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Delora A, Hadjialiakbari C, Percenti E, Torres J, Alderazi YJ, Ezzeldin R, Hassan AE, Ezzeldin M. Viz LVO versus Rapid LVO in detection of large vessel occlusion on CT angiography for acute stroke. J Neurointerv Surg 2024; 16:599-602. [PMID: 37355255 DOI: 10.1136/jnis-2023-020445] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2023] [Accepted: 06/10/2023] [Indexed: 06/26/2023]
Abstract
BACKGROUND Endovascular thrombectomy improves outcomes and reduces mortality for large vessel occlusion (LVO) and is time-sensitive. Computer automation may aid in the early detection of LVOs, but false values may lead to alarm desensitization. We compared Viz LVO and Rapid LVO for automated LVO detection. METHODS Data were retrospectively extracted from Rapid LVO and Viz LVO running concurrently from January 2022 to January 2023 on CT angiography (CTA) images compared with a radiologist interpretation. We calculated diagnostic accuracy measures and performed a McNemar test to look for a difference between the algorithms' errors. We collected demographic data, comorbidities, ejection fraction (EF), and imaging features and performed a multiple logistic regression to determine if any of these variables predicted the incorrect classification of LVO on CTA. RESULTS 360 participants were included, with 47 large vessel occlusions. Viz LVO and Rapid LVO had a specificity of 0.96 and 0.85, a sensitivity of 0.87 and 0.87, a positive predictive value of 0.75 and 0.46, and a negative predictive value of 0.98 and 0.97, respectively. A McNemar test on correct and incorrect classifications showed a statistically significant difference between the two algorithms' errors (P=0.00000031). A multiple logistic regression showed that low EF (Viz P=0.00125, Rapid P=0.0286) and Modified Woodcock Score >1 (Viz P=0.000198, Rapid P=0.000000975) were significant predictors of incorrect classification. CONCLUSION Rapid LVO produced a significantly larger number of false positive values that may contribute to alarm desensitization, leading to missed alarms or delayed responses. EF and intracranial atherosclerosis were significant predictors of incorrect predictions.
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Affiliation(s)
- Adam Delora
- Emergency Medicine, HCA Houston, Kingwood, Texas, USA
| | | | - Eryn Percenti
- Internal Medicine, HCA Houston, Kingwood, Texas, USA
| | - Jordan Torres
- Internal Medicine, HCA Houston, Kingwood, Texas, USA
| | | | - Rime Ezzeldin
- Medicine, Jordan University of Science and Technology, Irbid, Jordan
| | - Ameer E Hassan
- Department of Neurology, University of Texas Rio Grande Valley, Harlingen, Texas, USA
| | - Mohamad Ezzeldin
- Department of Clinical Sciences, College of Medicine, University of Houston, Houston, Texas, USA
- Neuroendovascular Surgery, HCA Houston, Houston, Texas, USA
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2
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Luo W, Xu Y, Liu C, Zhang H. The Influence of the Novel Computer-Aided Triage System Based on Artificial Intelligence on Endovascular Therapy in Patients with Large Vascular Occlusions: A Meta-Analysis. World Neurosurg 2024; 182:200-207.e2. [PMID: 38048961 DOI: 10.1016/j.wneu.2023.11.140] [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: 11/25/2023] [Accepted: 11/28/2023] [Indexed: 12/06/2023]
Abstract
OBJECTIVE The integration of artificial intelligence (AI) with modern healthcare has become increasingly prominent. The purpose of this study is to explore the impact of the novel computer-aided triage system based on artificial intelligence (AI-CTS) on endovascular therapy (EVT) in patients with large vascular occlusions (LVO). This study marks the first comprehensive systematic review and meta-analysis on the subject. METHODS A comprehensive study was performed on PubMed, Medline, Embase, Cochrane Library, and Chinese databases from their establishment to September 2023, in accordance with PRISMA recommendations. RevMan 5.4 software was used for summative analysis. The outcomes included door-to-groin (DTG) time, time from CT scan initiation to EVT, time from CT scan to reperfusion, and 90-day modified Rankin Scale (mRS). RESULTS A total of 7 studies involving 752 participants were included in the meta-analysis. The pooled results demonstrated that patients in the post-AI group had less time of DTG [SMD, 0.54; 95% CI, 0.40-0.69; P < 0.00001] and CT scan to EVT [SMD, 0.57; 95% CI, 0.42-0.73; P < 0.00001], as well as less time of CTA to recanalization [SMD, 0.63; 95% CI, 0.36-0.90; P < 0.00001]. There was no significant difference between the 2 groups in terms of the mRS at 90 days [OR, 0.66; 95% CI, 0.43-1.01; P = 0.06]. CONCLUSIONS The combination of AI-CTS and EVT has improved the therapy process for LVO patients. However, the improvement in mRS at 90 days was not significant; further research is warranted.
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Affiliation(s)
- Wenmiao Luo
- Department of Neurosurgery, Susong Hospital, Xiamen, China; Department of Neurosurgery, Clinical Medical College of Yangzhou University, Yangzhou, China
| | - Yonggang Xu
- Department of Neurosurgery, Susong Hospital, Xiamen, China
| | - Chao Liu
- Department of Neurosurgery, Susong Hospital, Xiamen, China
| | - Hengzhu Zhang
- Department of Neurosurgery, Clinical Medical College of Yangzhou University, Yangzhou, China.
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3
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Kunst M, Gupta R, Coombs LP, Delfino JG, Khan A, Berglar I, Kozak B, Small JE, Gillis L, Noonan P, Fang J, Pai V, Tilkin M, Allen B, Dreyer K, Wald C. Real-World Performance of Large Vessel Occlusion Artificial Intelligence-Based Computer-Aided Triage and Notification Algorithms-What the Stroke Team Needs to Know. J Am Coll Radiol 2024; 21:329-340. [PMID: 37196818 DOI: 10.1016/j.jacr.2023.04.003] [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: 12/02/2022] [Revised: 03/21/2023] [Accepted: 04/06/2023] [Indexed: 05/19/2023]
Abstract
PURPOSE To evaluate the real-world performance of two FDA-approved artificial intelligence (AI)-based computer-aided triage and notification (CADt) detection devices and compare them with the manufacturer-reported performance testing in the instructions for use. MATERIALS AND METHODS Clinical performance of two FDA-cleared CADt large-vessel occlusion (LVO) devices was retrospectively evaluated at two separate stroke centers. Consecutive "code stroke" CT angiography examinations were included and assessed for patient demographics, scanner manufacturer, presence or absence of CADt result, CADt result, and LVO in the internal carotid artery (ICA), horizontal middle cerebral artery (MCA) segment (M1), Sylvian MCA segments after the bifurcation (M2), precommunicating part of cerebral artery, postcommunicating part of the cerebral artery, vertebral artery, basilar artery vessel segments. The original radiology report served as the reference standard, and a study radiologist extracted the above data elements from the imaging examination and radiology report. RESULTS At hospital A, the CADt algorithm manufacturer reports assessment of intracranial ICA and MCA with sensitivity of 97% and specificity of 95.6%. Real-world performance of 704 cases included 79 in which no CADt result was available. Sensitivity and specificity in ICA and M1 segments were 85.3% and 91.9%. Sensitivity decreased to 68.5% when M2 segments were included and to 59.9% when all proximal vessel segments were included. At hospital B the CADt algorithm manufacturer reports sensitivity of 87.8% and specificity of 89.6%, without specifying the vessel segments. Real-world performance of 642 cases included 20 cases in which no CADt result was available. Sensitivity and specificity in ICA and M1 segments were 90.7% and 97.9%. Sensitivity decreased to 76.4% when M2 segments were included and to 59.4% when all proximal vessel segments are included. DISCUSSION Real-world testing of two CADt LVO detection algorithms identified gaps in the detection and communication of potentially treatable LVOs when considering vessels beyond the intracranial ICA and M1 segments and in cases with absent and uninterpretable data.
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Affiliation(s)
- Mara Kunst
- Neuroradiology Section Head, Lahey Hospital and Medical Center, Burlington, Massachusetts
| | - Rajiv Gupta
- Neuroradiology Section Head, Massachusetts General Hospital, Boston, Massachusetts
| | | | - Jana G Delfino
- US Food and Drug Administration, Silver Spring, Maryland
| | - Amir Khan
- US Food and Drug Administration, Silver Spring, Maryland
| | - Inka Berglar
- Massachusetts General Hospital, Boston, Massachusetts
| | | | - Juan E Small
- Lahey Hospital and Medical Center, Burlington, Massachusetts
| | | | - Patrick Noonan
- US Food and Drug Administration, Silver Spring, Maryland
| | - Junyong Fang
- US Food and Drug Administration, Silver Spring, Maryland
| | - Vinay Pai
- US Food and Drug Administration, Silver Spring, Maryland
| | | | - Bibb Allen
- American College of Radiology; Chief Medical Officer of the ACR Data Science Institute
| | - Keith Dreyer
- Chief Data Science Officer, Vice Chairman of Radiology, Massachusetts General Hospital, Boston, Massachusetts
| | - Christoph Wald
- Chairman of Radiology, Lahey Hospital and Medical Center, Burlington, Massachusetts; and Chair, ACR Commission on Informatics
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4
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Yearley AG, Goedmakers CMW, Panahi A, Doucette J, Rana A, Ranganathan K, Smith TR. FDA-approved machine learning algorithms in neuroradiology: A systematic review of the current evidence for approval. Artif Intell Med 2023; 143:102607. [PMID: 37673576 DOI: 10.1016/j.artmed.2023.102607] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 05/30/2023] [Accepted: 06/05/2023] [Indexed: 09/08/2023]
Abstract
Over the past decade, machine learning (ML) and artificial intelligence (AI) have become increasingly prevalent in the medical field. In the United States, the Food and Drug Administration (FDA) is responsible for regulating AI algorithms as "medical devices" to ensure patient safety. However, recent work has shown that the FDA approval process may be deficient. In this study, we evaluate the evidence supporting FDA-approved neuroalgorithms, the subset of machine learning algorithms with applications in the central nervous system (CNS), through a systematic review of the primary literature. Articles covering the 53 FDA-approved algorithms with applications in the CNS published in PubMed, EMBASE, Google Scholar and Scopus between database inception and January 25, 2022 were queried. Initial searches identified 1505 studies, of which 92 articles met the criteria for extraction and inclusion. Studies were identified for 26 of the 53 neuroalgorithms, of which 10 algorithms had only a single peer-reviewed publication. Performance metrics were available for 15 algorithms, external validation studies were available for 24 algorithms, and studies exploring the use of algorithms in clinical practice were available for 7 algorithms. Papers studying the clinical utility of these algorithms focused on three domains: workflow efficiency, cost savings, and clinical outcomes. Our analysis suggests that there is a meaningful gap between the FDA approval of machine learning algorithms and their clinical utilization. There appears to be room for process improvement by implementation of the following recommendations: the provision of compelling evidence that algorithms perform as intended, mandating minimum sample sizes, reporting of a predefined set of performance metrics for all algorithms and clinical application of algorithms prior to widespread use. This work will serve as a baseline for future research into the ideal regulatory framework for AI applications worldwide.
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Affiliation(s)
- Alexander G Yearley
- Harvard Medical School, 25 Shattuck St, Boston, MA 02115, USA; Computational Neuroscience Outcomes Center (CNOC), Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115, USA.
| | - Caroline M W Goedmakers
- Computational Neuroscience Outcomes Center (CNOC), Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115, USA; Department of Neurosurgery, Leiden University Medical Center, Albinusdreef 2, 2333 ZA Leiden, Netherlands
| | - Armon Panahi
- The George Washington University School of Medicine and Health Sciences, 2300 I St NW, Washington, DC 20052, USA
| | - Joanne Doucette
- Computational Neuroscience Outcomes Center (CNOC), Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115, USA; School of Pharmacy, MCPHS University, 179 Longwood Ave, Boston, MA 02115, USA
| | - Aakanksha Rana
- Computational Neuroscience Outcomes Center (CNOC), Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115, USA; Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, USA
| | - Kavitha Ranganathan
- Division of Plastic Surgery, Brigham and Women's Hospital, 75 Francis St, Boston, MA 02115, USA
| | - Timothy R Smith
- Harvard Medical School, 25 Shattuck St, Boston, MA 02115, USA; Computational Neuroscience Outcomes Center (CNOC), Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115, USA
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5
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Brugnara G, Baumgartner M, Scholze ED, Deike-Hofmann K, Kades K, Scherer J, Denner S, Meredig H, Rastogi A, Mahmutoglu MA, Ulfert C, Neuberger U, Schönenberger S, Schlamp K, Bendella Z, Pinetz T, Schmeel C, Wick W, Ringleb PA, Floca R, Möhlenbruch M, Radbruch A, Bendszus M, Maier-Hein K, Vollmuth P. Deep-learning based detection of vessel occlusions on CT-angiography in patients with suspected acute ischemic stroke. Nat Commun 2023; 14:4938. [PMID: 37582829 PMCID: PMC10427649 DOI: 10.1038/s41467-023-40564-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Accepted: 08/01/2023] [Indexed: 08/17/2023] Open
Abstract
Swift diagnosis and treatment play a decisive role in the clinical outcome of patients with acute ischemic stroke (AIS), and computer-aided diagnosis (CAD) systems can accelerate the underlying diagnostic processes. Here, we developed an artificial neural network (ANN) which allows automated detection of abnormal vessel findings without any a-priori restrictions and in <2 minutes. Pseudo-prospective external validation was performed in consecutive patients with suspected AIS from 4 different hospitals during a 6-month timeframe and demonstrated high sensitivity (≥87%) and negative predictive value (≥93%). Benchmarking against two CE- and FDA-approved software solutions showed significantly higher performance for our ANN with improvements of 25-45% for sensitivity and 4-11% for NPV (p ≤ 0.003 each). We provide an imaging platform ( https://stroke.neuroAI-HD.org ) for online processing of medical imaging data with the developed ANN, including provisions for data crowdsourcing, which will allow continuous refinements and serve as a blueprint to build robust and generalizable AI algorithms.
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Affiliation(s)
- Gianluca Brugnara
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
- Division for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Michael Baumgartner
- Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Helmholtz Imaging, Heidelberg, Germany
- Faculty of Mathematics and Computer Science, Heidelberg University, Heidelberg, Germany
| | - Edwin David Scholze
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
- Division for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Katerina Deike-Hofmann
- Department of Neuroradiology, Bonn University Hospital, Bonn, Germany
- Clinical Neuroimaging Group, German Center for Neurodegenerative Diseases, DZNE, Bonn, Germany
| | - Klaus Kades
- Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Faculty of Mathematics and Computer Science, Heidelberg University, Heidelberg, Germany
| | - Jonas Scherer
- Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Stefan Denner
- Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Faculty of Medicine, University of Heidelberg, Heidelberg, Germany
| | - Hagen Meredig
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
- Division for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Aditya Rastogi
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
- Division for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Mustafa Ahmed Mahmutoglu
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
- Division for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Christian Ulfert
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Ulf Neuberger
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | | | - Kai Schlamp
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at University of Heidelberg, Heidelberg, Germany
| | - Zeynep Bendella
- Department of Neuroradiology, Bonn University Hospital, Bonn, Germany
| | - Thomas Pinetz
- Institute for Applied Mathematics, University of Bonn, Bonn, Germany
| | - Carsten Schmeel
- Department of Neuroradiology, Bonn University Hospital, Bonn, Germany
- Clinical Neuroimaging Group, German Center for Neurodegenerative Diseases, DZNE, Bonn, Germany
| | - Wolfgang Wick
- Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany
| | - Peter A Ringleb
- Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany
| | - Ralf Floca
- Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Heidelberg Institute of Radiation Oncology (HIRO), National Center for Radiation Research in Oncology (NCRO), Heidelberg, Germany
| | - Markus Möhlenbruch
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Alexander Radbruch
- Department of Neuroradiology, Bonn University Hospital, Bonn, Germany
- Clinical Neuroimaging Group, German Center for Neurodegenerative Diseases, DZNE, Bonn, Germany
| | - Martin Bendszus
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Klaus Maier-Hein
- Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Pattern Analysis and Learning Group, Heidelberg University Hospital, Heidelberg, Germany
| | - Philipp Vollmuth
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany.
- Division for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany.
- Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany.
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Wagner DT, Tilmans L, Peng K, Niedermeier M, Rohl M, Ryan S, Yadav D, Takacs N, Garcia-Fraley K, Koso M, Dikici E, Prevedello LM, Nguyen XV. Artificial Intelligence in Neuroradiology: A Review of Current Topics and Competition Challenges. Diagnostics (Basel) 2023; 13:2670. [PMID: 37627929 PMCID: PMC10453240 DOI: 10.3390/diagnostics13162670] [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: 05/16/2023] [Revised: 08/07/2023] [Accepted: 08/09/2023] [Indexed: 08/27/2023] Open
Abstract
There is an expanding body of literature that describes the application of deep learning and other machine learning and artificial intelligence methods with potential relevance to neuroradiology practice. In this article, we performed a literature review to identify recent developments on the topics of artificial intelligence in neuroradiology, with particular emphasis on large datasets and large-scale algorithm assessments, such as those used in imaging AI competition challenges. Numerous applications relevant to ischemic stroke, intracranial hemorrhage, brain tumors, demyelinating disease, and neurodegenerative/neurocognitive disorders were discussed. The potential applications of these methods to spinal fractures, scoliosis grading, head and neck oncology, and vascular imaging were also reviewed. The AI applications examined perform a variety of tasks, including localization, segmentation, longitudinal monitoring, diagnostic classification, and prognostication. While research on this topic is ongoing, several applications have been cleared for clinical use and have the potential to augment the accuracy or efficiency of neuroradiologists.
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Affiliation(s)
- Daniel T. Wagner
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA (L.M.P.)
| | - Luke Tilmans
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA (L.M.P.)
| | - Kevin Peng
- College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | | | - Matt Rohl
- College of Arts and Sciences, The Ohio State University, Columbus, OH 43210, USA
| | - Sean Ryan
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA (L.M.P.)
| | - Divya Yadav
- College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | - Noah Takacs
- College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | - Krystle Garcia-Fraley
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA (L.M.P.)
| | - Mensur Koso
- College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | - Engin Dikici
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA (L.M.P.)
| | - Luciano M. Prevedello
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA (L.M.P.)
| | - Xuan V. Nguyen
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA (L.M.P.)
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7
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Temmen SE, Becks MJ, Schalekamp S, van Leeuwen KG, Meijer FJA. Duration and accuracy of automated stroke CT workflow with AI-supported intracranial large vessel occlusion detection. Sci Rep 2023; 13:12551. [PMID: 37532773 PMCID: PMC10397283 DOI: 10.1038/s41598-023-39831-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 07/31/2023] [Indexed: 08/04/2023] Open
Abstract
The Automation Platform (AP) is a software platform to support the workflow of radiologists and includes a stroke CT package with integrated artificial intelligence (AI) based tools. The aim of this study was to evaluate the diagnostic performance of the AP for the detection of intracranial large vessel occlusions (LVO) on conventional CT angiography (CTA), and the duration of CT processing in a cohort of acute stroke patients. The diagnostic performance for intracranial LVO detection on CTA by the AP was evaluated in a retrospective cohort of 100 acute stroke patients and compared to the diagnostic performance of five radiologists with different levels of experience. The reference standard was set by an independent neuroradiologist, with access to the readings of the different radiologists, clinical data, and follow-up. The data processing time of the AP for ICH detection on non-contrast CT, LVO detection on CTA, and the processing of CTP maps was assessed in a subset 60 patients of the retrospective cohort. This was compared to 13 radiologists, who were prospectively timed for the processing and reading of 21 stroke CTs. The AP showed shorter processing time of CTA (mean 60 versus 395 s) and CTP (mean 196 versus 243-349 s) as compared to radiologists, but showed lower sensitivity for LVO detection (sensitivity 77% of the AP vs mean sensitivity 87% of radiologists). If the AP would have been used as a stand-alone system, 1 ICA occlusion, 2 M1 occlusions and 8 M2 occlusions would have been missed, which would be eligible for mechanical thrombectomy. In conclusion, the AP showed shorter processing time of CTA and CTP as compared with radiologists, which illustrates the potential of the AP to speed-up the diagnostic work-up. However, its performance for LVO detection was lower as compared with radiologists, especially for M2 vessel occlusions.
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Affiliation(s)
- Sander E Temmen
- Department of Medical Imaging, Radboud University Medical Center, Geert Grooteplein Zuid 10, 766, PO Box 9101, 6500HB, Nijmegen, The Netherlands
| | - Marinus J Becks
- Department of Medical Imaging, Radboud University Medical Center, Geert Grooteplein Zuid 10, 766, PO Box 9101, 6500HB, Nijmegen, The Netherlands
| | - Steven Schalekamp
- Department of Medical Imaging, Radboud University Medical Center, Geert Grooteplein Zuid 10, 766, PO Box 9101, 6500HB, Nijmegen, The Netherlands
| | - Kicky G van Leeuwen
- Department of Medical Imaging, Radboud University Medical Center, Geert Grooteplein Zuid 10, 766, PO Box 9101, 6500HB, Nijmegen, The Netherlands
| | - Frederick J A Meijer
- Department of Medical Imaging, Radboud University Medical Center, Geert Grooteplein Zuid 10, 766, PO Box 9101, 6500HB, Nijmegen, The Netherlands.
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8
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Chandrabhatla AS, Kuo EA, Sokolowski JD, Kellogg RT, Park M, Mastorakos P. Artificial Intelligence and Machine Learning in the Diagnosis and Management of Stroke: A Narrative Review of United States Food and Drug Administration-Approved Technologies. J Clin Med 2023; 12:jcm12113755. [PMID: 37297949 DOI: 10.3390/jcm12113755] [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: 04/30/2023] [Revised: 05/22/2023] [Accepted: 05/26/2023] [Indexed: 06/12/2023] Open
Abstract
Stroke is an emergency in which delays in treatment can lead to significant loss of neurological function and be fatal. Technologies that increase the speed and accuracy of stroke diagnosis or assist in post-stroke rehabilitation can improve patient outcomes. No resource exists that comprehensively assesses artificial intelligence/machine learning (AI/ML)-enabled technologies indicated for the management of ischemic and hemorrhagic stroke. We queried a United States Food and Drug Administration (FDA) database, along with PubMed and private company websites, to identify the recent literature assessing the clinical performance of FDA-approved AI/ML-enabled technologies. The FDA has approved 22 AI/ML-enabled technologies that triage brain imaging for more immediate diagnosis or promote post-stroke neurological/functional recovery. Technologies that assist with diagnosis predominantly use convolutional neural networks to identify abnormal brain images (e.g., CT perfusion). These technologies perform comparably to neuroradiologists, improve clinical workflows (e.g., time from scan acquisition to reading), and improve patient outcomes (e.g., days spent in the neurological ICU). Two devices are indicated for post-stroke rehabilitation by leveraging neuromodulation techniques. Multiple FDA-approved technologies exist that can help clinicians better diagnose and manage stroke. This review summarizes the most up-to-date literature regarding the functionality, performance, and utility of these technologies so clinicians can make informed decisions when using them in practice.
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Affiliation(s)
- Anirudha S Chandrabhatla
- School of Medicine, University of Virginia Health Sciences Center, 1215 Lee Street, Charlottesville, VA 22903, USA
- Department of Neurological Surgery, University of Virginia Health Sciences Center, 1215 Lee Street, Charlottesville, VA 22903, USA
| | - Elyse A Kuo
- School of Medicine, University of Virginia Health Sciences Center, 1215 Lee Street, Charlottesville, VA 22903, USA
- Department of Neurological Surgery, University of Virginia Health Sciences Center, 1215 Lee Street, Charlottesville, VA 22903, USA
| | - Jennifer D Sokolowski
- Department of Neurological Surgery, University of Virginia Health Sciences Center, 1215 Lee Street, Charlottesville, VA 22903, USA
| | - Ryan T Kellogg
- Department of Neurological Surgery, University of Virginia Health Sciences Center, 1215 Lee Street, Charlottesville, VA 22903, USA
| | - Min Park
- Department of Neurological Surgery, University of Virginia Health Sciences Center, 1215 Lee Street, Charlottesville, VA 22903, USA
| | - Panagiotis Mastorakos
- Department of Neurological Surgery, University of Virginia Health Sciences Center, 1215 Lee Street, Charlottesville, VA 22903, USA
- Department of Neurological Surgery, Thomas Jefferson University Hospital, 111 S 11th Street, Philadelphia, PA 19107, USA
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9
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Tripathi P, Gulli C, Broomfield J, Alexandrou G, Kalofonou M, Bevan C, Moser N, Georgiou P. Classification of nucleic acid amplification on ISFET arrays using spectrogram-based neural networks. Comput Biol Med 2023; 161:107027. [PMID: 37211003 DOI: 10.1016/j.compbiomed.2023.107027] [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: 10/20/2022] [Revised: 04/20/2023] [Accepted: 05/09/2023] [Indexed: 05/23/2023]
Abstract
The COVID-19 pandemic has highlighted a significant research gap in the field of molecular diagnostics. This has brought forth the need for AI-based edge solutions that can provide quick diagnostic results whilst maintaining data privacy, security and high standards of sensitivity and specificity. This paper presents a novel proof-of-concept method to detect nucleic acid amplification using ISFET sensors and deep learning. This enables the detection of DNA and RNA on a low-cost and portable lab-on-chip platform for identifying infectious diseases and cancer biomarkers. We show that by using spectrograms to transform the signal to the time-frequency domain, image processing techniques can be applied to achieve the reliable classification of the detected chemical signals. Transformation to spectrograms is beneficial as it makes the data compatible with 2D convolutional neural networks and helps gain significant performance improvement over neural networks trained on the time domain data. The trained network achieves an accuracy of 84% with a size of 30kB making it suitable for deployment on edge devices. This facilitates a new wave of intelligent lab-on-chip platforms that combine microfluidics, CMOS-based chemical sensing arrays and AI-based edge solutions for more intelligent and rapid molecular diagnostics.
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Affiliation(s)
- Prateek Tripathi
- Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Faculty of Engineering, Imperial College London, SW7 2AZ, London, UK.
| | - Costanza Gulli
- Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Faculty of Engineering, Imperial College London, SW7 2AZ, London, UK
| | - Joseph Broomfield
- Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Faculty of Engineering, Imperial College London, SW7 2AZ, London, UK; Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, SW7 2AZ, London, UK
| | - George Alexandrou
- Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Faculty of Engineering, Imperial College London, SW7 2AZ, London, UK
| | - Melpomeni Kalofonou
- Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Faculty of Engineering, Imperial College London, SW7 2AZ, London, UK
| | - Charlotte Bevan
- Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, SW7 2AZ, London, UK
| | - Nicolas Moser
- Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Faculty of Engineering, Imperial College London, SW7 2AZ, London, UK
| | - Pantelis Georgiou
- Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Faculty of Engineering, Imperial College London, SW7 2AZ, London, UK
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Karamchandani RR, Helms AM, Satyanarayana S, Yang H, Clemente JD, Defilipp G, Strong D, Rhoten JB, Asimos AW. Automated detection of intracranial large vessel occlusions using Viz.ai software: Experience in a large, integrated stroke network. Brain Behav 2023; 13:e2808. [PMID: 36457286 PMCID: PMC9847593 DOI: 10.1002/brb3.2808] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 09/27/2022] [Accepted: 10/11/2022] [Indexed: 12/05/2022] Open
Abstract
BACKGROUND AND PURPOSE Endovascular thrombectomy is an evidence-based treatment for large vessel occlusion (LVO) stroke. Commercially available artificial intelligence has been designed to detect the presence of an LVO on computed tomography angiogram (CTA). We compared Viz.ai-LVO (San Francisco, CA, USA) to CTA interpretation by board-certified neuroradiologists (NRs) in a large, integrated stroke network. METHODS From January 2021 to December 2021, we compared Viz.ai detection of an internal carotid artery (ICA) or middle cerebral artery first segment (MCA-M1) occlusion to the gold standard of CTA interpretation by board-certified NRs for all code stroke CTAs. On a monthly basis, sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) were calculated. Trend analyses were conducted to evaluate for any improvement of LVO detection by the software over time. RESULTS 3851 patients met study inclusion criteria, of whom 220 (5.7%) had an ICA or MCA-M1 occlusion per NR. Sensitivity and specificity were 78.2% (95% CI 72%-83%) and 97% (95% CI 96%-98%), respectively. PPV was 61% (95% CI 55%-67%), NPV 99% (95% CI 98%-99%), and accuracy was 95.9% (95% CI 95.3%-96.5%). Neither specificity or sensitivity improved over time in the trend analysis. CONCLUSIONS Viz.ai-LVO has high specificity and moderately high sensitivity to detect an ICA or proximal MCA occlusion. The software has the potential to streamline code stroke workflows and may be particularly impactful when emergency access to NRs or vascular neurologists is limited.
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Affiliation(s)
| | - Anna Maria Helms
- Neurosciences Institute, Atrium Health, Charlotte, North Carolina, USA
| | - Sagar Satyanarayana
- Information and Analytics Services, Atrium Health, Charlotte, North Carolina, USA
| | - Hongmei Yang
- Information and Analytics Services, Atrium Health, Charlotte, North Carolina, USA
| | - Jonathan D Clemente
- Charlotte Radiology, Neurosciences Institute, Atrium Health, Charlotte, North Carolina, USA
| | - Gary Defilipp
- Charlotte Radiology, Neurosciences Institute, Atrium Health, Charlotte, North Carolina, USA
| | - Dale Strong
- Information and Analytics Services, Atrium Health, Charlotte, North Carolina, USA
| | - Jeremy B Rhoten
- Neurosciences Institute, Atrium Health, Charlotte, North Carolina, USA
| | - Andrew W Asimos
- Emergency Medicine, Neurosciences Institute, Atrium Health, Charlotte, North Carolina, USA
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