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Warren BE, Bilbily A, Gichoya JW, Conway A, Li B, Fawzy A, Barragán C, Jaberi A, Mafeld S. An Introductory Guide to Artificial Intelligence in Interventional Radiology: Part 1 Foundational Knowledge. Can Assoc Radiol J 2024; 75:558-567. [PMID: 38445497 DOI: 10.1177/08465371241236376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/07/2024] Open
Abstract
Artificial intelligence (AI) is rapidly evolving and has transformative potential for interventional radiology (IR) clinical practice. However, formal training in AI may be limited for many clinicians and therefore presents a challenge for initial implementation and trust in AI. An understanding of the foundational concepts in AI may help familiarize the interventional radiologist with the field of AI, thus facilitating understanding and participation in the development and deployment of AI. A pragmatic classification system of AI based on the complexity of the model may guide clinicians in the assessment of AI. Finally, the current state of AI in IR and the patterns of implementation are explored (pre-procedural, intra-procedural, and post-procedural).
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Affiliation(s)
- Blair Edward Warren
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
- Joint Department of Medical Imaging, University Health Network, Toronto, ON, Canada
| | - Alexander Bilbily
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
- 16 Bit Inc., Toronto, ON, Canada
- Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | | | - Aaron Conway
- Prince Charles Hospital, Queensland University of Technology, Brisbane, QLD, Australia
| | - Ben Li
- Division of Vascular Surgery, Department of Surgery, University of Toronto, Toronto, ON, Canada
| | - Aly Fawzy
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
| | - Camilo Barragán
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
- Joint Department of Medical Imaging, University Health Network, Toronto, ON, Canada
| | - Arash Jaberi
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
- Joint Department of Medical Imaging, University Health Network, Toronto, ON, Canada
| | - Sebastian Mafeld
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
- Joint Department of Medical Imaging, University Health Network, Toronto, ON, Canada
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Nishi H, Cancelliere NM, Rustici A, Charbonnier G, Chan V, Spears J, Marotta TR, Mendes Pereira V. Deep learning-based cerebral aneurysm segmentation and morphological analysis with three-dimensional rotational angiography. J Neurointerv Surg 2024; 16:197-203. [PMID: 37192786 DOI: 10.1136/jnis-2023-020192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Accepted: 04/14/2023] [Indexed: 05/18/2023]
Abstract
BACKGROUND The morphological assessment of cerebral aneurysms based on cerebral angiography is an essential step when planning strategy and device selection in endovascular treatment, but manual evaluation by human raters only has moderate interrater/intrarater reliability. METHODS We collected data for 889 cerebral angiograms from consecutive patients with suspected cerebral aneurysms at our institution from January 2017 to October 2021. The automatic morphological analysis model was developed on the derivation cohort dataset consisting of 388 scans with 437 aneurysms, and the performance of the model was tested on the validation cohort dataset consisting of 96 scans with 124 aneurysms. Five clinically important parameters were automatically calculated by the model: aneurysm volume, maximum aneurysm size, neck size, aneurysm height, and aspect ratio. RESULTS On the validation cohort dataset the average aneurysm size was 7.9±4.6 mm. The proposed model displayed high segmentation accuracy with a mean Dice similarity index of 0.87 (median 0.93). All the morphological parameters were significantly correlated with the reference standard (all P<0.0001; Pearson correlation analysis). The difference in the maximum aneurysm size between the model prediction and reference standard was 0.5±0.7 mm (mean±SD). The difference in neck size between the model prediction and reference standard was 0.8±1.7 mm (mean±SD). CONCLUSION The automatic aneurysm analysis model based on angiography data exhibited high accuracy for evaluating the morphological characteristics of cerebral aneurysms.
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Affiliation(s)
- Hidehisa Nishi
- Department of Surgery, Division of Neurosurgery, St Michael's Hospital, Toronto, Ontario, Canada
- RADIS Lab, Li Ka Shing Knowledge Institute, St Michael's Hospital, Toronto, Ontario, Canada
| | - Nicole M Cancelliere
- Department of Surgery, Division of Neurosurgery, St Michael's Hospital, Toronto, Ontario, Canada
- RADIS Lab, Li Ka Shing Knowledge Institute, St Michael's Hospital, Toronto, Ontario, Canada
| | - Ariana Rustici
- RADIS Lab, Li Ka Shing Knowledge Institute, St Michael's Hospital, Toronto, Ontario, Canada
| | - Guillaume Charbonnier
- RADIS Lab, Li Ka Shing Knowledge Institute, St Michael's Hospital, Toronto, Ontario, Canada
| | - Vanessa Chan
- RADIS Lab, Li Ka Shing Knowledge Institute, St Michael's Hospital, Toronto, Ontario, Canada
| | - Julian Spears
- Department of Surgery, Division of Neurosurgery, St Michael's Hospital, Toronto, Ontario, Canada
| | - Thomas R Marotta
- Department of Medical Imaging, St Michael's Hospital, Toronto, Ontario, Canada
| | - Vitor Mendes Pereira
- Department of Surgery, Division of Neurosurgery, St Michael's Hospital, Toronto, Ontario, Canada
- RADIS Lab, Li Ka Shing Knowledge Institute, St Michael's Hospital, Toronto, Ontario, Canada
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Kamphuis MJ, Timmins KM, Kuijf HJ, de Graaf EKL, Rinkel GJE, Vergouwen MDI, van der Schaaf IC. Three-Dimensional Morphological Change of Intracranial Aneurysms Before and Around Rupture. Neurosurgery 2024:00006123-990000000-01009. [PMID: 38169305 DOI: 10.1227/neu.0000000000002812] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 11/13/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND AND OBJECTIVES Patients with an unruptured intracranial aneurysm often undergo periodic imaging to detect potential aneurysm growth, which is associated with an increased rupture risk. Because prediction of rupture based on growth is moderate, morphological changes have gained interest as a risk factor for rupture. We studied 3-dimensional-quantified morphological changes over time during radiological monitoring before rupture and around rupture. METHODS In this retrospective observational study, we identified aneurysms that ruptured during follow-up, with imaging available for at least 2 time points before rupture and one after rupture. For each time point, we obtained 8 morphological parameters: 2-dimensional size, volume, surface area, compactness 1 and 2, sphericity, elongation, and flatness. Morphological changes before rupture and around rupture were log-transformed, scaled, and analyzed with linear mixed-effects models. RESULTS We included 16 aneurysms in 16 patients who were imaged between 2004 and 2021. In the time period before rupture (median follow-up duration 1200 days, IQR 736-1340), 3 size-related morphological parameters increased: 2-dimensional size (estimated mean change 0.44, 95% CI 0.24-0.65), volume (estimated mean change 0.34, 95% CI 0.12-0.56), and surface area (0.33, 95% CI 0.11-0.54). In the period around rupture (median follow-up duration 407 days, IQR 148-719), these parameters further increased. In addition, 5 morphological parameters (compactness 1 and 2, sphericity, elongation, and flatness) decreased around rupture but not before rupture. CONCLUSION Change in aneurysm volume and surface area may be novel risk factors for rupture. Because most morphological parameters changed around but not before rupture, morphological changes during these 2 periods should be regarded as different processes. This implies that postrupture morphology should not be used as a surrogate for prerupture morphology in rupture prediction models.
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Affiliation(s)
- Maarten J Kamphuis
- Department of Radiology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Kimberley M Timmins
- Image Sciences Institute, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Hugo J Kuijf
- Image Sciences Institute, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Eva K L de Graaf
- Department of Radiology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Gabriel J E Rinkel
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Mervyn D I Vergouwen
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Irene C van der Schaaf
- Department of Radiology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
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Snyder SJ, Gauden A, Copeland K, Spiotta AM, Heit JJ. RAPID aneurysm accurately measures aneurysm size on CT angiography compared to three-dimensional digital subtraction angiography. Interv Neuroradiol 2023:15910199231222676. [PMID: 38150662 DOI: 10.1177/15910199231222676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2023] Open
Abstract
BACKGROUND Cerebral aneurysms are often identified and characterized on non-invasive CT Angiography (CTA) images, but digital subtraction angiography (DSA) is the gold standard for aneurysm evaluation. OBJECTIVE We compared cerebral aneurysm size measurements as measured from CTA processed by a semi-automated artificial intelligence software program (RAPID Aneurysm) and three-dimensional rotational DSA (3D-DSA). METHODS We performed a retrospective cohort study of consecutive patients with a cerebral aneurysm who underwent CTA and DSA with 3D reformations. CTA images were processed by RAPID Aneurysm to determine aneurysm height, width, and neck width. The reference standard was aneurysm measurements on 3D-DSA as measured by two neurointerventionalists. Both readers were blinded to RAPID Aneurysm measurements. Correlation and bias between these measurements were determined. RESULTS Results from 50 patients with 50 aneurysms were compared. 32 patients (64%) were female. Median age was 65 (IQR: 56.25-71.75). 37 patients (74%) presented with ruptured aneurysms. The aneurysms represented a range of aneurysm sizes (1.9-33.3 mm; IQR 3.6-7.2 mm). RAPID Aneurysm size measurements showed excellent correlation and low bias (correlation, mean difference) when compared to the reference standard for aneurysm height (0.98, -0.9 mm), width (0.98, 0.1 mm), and neck width (0.94, 1.1 mm). The inter-reader comparison between the two neurointerventionalists was similarly excellent for aneurysm height (0.97, -0.4 mm), width (0.98, -0.2 mm), and neck width (0.89, 0.8 mm). CONCLUSION RAPID Aneurysm measurement of cerebral aneurysm height, width, and neck width on CTA is strongly correlated to expert neurointerventionalist measurements on 3D-DSA.
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Affiliation(s)
- Sarah J Snyder
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Andrew Gauden
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
| | | | - Alejandro M Spiotta
- Department of Neurosurgery, Medical University of South Carolina, Charlestown, NC, USA
| | - Jeremy J Heit
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
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Gilotra K, Swarna S, Mani R, Basem J, Dashti R. Role of artificial intelligence and machine learning in the diagnosis of cerebrovascular disease. Front Hum Neurosci 2023; 17:1254417. [PMID: 37746051 PMCID: PMC10516608 DOI: 10.3389/fnhum.2023.1254417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 08/23/2023] [Indexed: 09/26/2023] Open
Abstract
Introduction Cerebrovascular diseases are known to cause significant morbidity and mortality to the general population. In patients with cerebrovascular disease, prompt clinical evaluation and radiographic interpretation are both essential in optimizing clinical management and in triaging patients for critical and potentially life-saving neurosurgical interventions. With recent advancements in the domains of artificial intelligence (AI) and machine learning (ML), many AI and ML algorithms have been developed to further optimize the diagnosis and subsequent management of cerebrovascular disease. Despite such advances, further studies are needed to substantively evaluate both the diagnostic accuracy and feasibility of these techniques for their application in clinical practice. This review aims to analyze the current use of AI and MI algorithms in the diagnosis of, and clinical decision making for cerebrovascular disease, and to discuss both the feasibility and future applications of utilizing such algorithms. Methods We review the use of AI and ML algorithms to assist clinicians in the diagnosis and management of ischemic stroke, hemorrhagic stroke, intracranial aneurysms, and arteriovenous malformations (AVMs). After identifying the most widely used algorithms, we provide a detailed analysis of the accuracy and effectiveness of these algorithms in practice. Results The incorporation of AI and ML algorithms for cerebrovascular patients has demonstrated improvements in time to detection of intracranial pathologies such as intracerebral hemorrhage (ICH) and infarcts. For ischemic and hemorrhagic strokes, commercial AI software platforms such as RapidAI and Viz.AI have bene implemented into routine clinical practice at many stroke centers to expedite the detection of infarcts and ICH, respectively. Such algorithms and neural networks have also been analyzed for use in prognostication for such cerebrovascular pathologies. These include predicting outcomes for ischemic stroke patients, hematoma expansion, risk of aneurysm rupture, bleeding of AVMs, and in predicting outcomes following interventions such as risk of occlusion for various endovascular devices. Preliminary analyses have yielded promising sensitivities when AI and ML are used in concert with imaging modalities and a multidisciplinary team of health care providers. Conclusion The implementation of AI and ML algorithms to supplement clinical practice has conferred a high degree of accuracy, efficiency, and expedited detection in the clinical and radiographic evaluation and management of ischemic and hemorrhagic strokes, AVMs, and aneurysms. Such algorithms have been explored for further purposes of prognostication for these conditions, with promising preliminary results. Further studies should evaluate the longitudinal implementation of such techniques into hospital networks and residency programs to supplement clinical practice, and the extent to which these techniques improve patient care and clinical outcomes in the long-term.
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Affiliation(s)
| | | | | | | | - Reza Dashti
- Dashti Lab, Department of Neurological Surgery, Stony Brook University Hospital, Stony Brook, NY, United States
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Tangsrivimol JA, Schonfeld E, Zhang M, Veeravagu A, Smith TR, Härtl R, Lawton MT, El-Sherbini AH, Prevedello DM, Glicksberg BS, Krittanawong C. Artificial Intelligence in Neurosurgery: A State-of-the-Art Review from Past to Future. Diagnostics (Basel) 2023; 13:2429. [PMID: 37510174 PMCID: PMC10378231 DOI: 10.3390/diagnostics13142429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 07/06/2023] [Accepted: 07/10/2023] [Indexed: 07/30/2023] Open
Abstract
In recent years, there has been a significant surge in discussions surrounding artificial intelligence (AI), along with a corresponding increase in its practical applications in various facets of everyday life, including the medical industry. Notably, even in the highly specialized realm of neurosurgery, AI has been utilized for differential diagnosis, pre-operative evaluation, and improving surgical precision. Many of these applications have begun to mitigate risks of intraoperative and postoperative complications and post-operative care. This article aims to present an overview of the principal published papers on the significant themes of tumor, spine, epilepsy, and vascular issues, wherein AI has been applied to assess its potential applications within neurosurgery. The method involved identifying high-cited seminal papers using PubMed and Google Scholar, conducting a comprehensive review of various study types, and summarizing machine learning applications to enhance understanding among clinicians for future utilization. Recent studies demonstrate that machine learning (ML) holds significant potential in neuro-oncological care, spine surgery, epilepsy management, and other neurosurgical applications. ML techniques have proven effective in tumor identification, surgical outcomes prediction, seizure outcome prediction, aneurysm prediction, and more, highlighting its broad impact and potential in improving patient management and outcomes in neurosurgery. This review will encompass the current state of research, as well as predictions for the future of AI within neurosurgery.
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Affiliation(s)
- Jonathan A Tangsrivimol
- Division of Neurosurgery, Department of Surgery, Chulabhorn Hospital, Chulabhorn Royal Academy, Bangkok 10210, Thailand
- Department of Neurological Surgery, The Ohio State University Wexner Medical Center and Jame Cancer Institute, Columbus, OH 43210, USA
| | - Ethan Schonfeld
- Department Biomedical Informatics, Stanford University School of Medicine, Palo Alto, CA 94305, USA
| | - Michael Zhang
- Department of Neurosurgery, Stanford University School of Medicine, Palo Alto, CA 94305, USA
| | - Anand Veeravagu
- Stanford Neurosurgical Artificial Intelligence and Machine Learning Laboratory, Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Timothy R Smith
- Department of Neurosurgery, Computational Neuroscience Outcomes Center (CNOC), Mass General Brigham, Harvard Medical School, Boston, MA 02115, USA
| | - Roger Härtl
- Weill Cornell Medicine Brain and Spine Center, New York, NY 10022, USA
| | - Michael T Lawton
- Department of Neurosurgery, Barrow Neurological Institute (BNI), Phoenix, AZ 85013, USA
| | - Adham H El-Sherbini
- Faculty of Health Sciences, Queen's University, Kingston, ON K7L 3N6, Canada
| | - Daniel M Prevedello
- Department of Neurological Surgery, The Ohio State University Wexner Medical Center and Jame Cancer Institute, Columbus, OH 43210, USA
| | - Benjamin S Glicksberg
- Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Chayakrit Krittanawong
- Cardiology Division, New York University Langone Health, New York University School of Medicine, New York, NY 10016, USA
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Pham N, Hill V, Rauschecker A, Lui Y, Niogi S, Fillipi CG, Chang P, Zaharchuk G, Wintermark M. Critical Appraisal of Artificial Intelligence-Enabled Imaging Tools Using the Levels of Evidence System. AJNR Am J Neuroradiol 2023; 44:E21-E28. [PMID: 37080722 PMCID: PMC10171388 DOI: 10.3174/ajnr.a7850] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 03/16/2023] [Indexed: 04/22/2023]
Abstract
Clinical adoption of an artificial intelligence-enabled imaging tool requires critical appraisal of its life cycle from development to implementation by using a systematic, standardized, and objective approach that can verify both its technical and clinical efficacy. Toward this concerted effort, the ASFNR/ASNR Artificial Intelligence Workshop Technology Working Group is proposing a hierarchal evaluation system based on the quality, type, and amount of scientific evidence that the artificial intelligence-enabled tool can demonstrate for each component of its life cycle. The current proposal is modeled after the levels of evidence in medicine, with the uppermost level of the hierarchy showing the strongest evidence for potential impact on patient care and health care outcomes. The intended goal of establishing an evidence-based evaluation system is to encourage transparency, foster an understanding of the creation of artificial intelligence tools and the artificial intelligence decision-making process, and to report the relevant data on the efficacy of artificial intelligence tools that are developed. The proposed system is an essential step in working toward a more formalized, clinically validated, and regulated framework for the safe and effective deployment of artificial intelligence imaging applications that will be used in clinical practice.
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Affiliation(s)
- N Pham
- From the Department of Radiology (N.P., G.Z.), Stanford School of Medicine, Palo Alto, California
| | - V Hill
- Department of Radiology (V.H.), Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | - A Rauschecker
- Department of Radiology (A.R.), University of California, San Francisco, San Francisco, California
| | - Y Lui
- Department of Radiology (Y.L.), NYU Grossman School of Medicine, New York, New York
| | - S Niogi
- Department of Radiology (S.N.), Weill Cornell Medicine, New York, New York
| | - C G Fillipi
- Department of Radiology (C.G.F.), Tufts University School of Medicine, Boston, Massachusetts
| | - P Chang
- Department of Radiology (P.C.), University of California, Irvine, Irvine, California
| | - G Zaharchuk
- From the Department of Radiology (N.P., G.Z.), Stanford School of Medicine, Palo Alto, California
| | - M Wintermark
- Department of Neuroradiology (M.W.), The University of Texas MD Anderson Cancer Center, Houston, Texas
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