tianjin University, No. 135, Yaguan Road, Haihe Education Park, Jinnan District, Tianjin, 300354, China
| | - Shan Jiang
- Mechanical Engineering Department, Tianjin University, No. 135, Yaguan Road, Haihe Education Park, Jinnan District, Tianjin, 300354, China.
| | - Zhiyong Yang
- Mechanical Engineering Department, Tianjin University, No. 135, Yaguan Road, Haihe Education Park, Jinnan District, Tianjin, 300354, China
| | - Abubakar Umar
- Mechanical Engineering Department, Hebei University of Technology, Tianjin, China
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24
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Das D, Mahanta LB. AIM in Neurology. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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25
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Huang J, Shlobin NA, DeCuypere M, Lam SK. Deep Learning for Outcome Prediction in Neurosurgery: A Systematic Review of Design, Reporting, and Reproducibility. Neurosurgery 2022; 90:16-38. [PMID: 34982868 DOI: 10.1227/neu.0000000000001736] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Accepted: 08/18/2021] [Indexed: 02/06/2023] Open
Abstract
Deep learning (DL) is a powerful machine learning technique that has increasingly been used to predict surgical outcomes. However, the large quantity of data required and lack of model interpretability represent substantial barriers to the validity and reproducibility of DL models. The objective of this study was to systematically review the characteristics of DL studies involving neurosurgical outcome prediction and to assess their bias and reporting quality. Literature search using the PubMed, Scopus, and Embase databases identified 1949 records of which 35 studies were included. Of these, 32 (91%) developed and validated a DL model while 3 (9%) validated a pre-existing model. The most commonly represented subspecialty areas were oncology (16 of 35, 46%), spine (8 of 35, 23%), and vascular (6 of 35, 17%). Risk of bias was low in 18 studies (51%), unclear in 5 (14%), and high in 12 (34%), most commonly because of data quality deficiencies. Adherence to transparent reporting of a multivariable prediction model for individual prognosis or diagnosis reporting standards was low, with a median of 12 transparent reporting of a multivariable prediction model for individual prognosis or diagnosis items (39%) per study not reported. Model transparency was severely limited because code was provided in only 3 studies (9%) and final models in 2 (6%). With the exception of public databases, no study data sets were readily available. No studies described DL models as ready for clinical use. The use of DL for neurosurgical outcome prediction remains nascent. Lack of appropriate data sets poses a major concern for bias. Although studies have demonstrated promising results, greater transparency in model development and reporting is needed to facilitate reproducibility and validation.
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Affiliation(s)
- Jonathan Huang
- Ann and Robert H. Lurie Children's Hospital, Division of Pediatric Neurosurgery, Department of Neurological Surgery, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
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26
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Buchlak QD, Esmaili N, Bennett C, Farrokhi F. Natural Language Processing Applications in the Clinical Neurosciences: A Machine Learning Augmented Systematic Review. Acta Neurochir Suppl 2022; 134:277-289. [PMID: 34862552 DOI: 10.1007/978-3-030-85292-4_32] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
Natural language processing (NLP), a domain of artificial intelligence (AI) that models human language, has been used in medicine to automate diagnostics, detect adverse events, support decision making and predict clinical outcomes. However, applications to the clinical neurosciences appear to be limited. NLP has matured with the implementation of deep transformer models (e.g., XLNet, BERT, T5, and RoBERTa) and transfer learning. The objectives of this study were to (1) systematically review NLP applications in the clinical neurosciences, and (2) explore NLP analysis to facilitate literature synthesis, providing clear examples to demonstrate the potential capabilities of these technologies for a clinical audience. Our NLP analysis consisted of keyword identification, text summarization and document classification. A total of 48 articles met inclusion criteria. NLP has been applied in the clinical neurosciences to facilitate literature synthesis, data extraction, patient identification, automated clinical reporting and outcome prediction. The number of publications applying NLP has increased rapidly over the past five years. Document classifiers trained to differentiate included and excluded articles demonstrated moderate performance (XLNet AUC = 0.66, BERT AUC = 0.59, RoBERTa AUC = 0.62). The T5 transformer model generated acceptable abstract summaries. The application of NLP has the potential to enhance research and practice in the clinical neurosciences.
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Affiliation(s)
- Quinlan D Buchlak
- School of Medicine, The University of Notre Dame Australia, Sydney, NSW, Australia.
| | - Nazanin Esmaili
- School of Medicine, The University of Notre Dame Australia, Sydney, NSW, Australia
- Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW, Australia
| | - Christine Bennett
- School of Medicine, The University of Notre Dame Australia, Sydney, NSW, Australia
| | - Farrokh Farrokhi
- Neuroscience Institute, Virginia Mason Medical Center, Seattle, WA, USA
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27
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Abstract
Predictive analytics are increasingly reported by clinicians. These tools aim to improve patient outcomes in terms of quality, safety, and efficiency. However, deploying predictive analytics in clinical practice remains challenging today. We highlight several advantages and disadvantages of the application of predictive analytics in clinical practice. To flourish and reach its potential, predictive analytics need data that is of adequate quantity and quality, ideally tailored to clinical scenarios in equipoise regarding optimal management. Adequate reporting of predictive analytic tools is incumbent for uptake into clinical workflows. At least for now, the clinicians' knowledge, experience, and vigilance remain imperative for applying predictive analytics in clinical practice.
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Affiliation(s)
- Hendrik-Jan Mijderwijk
- Department of Neurosurgery, Heinrich Heine University, Medical Faculty, Düsseldorf, Germany.
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28
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Jimenez AE, Feghali J, Schilling AT, Azad TD. Deployment of Clinical Prediction Models: A Practical Guide to Nomograms and Online Calculators. Acta Neurochir Suppl 2021; 134:101-108. [PMID: 34862533 DOI: 10.1007/978-3-030-85292-4_13] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The use of predictive models within neurosurgery is increasing and many models described in published journal articles are made available to readers in formats such as nomograms and online calculators. The present chapter details a step-by-step methodology with accompanying R code that may be used to implement models both in the form of traditional nomograms and as open-access, online calculators through RStudio's Shinyapps. The chapter assumes a basic understanding of predictive modeling in R and utilizes open-access files created by the Machine Intelligence in Clinical Neuroscience (MICN) Lab (Department of Neurosurgery and the Clinical Neuroscience Center of the University Hospital Zurich). When implemented correctly, tools such as nomograms and predictive calculators have the potential to improve user understanding of the underlying statistical models, facilitate broader adoption, and to streamline the eventual use of such models in clinical settings.
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Affiliation(s)
- Adrian E Jimenez
- Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - James Feghali
- Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Andrew T Schilling
- Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Tej D Azad
- Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
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29
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Affiliation(s)
- Mervyn J R Lim
- Division of Neurosurgery University Surgical Centre National University Hospital Singapore
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30
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Williams S, Layard Horsfall H, Funnell JP, Hanrahan JG, Khan DZ, Muirhead W, Stoyanov D, Marcus HJ. Artificial Intelligence in Brain Tumour Surgery-An Emerging Paradigm. Cancers (Basel) 2021; 13:cancers13195010. [PMID: 34638495 PMCID: PMC8508169 DOI: 10.3390/cancers13195010] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Revised: 10/02/2021] [Accepted: 10/03/2021] [Indexed: 01/01/2023] Open
Abstract
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.
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Affiliation(s)
- Simon Williams
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK; (H.L.H.); (J.P.F.); (J.G.H.); (D.Z.K.); (W.M.); (H.J.M.)
- Wellcome/Engineering and Physical Sciences Research Council (EPSRC) Centre for Interventional and Surgical Sciences (WEISS), London W1W 7TY, UK;
- Correspondence:
| | - Hugo Layard Horsfall
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK; (H.L.H.); (J.P.F.); (J.G.H.); (D.Z.K.); (W.M.); (H.J.M.)
- Wellcome/Engineering and Physical Sciences Research Council (EPSRC) Centre for Interventional and Surgical Sciences (WEISS), London W1W 7TY, UK;
| | - Jonathan P. Funnell
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK; (H.L.H.); (J.P.F.); (J.G.H.); (D.Z.K.); (W.M.); (H.J.M.)
- Wellcome/Engineering and Physical Sciences Research Council (EPSRC) Centre for Interventional and Surgical Sciences (WEISS), London W1W 7TY, UK;
| | - John G. Hanrahan
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK; (H.L.H.); (J.P.F.); (J.G.H.); (D.Z.K.); (W.M.); (H.J.M.)
- Wellcome/Engineering and Physical Sciences Research Council (EPSRC) Centre for Interventional and Surgical Sciences (WEISS), London W1W 7TY, UK;
| | - Danyal Z. Khan
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK; (H.L.H.); (J.P.F.); (J.G.H.); (D.Z.K.); (W.M.); (H.J.M.)
- Wellcome/Engineering and Physical Sciences Research Council (EPSRC) Centre for Interventional and Surgical Sciences (WEISS), London W1W 7TY, UK;
| | - William Muirhead
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK; (H.L.H.); (J.P.F.); (J.G.H.); (D.Z.K.); (W.M.); (H.J.M.)
- Wellcome/Engineering and Physical Sciences Research Council (EPSRC) Centre for Interventional and Surgical Sciences (WEISS), London W1W 7TY, UK;
| | - Danail Stoyanov
- Wellcome/Engineering and Physical Sciences Research Council (EPSRC) Centre for Interventional and Surgical Sciences (WEISS), London W1W 7TY, UK;
| | - Hani J. Marcus
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK; (H.L.H.); (J.P.F.); (J.G.H.); (D.Z.K.); (W.M.); (H.J.M.)
- Wellcome/Engineering and Physical Sciences Research Council (EPSRC) Centre for Interventional and Surgical Sciences (WEISS), London W1W 7TY, UK;
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Coppola F, Faggioni L, Gabelloni M, De Vietro F, Mendola V, Cattabriga A, Cocozza MA, Vara G, Piccinino A, Lo Monaco S, Pastore LV, Mottola M, Malavasi S, Bevilacqua A, Neri E, Golfieri R. Human, All Too Human? An All-Around Appraisal of the "Artificial Intelligence Revolution" in Medical Imaging. Front Psychol 2021; 12:710982. [PMID: 34650476 PMCID: PMC8505993 DOI: 10.3389/fpsyg.2021.710982] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Accepted: 09/02/2021] [Indexed: 12/22/2022] Open
Abstract
Artificial intelligence (AI) has seen dramatic growth over the past decade, evolving from a niche super specialty computer application into a powerful tool which has revolutionized many areas of our professional and daily lives, and the potential of which seems to be still largely untapped. The field of medicine and medical imaging, as one of its various specialties, has gained considerable benefit from AI, including improved diagnostic accuracy and the possibility of predicting individual patient outcomes and options of more personalized treatment. It should be noted that this process can actively support the ongoing development of advanced, highly specific treatment strategies (e.g., target therapies for cancer patients) while enabling faster workflow and more efficient use of healthcare resources. The potential advantages of AI over conventional methods have made it attractive for physicians and other healthcare stakeholders, raising much interest in both the research and the industry communities. However, the fast development of AI has unveiled its potential for disrupting the work of healthcare professionals, spawning concerns among radiologists that, in the future, AI may outperform them, thus damaging their reputations or putting their jobs at risk. Furthermore, this development has raised relevant psychological, ethical, and medico-legal issues which need to be addressed for AI to be considered fully capable of patient management. The aim of this review is to provide a brief, hopefully exhaustive, overview of the state of the art of AI systems regarding medical imaging, with a special focus on how AI and the entire healthcare environment should be prepared to accomplish the goal of a more advanced human-centered world.
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Affiliation(s)
- Francesca Coppola
- Department of Radiology, IRCCS Azienda Ospedaliero Universitaria di Bologna, Bologna, Italy
- SIRM Foundation, Italian Society of Medical and Interventional Radiology, Milan, Italy
| | - Lorenzo Faggioni
- Academic Radiology, Department of Translational Research, University of Pisa, Pisa, Italy
| | - Michela Gabelloni
- Academic Radiology, Department of Translational Research, University of Pisa, Pisa, Italy
| | - Fabrizio De Vietro
- Academic Radiology, Department of Translational Research, University of Pisa, Pisa, Italy
| | - Vincenzo Mendola
- Academic Radiology, Department of Translational Research, University of Pisa, Pisa, Italy
| | - Arrigo Cattabriga
- Department of Radiology, IRCCS Azienda Ospedaliero Universitaria di Bologna, Bologna, Italy
| | - Maria Adriana Cocozza
- Department of Radiology, IRCCS Azienda Ospedaliero Universitaria di Bologna, Bologna, Italy
| | - Giulio Vara
- Department of Radiology, IRCCS Azienda Ospedaliero Universitaria di Bologna, Bologna, Italy
| | - Alberto Piccinino
- Department of Radiology, IRCCS Azienda Ospedaliero Universitaria di Bologna, Bologna, Italy
| | - Silvia Lo Monaco
- Department of Radiology, IRCCS Azienda Ospedaliero Universitaria di Bologna, Bologna, Italy
| | - Luigi Vincenzo Pastore
- Department of Radiology, IRCCS Azienda Ospedaliero Universitaria di Bologna, Bologna, Italy
| | - Margherita Mottola
- Department of Computer Science and Engineering, University of Bologna, Bologna, Italy
| | - Silvia Malavasi
- Department of Computer Science and Engineering, University of Bologna, Bologna, Italy
| | - Alessandro Bevilacqua
- Department of Computer Science and Engineering, University of Bologna, Bologna, Italy
| | - Emanuele Neri
- SIRM Foundation, Italian Society of Medical and Interventional Radiology, Milan, Italy
- Academic Radiology, Department of Translational Research, University of Pisa, Pisa, Italy
| | - Rita Golfieri
- Department of Radiology, IRCCS Azienda Ospedaliero Universitaria di Bologna, Bologna, Italy
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Shlobin NA, Moher D. Commentary: Reporting Guidelines for Studies on Artificial Intelligence: What Neurosurgeons Should Know. Neurosurgery 2021; 89:E316-E317. [PMID: 34432029 DOI: 10.1093/neuros/nyab331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 07/28/2021] [Indexed: 11/14/2022] Open
Affiliation(s)
- Nathan A Shlobin
- Department of Neurological Surgery, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA.,Division of Pediatric Neurosurgery, Lurie Children's Hospital, Chicago, Illinois, USA
| | - David Moher
- Centre for Journalology, Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada.,School of Epidemiology and Public Health, Faculty of Medicine, University of Ottawa, Ottawa, Canada
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Abstract
Neurosurgeons receive extensive and lengthy training to equip themselves with various technical skills, and neurosurgery require a great deal of pre-, intra- and postoperative clinical data collection, decision making, care and recovery. The last decade has seen a significant increase in the importance of artificial intelligence (AI) in neurosurgery. AI can provide a great promise in neurosurgery by complementing neurosurgeons' skills to provide the best possible interventional and noninterventional care for patients by enhancing diagnostic and prognostic outcomes in clinical treatment and help neurosurgeons with decision making during surgical interventions to improve patient outcomes. Furthermore, AI is playing a pivotal role in the production, processing and storage of clinical and experimental data. AI usage in neurosurgery can also reduce the costs associated with surgical care and provide high-quality healthcare to a broader population. Additionally, AI and neurosurgery can build a symbiotic relationship where AI helps to push the boundaries of neurosurgery, and neurosurgery can help AI to develop better and more robust algorithms. This review explores the role of AI in interventional and noninterventional aspects of neurosurgery during pre-, intra- and postoperative care, such as diagnosis, clinical decision making, surgical operation, prognosis, data acquisition, and research within the neurosurgical arena.
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Affiliation(s)
- Mohammad Mofatteh
- Sir William Dunn School of Pathology, Medical Sciences Division, University of Oxford, South Parks Road, Oxford OX1 3RE, United Kingdom
- Lincoln College, University of Oxford, Turl Street, Oxford OX1 3DR, United Kingdom
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Edwards CA, Goyal A, Rusheen AE, Kouzani AZ, Lee KH. DeepNavNet: Automated Landmark Localization for Neuronavigation. Front Neurosci 2021; 15:670287. [PMID: 34220429 PMCID: PMC8245762 DOI: 10.3389/fnins.2021.670287] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2021] [Accepted: 05/25/2021] [Indexed: 11/13/2022] Open
Abstract
Functional neurosurgery requires neuroimaging technologies that enable precise navigation to targeted structures. Insufficient image resolution of deep brain structures necessitates alignment to a brain atlas to indirectly locate targets within preoperative magnetic resonance imaging (MRI) scans. Indirect targeting through atlas-image registration is innately imprecise, increases preoperative planning time, and requires manual identification of anterior and posterior commissure (AC and PC) reference landmarks which is subject to human error. As such, we created a deep learning-based pipeline that consistently and automatically locates, with submillimeter accuracy, the AC and PC anatomical landmarks within MRI volumes without the need for an atlas. Our novel deep learning pipeline (DeepNavNet) regresses from MRI scans to heatmap volumes centered on AC and PC anatomical landmarks to extract their three-dimensional coordinates with submillimeter accuracy. We collated and manually labeled the location of AC and PC points in 1128 publicly available MRI volumes used for training, validation, and inference experiments. Instantiations of our DeepNavNet architecture, as well as a baseline model for reference, were evaluated based on the average 3D localization errors for the AC and PC points across 311 MRI volumes. Our DeepNavNet model significantly outperformed a baseline and achieved a mean 3D localization error of 0.79 ± 0.33 mm and 0.78 ± 0.33 mm between the ground truth and the detected AC and PC points, respectively. In conclusion, the DeepNavNet model pipeline provides submillimeter accuracy for localizing AC and PC anatomical landmarks in MRI volumes, enabling improved surgical efficiency and accuracy.
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Affiliation(s)
- Christine A Edwards
- School of Engineering, Deakin University, Geelong, VIC, Australia.,Department of Neurologic Surgery, Mayo Clinic, Rochester, MN, United States.,Mayo Clinic Graduate School of Biomedical Sciences, Mayo Clinic, Rochester, MN, United States
| | - Abhinav Goyal
- Department of Neurologic Surgery, Mayo Clinic, Rochester, MN, United States.,Mayo Clinic College of Medical Scientist Training Program, Mayo Clinic, Rochester, MN, United States
| | - Aaron E Rusheen
- Department of Neurologic Surgery, Mayo Clinic, Rochester, MN, United States.,Mayo Clinic College of Medical Scientist Training Program, Mayo Clinic, Rochester, MN, United States
| | - Abbas Z Kouzani
- School of Engineering, Deakin University, Geelong, VIC, Australia
| | - Kendall H Lee
- Department of Neurologic Surgery, Mayo Clinic, Rochester, MN, United States.,Mayo Clinic Graduate School of Biomedical Sciences, Mayo Clinic, Rochester, MN, United States.,Mayo Clinic College of Medical Scientist Training Program, Mayo Clinic, Rochester, MN, United States.,Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, United States
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