1
|
Elsheikh S, Elbaz A, Rau A, Demerath T, Fung C, Kellner E, Urbach H, Reisert M. Accuracy of automated segmentation and volumetry of acute intracerebral hemorrhage following minimally invasive surgery using a patch-based convolutional neural network in a small dataset. Neuroradiology 2024; 66:601-608. [PMID: 38367095 PMCID: PMC10937775 DOI: 10.1007/s00234-024-03311-4] [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: 12/04/2023] [Accepted: 02/08/2024] [Indexed: 02/19/2024]
Abstract
PURPOSE In cases of acute intracerebral hemorrhage (ICH) volume estimation is of prognostic and therapeutic value following minimally invasive surgery (MIS). The ABC/2 method is widely used, but suffers from inaccuracies and is time consuming. Supervised machine learning using convolutional neural networks (CNN), trained on large datasets, is suitable for segmentation tasks in medical imaging. Our objective was to develop a CNN based machine learning model for the segmentation of ICH and of the drain and volumetry of ICH following MIS of acute supratentorial ICH on a relatively small dataset. METHODS Ninety two scans were assigned to training (n = 29 scans), validation (n = 4 scans) and testing (n = 59 scans) datasets. The mean age (SD) was 70 (± 13.56) years. Male patients were 36. A hierarchical, patch-based CNN for segmentation of ICH and drain was trained. Volume of ICH was calculated from the segmentation mask. RESULTS The best performing model achieved a Dice similarity coefficient of 0.86 and 0.91 for the ICH and drain respectively. Automated ICH volumetry yielded high agreement with ground truth (Intraclass correlation coefficient = 0.94 [95% CI: 0.91, 0.97]). Average difference in the ICH volume was 1.33 mL. CONCLUSION Using a relatively small dataset, originating from different CT-scanners and with heterogeneous voxel dimensions, we applied a patch-based CNN framework and successfully developed a machine learning model, which accurately segments the intracerebral hemorrhage (ICH) and the drains. This provides automated and accurate volumetry of the bleeding in acute ICH treated with minimally invasive surgery.
Collapse
Affiliation(s)
- Samer Elsheikh
- Department of Neuroradiology, Faculty of Medicine, Medical Center - University of Freiburg, Breisacherstr. 64, 79106, Freiburg, Germany.
| | - Ahmed Elbaz
- Department of Neuroradiology, Faculty of Medicine, Medical Center - University of Freiburg, Breisacherstr. 64, 79106, Freiburg, Germany
| | - Alexander Rau
- Department of Neuroradiology, Faculty of Medicine, Medical Center - University of Freiburg, Breisacherstr. 64, 79106, Freiburg, Germany
- Department of Diagnostic and Interventional Radiology, Faculty of Medicine, Medical Center - University of Freiburg, Freiburg, Germany
| | - Theo Demerath
- Department of Neuroradiology, Faculty of Medicine, Medical Center - University of Freiburg, Breisacherstr. 64, 79106, Freiburg, Germany
| | - Christian Fung
- Department of Neurosurgery, Faculty of Medicine, Medical Center - University of Freiburg, Freiburg, Germany
| | - Elias Kellner
- Medical Physics, Department of Diagnostic and Interventional Radiology, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Horst Urbach
- Department of Neuroradiology, Faculty of Medicine, Medical Center - University of Freiburg, Breisacherstr. 64, 79106, Freiburg, Germany
| | - Marco Reisert
- Medical Physics, Department of Diagnostic and Interventional Radiology, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Department of Stereotactic and Functional Neurosurgery, Faculty of Medicine, Medical Center - University of Freiburg, Freiburg, Germany
| |
Collapse
|
2
|
El Naamani K, Musmar B, Gupta N, Ikhdour O, Abdelrazeq H, Ghanem M, Wali MH, El-Hajj J, Alhussein A, Alhussein R, Tjoumakaris SI, Gooch MR, Rosenwasser RH, Jabbour PM, Herial NA. The Artificial Intelligence Revolution in Stroke Care: A Decade of Scientific Evidence in Review. World Neurosurg 2024; 184:15-22. [PMID: 38185459 DOI: 10.1016/j.wneu.2024.01.012] [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: 08/14/2023] [Revised: 01/01/2024] [Accepted: 01/02/2024] [Indexed: 01/09/2024]
Abstract
BACKGROUND The emergence of artificial intelligence (AI) has significantly influenced the diagnostic evaluation of stroke and has revolutionized acute stroke care delivery. The scientific evidence evaluating the role of AI, especially in areas of stroke treatment and rehabilitation is limited but continues to accumulate. We performed a systemic review of current scientific evidence evaluating the use of AI in stroke evaluation and care and examined the publication trends during the past decade. METHODS A systematic search of electronic databases was conducted to identify all studies published from 2012 to 2022 that incorporated AI in any aspect of stroke care. Studies not directly relevant to stroke care in the context of AI and duplicate studies were excluded. The level of evidence and publication trends were examined. RESULTS A total of 623 studies were examined, including 101 reviews (16.2%), 9 meta-analyses (1.4%), 140 original articles on AI methodology (22.5%), 2 case reports (0.3%), 2 case series (0.3%), 31 case-control studies (5%), 277 cohort studies (44.5%), 16 cross-sectional studies (2.6%), and 45 experimental studies (7.2%). The highest published area of AI in stroke was diagnosis (44.1%) and the lowest was rehabilitation (12%). A 10-year trend analysis revealed a significant increase in AI literature in stroke care. CONCLUSIONS Most research on AI is in the diagnostic area of stroke care, with a recent noteworthy trend of increased research focus on stroke treatment and rehabilitation.
Collapse
Affiliation(s)
- Kareem El Naamani
- Department of Neurological Surgery, Thomas Jefferson University Hospital, Philadelphia, Pennsylvania, USA
| | - Basel Musmar
- School of Medicine, An-Najah National University, Nablus, Palestine
| | - Nithin Gupta
- Jerry M. Wallace School of Osteopathic Medicine, Campbell University, Lillington, North Carolina, USA
| | - Osama Ikhdour
- School of Medicine, An-Najah National University, Nablus, Palestine
| | | | - Marc Ghanem
- Gilbert and Rose-Marie Chaghoury School of Medicine, Lebanese American University, Byblos, Lebanon
| | - Murad H Wali
- College of Public Health, Temple University, Philadelphia, Pennsylvania, USA
| | - Jad El-Hajj
- School of Medicine, St. George's University, St. George, Grenada
| | - Abdulaziz Alhussein
- Department of Neurological Surgery, Thomas Jefferson University Hospital, Philadelphia, Pennsylvania, USA
| | - Reyoof Alhussein
- Department of Neurological Surgery, Thomas Jefferson University Hospital, Philadelphia, Pennsylvania, USA
| | - Stavropoula I Tjoumakaris
- Department of Neurological Surgery, Thomas Jefferson University Hospital, Philadelphia, Pennsylvania, USA
| | - Michael R Gooch
- Department of Neurological Surgery, Thomas Jefferson University Hospital, Philadelphia, Pennsylvania, USA
| | - Robert H Rosenwasser
- Department of Neurological Surgery, Thomas Jefferson University Hospital, Philadelphia, Pennsylvania, USA
| | - Pascal M Jabbour
- Department of Neurological Surgery, Thomas Jefferson University Hospital, Philadelphia, Pennsylvania, USA
| | - Nabeel A Herial
- Department of Neurological Surgery, Thomas Jefferson University Hospital, Philadelphia, Pennsylvania, USA.
| |
Collapse
|
3
|
Kolasa K, Admassu B, Hołownia-Voloskova M, Kędzior KJ, Poirrier JE, Perni S. Systematic reviews of machine learning in healthcare: a literature review. Expert Rev Pharmacoecon Outcomes Res 2024; 24:63-115. [PMID: 37955147 DOI: 10.1080/14737167.2023.2279107] [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: 07/17/2023] [Accepted: 10/31/2023] [Indexed: 11/14/2023]
Abstract
INTRODUCTION The increasing availability of data and computing power has made machine learning (ML) a viable approach to faster, more efficient healthcare delivery. METHODS A systematic literature review (SLR) of published SLRs evaluating ML applications in healthcare settings published between1 January 2010 and 27 March 2023 was conducted. RESULTS In total 220 SLRs covering 10,462 ML algorithms were reviewed. The main application of AI in medicine related to the clinical prediction and disease prognosis in oncology and neurology with the use of imaging data. Accuracy, specificity, and sensitivity were provided in 56%, 28%, and 25% SLRs respectively. Internal and external validation was reported in 53% and less than 1% of the cases respectively. The most common modeling approach was neural networks (2,454 ML algorithms), followed by support vector machine and random forest/decision trees (1,578 and 1,522 ML algorithms, respectively). EXPERT OPINION The review indicated considerable reporting gaps in terms of the ML's performance, both internal and external validation. Greater accessibility to healthcare data for developers can ensure the faster adoption of ML algorithms into clinical practice.
Collapse
Affiliation(s)
- Katarzyna Kolasa
- Division of Health Economics and Healthcare Management, Kozminski University, Warsaw, Poland
| | - Bisrat Admassu
- Division of Health Economics and Healthcare Management, Kozminski University, Warsaw, Poland
| | | | | | | | | |
Collapse
|
4
|
Teneggi J, Yi PH, Sulam J. Examination-Level Supervision for Deep Learning-based Intracranial Hemorrhage Detection on Head CT Scans. Radiol Artif Intell 2024; 6:e230159. [PMID: 38294324 PMCID: PMC10831525 DOI: 10.1148/ryai.230159] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 11/02/2023] [Accepted: 12/05/2023] [Indexed: 02/01/2024]
Abstract
Purpose To compare the effectiveness of weak supervision (ie, with examination-level labels only) and strong supervision (ie, with image-level labels) in training deep learning models for detection of intracranial hemorrhage (ICH) on head CT scans. Materials and Methods In this retrospective study, an attention-based convolutional neural network was trained with either local (ie, image level) or global (ie, examination level) binary labels on the Radiological Society of North America (RSNA) 2019 Brain CT Hemorrhage Challenge dataset of 21 736 examinations (8876 [40.8%] ICH) and 752 422 images (107 784 [14.3%] ICH). The CQ500 (436 examinations; 212 [48.6%] ICH) and CT-ICH (75 examinations; 36 [48.0%] ICH) datasets were employed for external testing. Performance in detecting ICH was compared between weak (examination-level labels) and strong (image-level labels) learners as a function of the number of labels available during training. Results On examination-level binary classification, strong and weak learners did not have different area under the receiver operating characteristic curve values on the internal validation split (0.96 vs 0.96; P = .64) and the CQ500 dataset (0.90 vs 0.92; P = .15). Weak learners outperformed strong ones on the CT-ICH dataset (0.95 vs 0.92; P = .03). Weak learners had better section-level ICH detection performance when more than 10 000 labels were available for training (average f1 = 0.73 vs 0.65; P < .001). Weakly supervised models trained on the entire RSNA dataset required 35 times fewer labels than equivalent strong learners. Conclusion Strongly supervised models did not achieve better performance than weakly supervised ones, which could reduce radiologist labor requirements for prospective dataset curation. Keywords: CT, Head/Neck, Brain/Brain Stem, Hemorrhage Supplemental material is available for this article. © RSNA, 2023 See also commentary by Wahid and Fuentes in this issue.
Collapse
Affiliation(s)
- Jacopo Teneggi
- From the Department of Computer Science (J.T.), Department of
Biomedical Engineering (J.S.), and Mathematical Institute for Data Science
(MINDS) (J.S., J.T.), Johns Hopkins University, 3400 N Charles St, Clark Hall,
Suite 320, Baltimore, MD 21218; and University of Maryland Medical Intelligent
Imaging Center (UM2ii), Department of Diagnostic Radiology and Nuclear Medicine,
University of Maryland School of Medicine, Baltimore, Md (P.H.Y.)
| | - Paul H. Yi
- From the Department of Computer Science (J.T.), Department of
Biomedical Engineering (J.S.), and Mathematical Institute for Data Science
(MINDS) (J.S., J.T.), Johns Hopkins University, 3400 N Charles St, Clark Hall,
Suite 320, Baltimore, MD 21218; and University of Maryland Medical Intelligent
Imaging Center (UM2ii), Department of Diagnostic Radiology and Nuclear Medicine,
University of Maryland School of Medicine, Baltimore, Md (P.H.Y.)
| | - Jeremias Sulam
- From the Department of Computer Science (J.T.), Department of
Biomedical Engineering (J.S.), and Mathematical Institute for Data Science
(MINDS) (J.S., J.T.), Johns Hopkins University, 3400 N Charles St, Clark Hall,
Suite 320, Baltimore, MD 21218; and University of Maryland Medical Intelligent
Imaging Center (UM2ii), Department of Diagnostic Radiology and Nuclear Medicine,
University of Maryland School of Medicine, Baltimore, Md (P.H.Y.)
| |
Collapse
|
5
|
Gerken A, Walluscheck S, Kohlmann P, Galinovic I, Villringer K, Fiebach JB, Klein J, Heldmann S. Deep learning-based segmentation of brain parenchyma and ventricular system in CT scans in the presence of anomalies. FRONTIERS IN NEUROIMAGING 2023; 2:1228255. [PMID: 37554647 PMCID: PMC10406198 DOI: 10.3389/fnimg.2023.1228255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Accepted: 07/18/2023] [Indexed: 08/10/2023]
Abstract
INTRODUCTION The automatic segmentation of brain parenchyma and cerebrospinal fluid-filled spaces such as the ventricular system is the first step for quantitative and qualitative analysis of brain CT data. For clinical practice and especially for diagnostics, it is crucial that such a method is robust to anatomical variability and pathological changes such as (hemorrhagic or neoplastic) lesions and chronic defects. This study investigates the increase in overall robustness of a deep learning algorithm that is gained by adding hemorrhage training data to an otherwise normal training cohort. METHODS A 2D U-Net is trained on subjects with normal appearing brain anatomy. In a second experiment the training data includes additional subjects with brain hemorrhage on image data of the RSNA Brain CT Hemorrhage Challenge with custom reference segmentations. The resulting networks are evaluated on normal and hemorrhage test casesseparately, and on an independent test set of patients with brain tumors of the publicly available GLIS-RT dataset. RESULTS Adding data with hemorrhage to the training set significantly improves the segmentation performance over an algorithm trained exclusively on normally appearing data, not only in the hemorrhage test set but also in the tumor test set. The performance on normally appearing data is stable. Overall, the improved algorithm achieves median Dice scores of 0.98 (parenchyma), 0.91 (left ventricle), 0.90 (right ventricle), 0.81 (third ventricle), and 0.80 (fourth ventricle) on the hemorrhage test set. On the tumor test set, the median Dice scores are 0.96 (parenchyma), 0.90 (left ventricle), 0.90 (right ventricle), 0.75 (third ventricle), and 0.73 (fourth ventricle). CONCLUSION Training on an extended data set that includes pathologies is crucial and significantly increases the overall robustness of a segmentation algorithm for brain parenchyma and ventricular system in CT data, also for anomalies completely unseen during training. Extension of the training set to include other diseases may further improve the generalizability of the algorithm.
Collapse
Affiliation(s)
- Annika Gerken
- Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany
| | - Sina Walluscheck
- Fraunhofer Institute for Digital Medicine MEVIS, Lübeck, Germany
| | - Peter Kohlmann
- Fraunhofer Institute for Digital Medicine MEVIS, Berlin, Germany
| | - Ivana Galinovic
- Center for Stroke Research Berlin (CSB) Charité, Universitätsmedizin, Berlin, Berlin, Germany
| | - Kersten Villringer
- Center for Stroke Research Berlin (CSB) Charité, Universitätsmedizin, Berlin, Berlin, Germany
| | - Jochen B. Fiebach
- Center for Stroke Research Berlin (CSB) Charité, Universitätsmedizin, Berlin, Berlin, Germany
| | - Jan Klein
- Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany
| | - Stefan Heldmann
- Fraunhofer Institute for Digital Medicine MEVIS, Lübeck, Germany
| |
Collapse
|
6
|
Leming MJ, Bron EE, Bruffaerts R, Ou Y, Iglesias JE, Gollub RL, Im H. Challenges of implementing computer-aided diagnostic models for neuroimages in a clinical setting. NPJ Digit Med 2023; 6:129. [PMID: 37443276 DOI: 10.1038/s41746-023-00868-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 06/26/2023] [Indexed: 07/15/2023] Open
Abstract
Advances in artificial intelligence have cultivated a strong interest in developing and validating the clinical utilities of computer-aided diagnostic models. Machine learning for diagnostic neuroimaging has often been applied to detect psychological and neurological disorders, typically on small-scale datasets or data collected in a research setting. With the collection and collation of an ever-growing number of public datasets that researchers can freely access, much work has been done in adapting machine learning models to classify these neuroimages by diseases such as Alzheimer's, ADHD, autism, bipolar disorder, and so on. These studies often come with the promise of being implemented clinically, but despite intense interest in this topic in the laboratory, limited progress has been made in clinical implementation. In this review, we analyze challenges specific to the clinical implementation of diagnostic AI models for neuroimaging data, looking at the differences between laboratory and clinical settings, the inherent limitations of diagnostic AI, and the different incentives and skill sets between research institutions, technology companies, and hospitals. These complexities need to be recognized in the translation of diagnostic AI for neuroimaging from the laboratory to the clinic.
Collapse
Affiliation(s)
- Matthew J Leming
- Center for Systems Biology, Massachusetts General Hospital, Boston, MA, USA.
- Massachusetts Alzheimer's Disease Research Center, Charlestown, MA, USA.
| | - Esther E Bron
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
| | - Rose Bruffaerts
- Computational Neurology, Experimental Neurobiology Unit (ENU), Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
- Biomedical Research Institute, Hasselt University, Diepenbeek, Belgium
| | - Yangming Ou
- Boston Children's Hospital, 300 Longwood Ave, Boston, MA, USA
| | - Juan Eugenio Iglesias
- Center for Medical Image Computing, University College London, London, UK
- Martinos Center for Biomedical Imaging, Harvard Medical School, Boston, MA, USA
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Randy L Gollub
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Hyungsoon Im
- Center for Systems Biology, Massachusetts General Hospital, Boston, MA, USA.
- Massachusetts Alzheimer's Disease Research Center, Charlestown, MA, USA.
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA.
| |
Collapse
|
7
|
Thanellas A, Peura H, Lavinto M, Ruokola T, Vieli M, Staartjes VE, Winklhofer S, Serra C, Regli L, Korja M. Development and External Validation of a Deep Learning Algorithm to Identify and Localize Subarachnoid Hemorrhage on CT Scans. Neurology 2023; 100:e1257-e1266. [PMID: 36639236 PMCID: PMC10033159 DOI: 10.1212/wnl.0000000000201710] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Accepted: 11/07/2022] [Indexed: 01/15/2023] Open
Abstract
BACKGROUND AND OBJECTIVES In medical imaging, a limited number of trained deep learning algorithms have been externally validated and released publicly. We hypothesized that a deep learning algorithm can be trained to identify and localize subarachnoid hemorrhage (SAH) on head computed tomography (CT) scans and that the trained model performs satisfactorily when tested using external and real-world data. METHODS We used noncontrast head CT images of patients admitted to Helsinki University Hospital between 2012 and 2017. We manually segmented (i.e., delineated) SAH on 90 head CT scans and used the segmented CT scans together with 22 negative (no SAH) control CT scans in training an open-source convolutional neural network (U-Net) to identify and localize SAH. We then tested the performance of the trained algorithm by using external data sets (137 SAH and 1,242 control cases) collected in 2 foreign countries and also by creating a data set of consecutive emergency head CT scans (8 SAH and 511 control cases) performed during on-call hours in 5 different domestic hospitals in September 2021. We assessed the algorithm's capability to identify SAH by calculating patient- and slice-level performance metrics, such as sensitivity and specificity. RESULTS In the external validation set of 1,379 cases, the algorithm identified 136 of 137 SAH cases correctly (sensitivity 99.3% and specificity 63.2%). Of the 49,064 axial head CT slices, the algorithm identified and localized SAH in 1845 of 2,110 slices with SAH (sensitivity 87.4% and specificity 95.3%). Of 519 consecutive emergency head CT scans imaged in September 2021, the algorithm identified all 8 SAH cases correctly (sensitivity 100.0% and specificity 75.3%). The slice-level (27,167 axial slices in total) sensitivity and specificity were 87.3% and 98.8%, respectively, as the algorithm identified and localized SAH in 58 of 77 slices with SAH. The performance of the algorithm can be tested on through a web service. DISCUSSION We show that the shared algorithm identifies SAH cases with a high sensitivity and that the slice-level specificity is high. In addition to openly sharing a high-performing deep learning algorithm, our work presents infrequently used approaches in designing, training, testing, and reporting deep learning algorithms developed for medical imaging diagnostics. CLASSIFICATION OF EVIDENCE This study provides Class III evidence that a deep learning algorithm correctly identifies the presence of subarachnoid hemorrhage on CT scan.
Collapse
Affiliation(s)
- Antonios Thanellas
- From the Department of Information Management (A.T.), Helsinki University Hospital, Helsinki, Finland; Department of Neurosurgery, University of Helsinki and Helsinki University Hospital (H.P., M.K.), Helsinki, Finland; CGI (M.L., T.R.), Helsinki, Finland; Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery (M.V., V.E.S., S.W., L.R.), Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland; Department of Neuroradiology (C.S.), Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Heikki Peura
- From the Department of Information Management (A.T.), Helsinki University Hospital, Helsinki, Finland; Department of Neurosurgery, University of Helsinki and Helsinki University Hospital (H.P., M.K.), Helsinki, Finland; CGI (M.L., T.R.), Helsinki, Finland; Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery (M.V., V.E.S., S.W., L.R.), Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland; Department of Neuroradiology (C.S.), Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Mikko Lavinto
- From the Department of Information Management (A.T.), Helsinki University Hospital, Helsinki, Finland; Department of Neurosurgery, University of Helsinki and Helsinki University Hospital (H.P., M.K.), Helsinki, Finland; CGI (M.L., T.R.), Helsinki, Finland; Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery (M.V., V.E.S., S.W., L.R.), Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland; Department of Neuroradiology (C.S.), Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Tomi Ruokola
- From the Department of Information Management (A.T.), Helsinki University Hospital, Helsinki, Finland; Department of Neurosurgery, University of Helsinki and Helsinki University Hospital (H.P., M.K.), Helsinki, Finland; CGI (M.L., T.R.), Helsinki, Finland; Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery (M.V., V.E.S., S.W., L.R.), Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland; Department of Neuroradiology (C.S.), Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Moira Vieli
- From the Department of Information Management (A.T.), Helsinki University Hospital, Helsinki, Finland; Department of Neurosurgery, University of Helsinki and Helsinki University Hospital (H.P., M.K.), Helsinki, Finland; CGI (M.L., T.R.), Helsinki, Finland; Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery (M.V., V.E.S., S.W., L.R.), Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland; Department of Neuroradiology (C.S.), Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Victor E Staartjes
- From the Department of Information Management (A.T.), Helsinki University Hospital, Helsinki, Finland; Department of Neurosurgery, University of Helsinki and Helsinki University Hospital (H.P., M.K.), Helsinki, Finland; CGI (M.L., T.R.), Helsinki, Finland; Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery (M.V., V.E.S., S.W., L.R.), Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland; Department of Neuroradiology (C.S.), Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Sebastian Winklhofer
- From the Department of Information Management (A.T.), Helsinki University Hospital, Helsinki, Finland; Department of Neurosurgery, University of Helsinki and Helsinki University Hospital (H.P., M.K.), Helsinki, Finland; CGI (M.L., T.R.), Helsinki, Finland; Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery (M.V., V.E.S., S.W., L.R.), Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland; Department of Neuroradiology (C.S.), Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Carlo Serra
- From the Department of Information Management (A.T.), Helsinki University Hospital, Helsinki, Finland; Department of Neurosurgery, University of Helsinki and Helsinki University Hospital (H.P., M.K.), Helsinki, Finland; CGI (M.L., T.R.), Helsinki, Finland; Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery (M.V., V.E.S., S.W., L.R.), Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland; Department of Neuroradiology (C.S.), Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Luca Regli
- From the Department of Information Management (A.T.), Helsinki University Hospital, Helsinki, Finland; Department of Neurosurgery, University of Helsinki and Helsinki University Hospital (H.P., M.K.), Helsinki, Finland; CGI (M.L., T.R.), Helsinki, Finland; Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery (M.V., V.E.S., S.W., L.R.), Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland; Department of Neuroradiology (C.S.), Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Miikka Korja
- From the Department of Information Management (A.T.), Helsinki University Hospital, Helsinki, Finland; Department of Neurosurgery, University of Helsinki and Helsinki University Hospital (H.P., M.K.), Helsinki, Finland; CGI (M.L., T.R.), Helsinki, Finland; Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery (M.V., V.E.S., S.W., L.R.), Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland; Department of Neuroradiology (C.S.), Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
| |
Collapse
|
8
|
Srinivasan S, Bai PSM, Mathivanan SK, Muthukumaran V, Babu JC, Vilcekova L. Grade Classification of Tumors from Brain Magnetic Resonance Images Using a Deep Learning Technique. Diagnostics (Basel) 2023; 13:diagnostics13061153. [PMID: 36980463 PMCID: PMC10046932 DOI: 10.3390/diagnostics13061153] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 02/14/2023] [Accepted: 03/14/2023] [Indexed: 03/22/2023] Open
Abstract
To improve the accuracy of tumor identification, it is necessary to develop a reliable automated diagnostic method. In order to precisely categorize brain tumors, researchers developed a variety of segmentation algorithms. Segmentation of brain images is generally recognized as one of the most challenging tasks in medical image processing. In this article, a novel automated detection and classification method was proposed. The proposed approach consisted of many phases, including pre-processing MRI images, segmenting images, extracting features, and classifying images. During the pre-processing portion of an MRI scan, an adaptive filter was utilized to eliminate background noise. For feature extraction, the local-binary grey level co-occurrence matrix (LBGLCM) was used, and for image segmentation, enhanced fuzzy c-means clustering (EFCMC) was used. After extracting the scan features, we used a deep learning model to classify MRI images into two groups: glioma and normal. The classifications were created using a convolutional recurrent neural network (CRNN). The proposed technique improved brain image classification from a defined input dataset. MRI scans from the REMBRANDT dataset, which consisted of 620 testing and 2480 training sets, were used for the research. The data demonstrate that the newly proposed method outperformed its predecessors. The proposed CRNN strategy was compared against BP, U-Net, and ResNet, which are three of the most prevalent classification approaches currently being used. For brain tumor classification, the proposed system outcomes were 98.17% accuracy, 91.34% specificity, and 98.79% sensitivity.
Collapse
Affiliation(s)
- Saravanan Srinivasan
- Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai 600062, India
| | | | - Sandeep Kumar Mathivanan
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore 632014, India
| | - Venkatesan Muthukumaran
- Department of Mathematics, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur 603203, India
| | - Jyothi Chinna Babu
- Department of Electronics and Communications Engineering, Annamacharya Institute of Technology and Sciences, Rajampet 516126, India
| | - Lucia Vilcekova
- Faculty of Management, Comenius University Bratislava, Odbojarov 10, 820 05 Bratislava, Slovakia
- Correspondence:
| |
Collapse
|
9
|
Intracranial Hemorrhage Detection Using Parallel Deep Convolutional Models and Boosting Mechanism. Diagnostics (Basel) 2023; 13:diagnostics13040652. [PMID: 36832137 PMCID: PMC9955715 DOI: 10.3390/diagnostics13040652] [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: 12/21/2022] [Revised: 01/31/2023] [Accepted: 02/02/2023] [Indexed: 02/12/2023] Open
Abstract
Intracranial hemorrhage (ICH) can lead to death or disability, which requires immediate action from radiologists. Due to the heavy workload, less experienced staff, and the complexity of subtle hemorrhages, a more intelligent and automated system is necessary to detect ICH. In literature, many artificial-intelligence-based methods are proposed. However, they are less accurate for ICH detection and subtype classification. Therefore, in this paper, we present a new methodology to improve the detection and subtype classification of ICH based on two parallel paths and a boosting technique. The first path employs the architecture of ResNet101-V2 to extract potential features from windowed slices, whereas Inception-V4 captures significant spatial information in the second path. Afterwards, the detection and subtype classification of ICH is performed by the light gradient boosting machine (LGBM) using the outputs of ResNet101-V2 and Inception-V4. Thus, the combined solution, known as ResNet101-V2, Inception-V4, and LGBM (Res-Inc-LGBM), is trained and tested over the brain computed tomography (CT) scans of CQ500 and Radiological Society of North America (RSNA) datasets. The experimental results state that the proposed solution efficiently obtains 97.7% accuracy, 96.5% sensitivity, and 97.4% F1 score using the RSNA dataset. Moreover, the proposed Res-Inc-LGBM outperforms the standard benchmarks for the detection and subtype classification of ICH regarding the accuracy, sensitivity, and F1 score. The results prove the significance of the proposed solution for its real-time application.
Collapse
|
10
|
Retrospective analysis and prospective validation of an AI-based software for intracranial haemorrhage detection at a high-volume trauma centre. Sci Rep 2022; 12:19885. [PMID: 36400834 PMCID: PMC9674833 DOI: 10.1038/s41598-022-24504-y] [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: 06/08/2022] [Accepted: 11/16/2022] [Indexed: 11/19/2022] Open
Abstract
Rapid detection of intracranial haemorrhage (ICH) is crucial for assessing patients with neurological symptoms. Prioritising these urgent scans for reporting presents a challenge for radiologists. Artificial intelligence (AI) offers a solution to enable radiologists to triage urgent scans and reduce reporting errors. This study aims to evaluate the accuracy of an ICH-detection AI software and whether it benefits a high-volume trauma centre in terms of triage and reducing diagnostic errors. A peer review of head CT scans performed prior to the implementation of the AI was conducted to identify the department's current miss-rate. Once implemented, the AI software was validated using CT scans performed over one month, and was reviewed by a neuroradiologist. The turn-around-time was calculated as the time taken from scan completion to report finalisation. 2916 head CT scans and reports were reviewed as part of the audit. The AI software flagged 20 cases that were negative-by-report. Two of these were true-misses that had no follow-up imaging. Both patients were followed up and exhibited no long-term neurological sequelae. For ICH-positive scans, there was an increase in TAT in the total sample (35.6%), and a statistically insignificant decrease in TAT in the emergency (- 5.1%) and outpatient (- 14.2%) cohorts. The AI software was tested on a sample of real-world data from a high-volume Australian centre. The diagnostic accuracy was comparable to that reported in literature. The study demonstrated the institution's low miss-rate and short reporting time, therefore any improvements from the use of AI would be marginal and challenging to measure.
Collapse
|
11
|
Altenbernd JC, Fischer S, Scharbrodt W, Schimrigk S, Eyding J, Nordmeyer H, Wohlert C, Dörner N, Li Y, Wrede K, Pierscianek D, Köhrmann M, Frank B, Forsting M, Deuschl C. CT and DSA for evaluation of spontaneous intracerebral lobar bleedings. Front Neurol 2022; 13:956888. [PMID: 36262835 PMCID: PMC9574012 DOI: 10.3389/fneur.2022.956888] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Accepted: 08/12/2022] [Indexed: 11/18/2022] Open
Abstract
Purpose This study retrospectively examined the extent to which computed tomography angiography (CTA) and digital subtraction angiography (DSA) can help identify the cause of lobar intracerebral bleeding. Materials and methods In the period from 2002 to 2020, data from patients who were >18 years at a university and an academic teaching hospital with lobar intracerebral bleeding were evaluated retrospectively. The CTA DSA data were reviewed separately by two neuroradiologists, and differences in opinion were resolved by consensus after discussion. A positive finding was defined as an underlying vascular etiology of lobar bleeding. Results The data of 412 patients were retrospectively investigated. DSA detected a macrovascular cause of bleeding in 125/412 patients (33%). In total, sixty patients had AVMs (15%), 30 patients with aneurysms (7%), 12 patients with vasculitis (3%), and 23 patients with dural fistulas (6%). The sensitivity, specificity, positive and negative predictive values, and accuracy of CTA compared with DSA were 93, 97, 100, and 97%. There were false-negative CTA readings for two AVMs and one dural fistula. Conclusion The DSA is still the gold standard diagnostic modality for detecting macrovascular causes of ICH; however, most patients with lobar ICH can be investigated first with CTA, and the cause of bleeding can be found. Our results showed higher sensitivity and specificity than those of other CTA studies.
Collapse
Affiliation(s)
- Jens-Christian Altenbernd
- Department of Radiology, Gemeinschaftskrankenhaus, Herdecke, Germany
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
- *Correspondence: Jens-Christian Altenbernd
| | | | | | | | - Jens Eyding
- Department of Neurology, Gemeinschaftskrankenhaus, Herdecke, Germany
| | | | - Christine Wohlert
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Nils Dörner
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Yan Li
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Karsten Wrede
- Department of Neurosurgery, University Hospital Essen, Essen, Germany
| | | | - Martin Köhrmann
- Department of Neurology, University Hospital Essen, Essen, Germany
| | - Benedikt Frank
- Department of Neurology, University Hospital Essen, Essen, Germany
| | - Michael Forsting
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Cornelius Deuschl
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| |
Collapse
|
12
|
Lam TYT, Cheung MFK, Munro YL, Lim KM, Shung D, Sung JJY. Randomized Controlled Trials of Artificial Intelligence in Clinical Practice: Systematic Review. J Med Internet Res 2022; 24:e37188. [PMID: 35904087 PMCID: PMC9459941 DOI: 10.2196/37188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 06/13/2022] [Accepted: 07/29/2022] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND The number of artificial intelligence (AI) studies in medicine has exponentially increased recently. However, there is no clear quantification of the clinical benefits of implementing AI-assisted tools in patient care. OBJECTIVE This study aims to systematically review all published randomized controlled trials (RCTs) of AI-assisted tools to characterize their performance in clinical practice. METHODS CINAHL, Cochrane Central, Embase, MEDLINE, and PubMed were searched to identify relevant RCTs published up to July 2021 and comparing the performance of AI-assisted tools with conventional clinical management without AI assistance. We evaluated the primary end points of each study to determine their clinical relevance. This systematic review was conducted following the updated PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines. RESULTS Among the 11,839 articles retrieved, only 39 (0.33%) RCTs were included. These RCTs were conducted in an approximately equal distribution from North America, Europe, and Asia. AI-assisted tools were implemented in 13 different clinical specialties. Most RCTs were published in the field of gastroenterology, with 15 studies on AI-assisted endoscopy. Most RCTs studied biosignal-based AI-assisted tools, and a minority of RCTs studied AI-assisted tools drawn from clinical data. In 77% (30/39) of the RCTs, AI-assisted interventions outperformed usual clinical care, and clinically relevant outcomes improved with AI-assisted intervention in 70% (21/30) of the studies. Small sample size and single-center design limited the generalizability of these studies. CONCLUSIONS There is growing evidence supporting the implementation of AI-assisted tools in daily clinical practice; however, the number of available RCTs is limited and heterogeneous. More RCTs of AI-assisted tools integrated into clinical practice are needed to advance the role of AI in medicine. TRIAL REGISTRATION PROSPERO CRD42021286539; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=286539.
Collapse
Affiliation(s)
- Thomas Y T Lam
- The Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, Hong Kong
- Stanley Ho Big Data Decision Analytics Research Centre, The Chinese University of Hong Kong., Hong Kong, Hong Kong
| | - Max F K Cheung
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Yasmin L Munro
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Kong Meng Lim
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Dennis Shung
- Department of Medicine (Digestive Diseases), Yale School of Medicine, New Haven, CT, United States
| | - Joseph J Y Sung
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| |
Collapse
|
13
|
Matsoukas S, Scaggiante J, Schuldt BR, Smith CJ, Chennareddy S, Kalagara R, Majidi S, Bederson JB, Fifi JT, Mocco J, Kellner CP. Accuracy of artificial intelligence for the detection of intracranial hemorrhage and chronic cerebral microbleeds: a systematic review and pooled analysis. LA RADIOLOGIA MEDICA 2022; 127:1106-1123. [PMID: 35962888 DOI: 10.1007/s11547-022-01530-4] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Accepted: 07/12/2022] [Indexed: 11/25/2022]
Abstract
BACKGROUND Artificial intelligence (AI)-driven software has been developed and become commercially available within the past few years for the detection of intracranial hemorrhage (ICH) and chronic cerebral microbleeds (CMBs). However, there is currently no systematic review that summarizes all of these tools or provides pooled estimates of their performance. METHODS In this PROSPERO-registered, PRISMA compliant systematic review, we sought to compile and review all MEDLINE and EMBASE published studies that have developed and/or tested AI algorithms for ICH detection on non-contrast CT scans (NCCTs) or MRI scans and CMBs detection on MRI scans. RESULTS In total, 40 studies described AI algorithms for ICH detection in NCCTs/MRIs and 19 for CMBs detection in MRIs. The overall sensitivity, specificity, and accuracy were 92.06%, 93.54%, and 93.46%, respectively, for ICH detection and 91.6%, 93.9%, and 92.7% for CMBs detection. Some of the challenges encountered in the development of these algorithms include the laborious work of creating large, labeled and balanced datasets, the volumetric nature of the imaging examinations, the fine tuning of the algorithms, and the reduction in false positives. CONCLUSIONS Numerous AI-driven software tools have been developed over the last decade. On average, they are characterized by high performance and expert-level accuracy for the diagnosis of ICH and CMBs. As a result, implementing these tools in clinical practice may improve workflow and act as a failsafe for the detection of such lesions. REGISTRATION-URL: https://www.crd.york.ac.uk/prospero/ Unique Identifier: CRD42021246848.
Collapse
Affiliation(s)
- Stavros Matsoukas
- Department of Neurosurgery, Mount Sinai Health System, Annenberg Building, Room 20-86, 1468 Madison Ave, New York, NY, 10029, USA.
| | - Jacopo Scaggiante
- Department of Neurosurgery, Mount Sinai Health System, Annenberg Building, Room 20-86, 1468 Madison Ave, New York, NY, 10029, USA
| | - Braxton R Schuldt
- Department of Neurosurgery, Mount Sinai Health System, Annenberg Building, Room 20-86, 1468 Madison Ave, New York, NY, 10029, USA
| | - Colton J Smith
- Department of Neurosurgery, Mount Sinai Health System, Annenberg Building, Room 20-86, 1468 Madison Ave, New York, NY, 10029, USA
| | - Susmita Chennareddy
- Department of Neurosurgery, Mount Sinai Health System, Annenberg Building, Room 20-86, 1468 Madison Ave, New York, NY, 10029, USA
| | - Roshini Kalagara
- Department of Neurosurgery, Mount Sinai Health System, Annenberg Building, Room 20-86, 1468 Madison Ave, New York, NY, 10029, USA
| | - Shahram Majidi
- Department of Neurosurgery, Mount Sinai Health System, Annenberg Building, Room 20-86, 1468 Madison Ave, New York, NY, 10029, USA
| | - Joshua B Bederson
- Department of Neurosurgery, Mount Sinai Health System, Annenberg Building, Room 20-86, 1468 Madison Ave, New York, NY, 10029, USA
| | - Johanna T Fifi
- Department of Neurosurgery, Mount Sinai Health System, Annenberg Building, Room 20-86, 1468 Madison Ave, New York, NY, 10029, USA
| | - J Mocco
- Department of Neurosurgery, Mount Sinai Health System, Annenberg Building, Room 20-86, 1468 Madison Ave, New York, NY, 10029, USA
| | - Christopher P Kellner
- Department of Neurosurgery, Mount Sinai Health System, Annenberg Building, Room 20-86, 1468 Madison Ave, New York, NY, 10029, USA
| |
Collapse
|
14
|
Brain Tumor Analysis Using Deep Learning and VGG-16 Ensembling Learning Approaches. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12147282] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
A brain tumor is a distorted tissue wherein cells replicate rapidly and indefinitely, with no control over tumor growth. Deep learning has been argued to have the potential to overcome the challenges associated with detecting and intervening in brain tumors. It is well established that the segmentation method can be used to remove abnormal tumor regions from the brain, as this is one of the advanced technological classification and detection tools. In the case of brain tumors, early disease detection can be achieved effectively using reliable advanced A.I. and Neural Network classification algorithms. This study aimed to critically analyze the proposed literature solutions, use the Visual Geometry Group (VGG 16) for discovering brain tumors, implement a convolutional neural network (CNN) model framework, and set parameters to train the model for this challenge. VGG is used as one of the highest-performing CNN models because of its simplicity. Furthermore, the study developed an effective approach to detect brain tumors using MRI to aid in making quick, efficient, and precise decisions. Faster CNN used the VGG 16 architecture as a primary network to generate convolutional feature maps, then classified these to yield tumor region suggestions. The prediction accuracy was used to assess performance. Our suggested methodology was evaluated on a dataset for brain tumor diagnosis using MR images comprising 253 MRI brain images, with 155 showing tumors. Our approach could identify brain tumors in MR images. In the testing data, the algorithm outperformed the current conventional approaches for detecting brain tumors (Precision = 96%, 98.15%, 98.41% and F1-score = 91.78%, 92.6% and 91.29% respectively) and achieved an excellent accuracy of CNN 96%, VGG 16 98.5% and Ensemble Model 98.14%. The study also presents future recommendations regarding the proposed research work.
Collapse
|
15
|
Wardlaw JM, Mair G, von Kummer R, Williams MC, Li W, Storkey AJ, Trucco E, Liebeskind DS, Farrall A, Bath PM, White P. Accuracy of Automated Computer-Aided Diagnosis for Stroke Imaging: A Critical Evaluation of Current Evidence. Stroke 2022; 53:2393-2403. [PMID: 35440170 DOI: 10.1161/strokeaha.121.036204] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
There is increasing interest in computer applications, using artificial intelligence methodologies, to perform health care tasks previously performed by humans, particularly in medical imaging for diagnosis. In stroke, there are now commercial artificial intelligence software for use with computed tomography or MR imaging to identify acute ischemic brain tissue pathology, arterial obstruction on computed tomography angiography or as hyperattenuated arteries on computed tomography, brain hemorrhage, or size of perfusion defects. A rapid, accurate diagnosis may aid treatment decisions for individual patients and could improve outcome if it leads to effective and safe treatment; or conversely, to disaster if a delayed or incorrect diagnosis results in inappropriate treatment. Despite this potential clinical impact, diagnostic tools including artificial intelligence methods are not subjected to the same clinical evaluation standards as are mandatory for drugs. Here, we provide an evidence-based review of the pros and cons of commercially available automated methods for medical imaging diagnosis, including those based on artificial intelligence, to diagnose acute brain pathology on computed tomography or magnetic resonance imaging in patients with stroke.
Collapse
Affiliation(s)
- Joanna M Wardlaw
- Centre for Clinical Brain Sciences, UK Dementia Research Institute Centre at the University of Edinburgh, Little France, United Kingdom (J.M.W., G.M., W.L., A.F.)
| | - Grant Mair
- Centre for Clinical Brain Sciences, UK Dementia Research Institute Centre at the University of Edinburgh, Little France, United Kingdom (J.M.W., G.M., W.L., A.F.)
| | - Rüdiger von Kummer
- Institute of Diagnostic and Interventional Neuroradiology, Universitätsklinikum Carl Gustav Carus, Dresden, Germany (R.v.K.)
| | - Michelle C Williams
- Centre for Cardiovascular Science, University of Edinburgh, Little France, United Kingdom (M.C.W.)
| | - Wenwen Li
- Centre for Clinical Brain Sciences, UK Dementia Research Institute Centre at the University of Edinburgh, Little France, United Kingdom (J.M.W., G.M., W.L., A.F.)
| | | | - Emanuel Trucco
- VAMPIRE project, Computing, School of Science and Engineering, University of Dundee (E.T.)
| | | | - Andrew Farrall
- Centre for Clinical Brain Sciences, UK Dementia Research Institute Centre at the University of Edinburgh, Little France, United Kingdom (J.M.W., G.M., W.L., A.F.)
| | - Philip M Bath
- Stroke Trials Unit, Mental Health & Clinical Neuroscience, University of Nottingham, Queen's Medical Centre campus, United Kingdom (P.M.B.)
| | - Philip White
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne and Newcastle upon Tyne Hospitals NHS Trust, United Kingdom (P.W.)
| |
Collapse
|
16
|
Deep Learning Paradigm for Cardiovascular Disease/Stroke Risk Stratification in Parkinson’s Disease Affected by COVID-19: A Narrative Review. Diagnostics (Basel) 2022; 12:diagnostics12071543. [PMID: 35885449 PMCID: PMC9324237 DOI: 10.3390/diagnostics12071543] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Revised: 06/14/2022] [Accepted: 06/16/2022] [Indexed: 11/16/2022] Open
Abstract
Background and Motivation: Parkinson’s disease (PD) is one of the most serious, non-curable, and expensive to treat. Recently, machine learning (ML) has shown to be able to predict cardiovascular/stroke risk in PD patients. The presence of COVID-19 causes the ML systems to become severely non-linear and poses challenges in cardiovascular/stroke risk stratification. Further, due to comorbidity, sample size constraints, and poor scientific and clinical validation techniques, there have been no well-explained ML paradigms. Deep neural networks are powerful learning machines that generalize non-linear conditions. This study presents a novel investigation of deep learning (DL) solutions for CVD/stroke risk prediction in PD patients affected by the COVID-19 framework. Method: The PRISMA search strategy was used for the selection of 292 studies closely associated with the effect of PD on CVD risk in the COVID-19 framework. We study the hypothesis that PD in the presence of COVID-19 can cause more harm to the heart and brain than in non-COVID-19 conditions. COVID-19 lung damage severity can be used as a covariate during DL training model designs. We, therefore, propose a DL model for the estimation of, (i) COVID-19 lesions in computed tomography (CT) scans and (ii) combining the covariates of PD, COVID-19 lesions, office and laboratory arterial atherosclerotic image-based biomarkers, and medicine usage for the PD patients for the design of DL point-based models for CVD/stroke risk stratification. Results: We validated the feasibility of CVD/stroke risk stratification in PD patients in the presence of a COVID-19 environment and this was also verified. DL architectures like long short-term memory (LSTM), and recurrent neural network (RNN) were studied for CVD/stroke risk stratification showing powerful designs. Lastly, we examined the artificial intelligence bias and provided recommendations for early detection of CVD/stroke in PD patients in the presence of COVID-19. Conclusion: The DL is a very powerful tool for predicting CVD/stroke risk in PD patients affected by COVID-19.
Collapse
|
17
|
Charting the potential of brain computed tomography deep learning systems. J Clin Neurosci 2022; 99:217-223. [DOI: 10.1016/j.jocn.2022.03.014] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Revised: 02/17/2022] [Accepted: 03/08/2022] [Indexed: 12/22/2022]
|
18
|
Bravo J, Wali AR, Hirshman BR, Gopesh T, Steinberg JA, Yan B, Pannell JS, Norbash A, Friend J, Khalessi AA, Santiago-Dieppa D. Robotics and Artificial Intelligence in Endovascular Neurosurgery. Cureus 2022; 14:e23662. [PMID: 35371874 PMCID: PMC8971092 DOI: 10.7759/cureus.23662] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/30/2022] [Indexed: 11/05/2022] Open
Abstract
The use of artificial intelligence (AI) and robotics in endovascular neurosurgery promises to transform neurovascular care. We present a review of the recently published neurosurgical literature on artificial intelligence and robotics in endovascular neurosurgery to provide insights into the current advances and applications of this technology. The PubMed database was searched for "neurosurgery" OR "endovascular" OR "interventional" AND "robotics" OR "artificial intelligence" between January 2016 and August 2021. A total of 1296 articles were identified, and after applying the inclusion and exclusion criteria, 38 manuscripts were selected for review and analysis. These manuscripts were divided into four categories: 1) robotics and AI for the diagnosis of cerebrovascular pathology, 2) robotics and AI for the treatment of cerebrovascular pathology, 3) robotics and AI for training in neuroendovascular procedures, and 4) robotics and AI for clinical outcome optimization. The 38 articles presented include 23 articles on AI-based diagnosis of cerebrovascular disease, 10 articles on AI-based treatment of cerebrovascular disease, two articles on AI-based training techniques for neuroendovascular procedures, and three articles reporting AI prediction models of clinical outcomes in vascular disorders of the brain. Innovation with robotics and AI focus on diagnostic efficiency, optimizing treatment and interventional procedures, improving physician procedural performance, and predicting clinical outcomes with the use of artificial intelligence and robotics. Experimental studies with robotic systems have demonstrated safety and efficacy in treating cerebrovascular disorders, and novel microcatheterization techniques may permit access to deeper brain regions. Other studies show that pre-procedural simulations increase overall physician performance. Artificial intelligence also shows superiority over existing statistical tools in predicting clinical outcomes. The recent advances and current usage of robotics and AI in the endovascular neurosurgery field suggest that the collaboration between physicians and machines has a bright future for the improvement of patient care. The aim of this work is to equip the medical readership, in particular the neurosurgical specialty, with tools to better understand and apply findings from research on artificial intelligence and robotics in endovascular neurosurgery.
Collapse
|
19
|
Seyam M, Weikert T, Sauter A, Brehm A, Psychogios MN, Blackham KA. Utilization of Artificial Intelligence-based Intracranial Hemorrhage Detection on Emergent Noncontrast CT Images in Clinical Workflow. Radiol Artif Intell 2022; 4:e210168. [PMID: 35391777 DOI: 10.1148/ryai.210168] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2021] [Revised: 01/10/2022] [Accepted: 01/20/2022] [Indexed: 01/23/2023]
Abstract
Authors implemented an artificial intelligence (AI)-based detection tool for intracranial hemorrhage (ICH) on noncontrast CT images into an emergent workflow, evaluated its diagnostic performance, and assessed clinical workflow metrics compared with pre-AI implementation. The finalized radiology report constituted the ground truth for the analysis, and CT examinations (n = 4450) before and after implementation were retrieved using various keywords for ICH. Diagnostic performance was assessed, and mean values with their respective 95% CIs were reported to compare workflow metrics (report turnaround time, communication time of a finding, consultation time of another specialty, and turnaround time in the emergency department). Although practicable diagnostic performance was observed for overall ICH detection with 93.0% diagnostic accuracy, 87.2% sensitivity, and 97.8% negative predictive value, the tool yielded lower detection rates for specific subtypes of ICH (eg, 69.2% [74 of 107] for subdural hemorrhage and 77.4% [24 of 31] for acute subarachnoid hemorrhage). Common false-positive findings included postoperative and postischemic defects (23.6%, 37 of 157), artifacts (19.7%, 31 of 157), and tumors (15.3%, 24 of 157). Although workflow metrics such as communicating a critical finding (70 minutes [95% CI: 54, 85] vs 63 minutes [95% CI: 55, 71]) were on average reduced after implementation, future efforts are necessary to streamline the workflow all along the workflow chain. It is crucial to define a clear framework and recognize limitations as AI tools are only as reliable as the environment in which they are deployed. Keywords: CT, CNS, Stroke, Diagnosis, Classification, Application Domain © RSNA, 2022.
Collapse
Affiliation(s)
- Muhannad Seyam
- Department of Diagnostic and Interventional Neuroradiology, Clinic of Radiology and Nuclear Medicine (M.S., A.B., M.N.P., K.A.B.), and Department of Radiology and Nuclear Medicine (T.W., A.S.), University Hospital of Basel, Petersgraben 4, 4031 Basel, Switzerland; and Department of Neurologic Sciences, University of Vermont Medical Center, Burlington, Vt (M.S.)
| | - Thomas Weikert
- Department of Diagnostic and Interventional Neuroradiology, Clinic of Radiology and Nuclear Medicine (M.S., A.B., M.N.P., K.A.B.), and Department of Radiology and Nuclear Medicine (T.W., A.S.), University Hospital of Basel, Petersgraben 4, 4031 Basel, Switzerland; and Department of Neurologic Sciences, University of Vermont Medical Center, Burlington, Vt (M.S.)
| | - Alexander Sauter
- Department of Diagnostic and Interventional Neuroradiology, Clinic of Radiology and Nuclear Medicine (M.S., A.B., M.N.P., K.A.B.), and Department of Radiology and Nuclear Medicine (T.W., A.S.), University Hospital of Basel, Petersgraben 4, 4031 Basel, Switzerland; and Department of Neurologic Sciences, University of Vermont Medical Center, Burlington, Vt (M.S.)
| | - Alex Brehm
- Department of Diagnostic and Interventional Neuroradiology, Clinic of Radiology and Nuclear Medicine (M.S., A.B., M.N.P., K.A.B.), and Department of Radiology and Nuclear Medicine (T.W., A.S.), University Hospital of Basel, Petersgraben 4, 4031 Basel, Switzerland; and Department of Neurologic Sciences, University of Vermont Medical Center, Burlington, Vt (M.S.)
| | - Marios-Nikos Psychogios
- Department of Diagnostic and Interventional Neuroradiology, Clinic of Radiology and Nuclear Medicine (M.S., A.B., M.N.P., K.A.B.), and Department of Radiology and Nuclear Medicine (T.W., A.S.), University Hospital of Basel, Petersgraben 4, 4031 Basel, Switzerland; and Department of Neurologic Sciences, University of Vermont Medical Center, Burlington, Vt (M.S.)
| | - Kristine A Blackham
- Department of Diagnostic and Interventional Neuroradiology, Clinic of Radiology and Nuclear Medicine (M.S., A.B., M.N.P., K.A.B.), and Department of Radiology and Nuclear Medicine (T.W., A.S.), University Hospital of Basel, Petersgraben 4, 4031 Basel, Switzerland; and Department of Neurologic Sciences, University of Vermont Medical Center, Burlington, Vt (M.S.)
| |
Collapse
|
20
|
Zeleňák K, Krajina A, Meyer L, Fiehler J, Behme D, Bulja D, Caroff J, Chotai AA, Da Ros V, Gentric JC, Hofmeister J, Kass-Hout O, Kocatürk Ö, Lynch J, Pearson E, Vukasinovic I. How to Improve the Management of Acute Ischemic Stroke by Modern Technologies, Artificial Intelligence, and New Treatment Methods. Life (Basel) 2021; 11:life11060488. [PMID: 34072071 PMCID: PMC8229281 DOI: 10.3390/life11060488] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 05/25/2021] [Accepted: 05/25/2021] [Indexed: 12/22/2022] Open
Abstract
Stroke remains one of the leading causes of death and disability in Europe. The European Stroke Action Plan (ESAP) defines four main targets for the years 2018 to 2030. The COVID-19 pandemic forced the use of innovative technologies and created pressure to improve internet networks. Moreover, 5G internet network will be helpful for the transfer and collecting of extremely big databases. Nowadays, the speed of internet connection is a limiting factor for robotic systems, which can be controlled and commanded potentially from various places in the world. Innovative technologies can be implemented for acute stroke patient management soon. Artificial intelligence (AI) and robotics are used increasingly often without the exception of medicine. Their implementation can be achieved in every level of stroke care. In this article, all steps of stroke health care processes are discussed in terms of how to improve them (including prehospital diagnosis, consultation, transfer of the patient, diagnosis, techniques of the treatment as well as rehabilitation and usage of AI). New ethical problems have also been discovered. Everything must be aligned to the concept of “time is brain”.
Collapse
Affiliation(s)
- Kamil Zeleňák
- Clinic of Radiology, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, 03659 Martin, Slovakia
- ESMINT Artificial Intelligence and Robotics Ad hoc Committee, ESMINT, 8008 Zurich, Switzerland; (E.A.I.R.A.h.C.); (D.B.); (D.B.); (J.C.); (A.A.C.); (V.D.R.); (J.-C.G.); (J.H.); (O.K.-H.); (Ö.K.); (J.L.); (E.P.); (I.V.)
- Correspondence: ; Tel.: +421-43-4203-990
| | - Antonín Krajina
- Department of Radiology, Charles University Faculty of Medicine and University Hospital, CZ-500 05 Hradec Králové, Czech Republic;
| | - Lukas Meyer
- Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, 20251 Hamburg, Germany; (L.M.); (J.F.)
| | - Jens Fiehler
- Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, 20251 Hamburg, Germany; (L.M.); (J.F.)
| | | | - Daniel Behme
- ESMINT Artificial Intelligence and Robotics Ad hoc Committee, ESMINT, 8008 Zurich, Switzerland; (E.A.I.R.A.h.C.); (D.B.); (D.B.); (J.C.); (A.A.C.); (V.D.R.); (J.-C.G.); (J.H.); (O.K.-H.); (Ö.K.); (J.L.); (E.P.); (I.V.)
- University Clinic for Neuroradiology, Medical Faculty, Otto-von-Guericke University Magdeburg, 39120 Magdeburg, Germany
| | - Deniz Bulja
- ESMINT Artificial Intelligence and Robotics Ad hoc Committee, ESMINT, 8008 Zurich, Switzerland; (E.A.I.R.A.h.C.); (D.B.); (D.B.); (J.C.); (A.A.C.); (V.D.R.); (J.-C.G.); (J.H.); (O.K.-H.); (Ö.K.); (J.L.); (E.P.); (I.V.)
- Diagnostic-Interventional Radiology Department, Clinic of Radiology, Clinical Center of University of Sarajevo, 71000 Sarajevo, Bosnia and Herzegovina
| | - Jildaz Caroff
- ESMINT Artificial Intelligence and Robotics Ad hoc Committee, ESMINT, 8008 Zurich, Switzerland; (E.A.I.R.A.h.C.); (D.B.); (D.B.); (J.C.); (A.A.C.); (V.D.R.); (J.-C.G.); (J.H.); (O.K.-H.); (Ö.K.); (J.L.); (E.P.); (I.V.)
- Department of Interventional Neuroradiology–NEURI Brain Vascular Center, Bicêtre Hospital, APHP, 94270 Paris, France
| | - Amar Ajay Chotai
- ESMINT Artificial Intelligence and Robotics Ad hoc Committee, ESMINT, 8008 Zurich, Switzerland; (E.A.I.R.A.h.C.); (D.B.); (D.B.); (J.C.); (A.A.C.); (V.D.R.); (J.-C.G.); (J.H.); (O.K.-H.); (Ö.K.); (J.L.); (E.P.); (I.V.)
- Department of Neuroradiology, Royal Victoria Infirmary, Newcastle upon Tyne NE14LP, UK
| | - Valerio Da Ros
- ESMINT Artificial Intelligence and Robotics Ad hoc Committee, ESMINT, 8008 Zurich, Switzerland; (E.A.I.R.A.h.C.); (D.B.); (D.B.); (J.C.); (A.A.C.); (V.D.R.); (J.-C.G.); (J.H.); (O.K.-H.); (Ö.K.); (J.L.); (E.P.); (I.V.)
- Department of Biomedicine and Prevention, University Hospital of Rome “Tor Vergata”, 00133 Rome, Italy
| | - Jean-Christophe Gentric
- ESMINT Artificial Intelligence and Robotics Ad hoc Committee, ESMINT, 8008 Zurich, Switzerland; (E.A.I.R.A.h.C.); (D.B.); (D.B.); (J.C.); (A.A.C.); (V.D.R.); (J.-C.G.); (J.H.); (O.K.-H.); (Ö.K.); (J.L.); (E.P.); (I.V.)
- Interventional Neuroradiology Unit, Hôpital de la Cavale Blanche, 29200 Brest, France
| | - Jeremy Hofmeister
- ESMINT Artificial Intelligence and Robotics Ad hoc Committee, ESMINT, 8008 Zurich, Switzerland; (E.A.I.R.A.h.C.); (D.B.); (D.B.); (J.C.); (A.A.C.); (V.D.R.); (J.-C.G.); (J.H.); (O.K.-H.); (Ö.K.); (J.L.); (E.P.); (I.V.)
- Unité de Neuroradiologie Interventionnelle, Service de Neuroradiologie Diagnostique et Interventionnelle, 1205 Genève, Switzerland
| | - Omar Kass-Hout
- ESMINT Artificial Intelligence and Robotics Ad hoc Committee, ESMINT, 8008 Zurich, Switzerland; (E.A.I.R.A.h.C.); (D.B.); (D.B.); (J.C.); (A.A.C.); (V.D.R.); (J.-C.G.); (J.H.); (O.K.-H.); (Ö.K.); (J.L.); (E.P.); (I.V.)
- Stroke and Neuroendovascular Surgery, Rex Hospital, University of North Carolina, 4207 Lake Boone Trail, Suite 220, Raleigh, NC 27607, USA
| | - Özcan Kocatürk
- ESMINT Artificial Intelligence and Robotics Ad hoc Committee, ESMINT, 8008 Zurich, Switzerland; (E.A.I.R.A.h.C.); (D.B.); (D.B.); (J.C.); (A.A.C.); (V.D.R.); (J.-C.G.); (J.H.); (O.K.-H.); (Ö.K.); (J.L.); (E.P.); (I.V.)
- Balikesir Atatürk City Hospital, Gaziosmanpaşa Mahallesi 209., Sok. No: 26, 10100 Altıeylül/Balıkesir, Turkey
| | - Jeremy Lynch
- ESMINT Artificial Intelligence and Robotics Ad hoc Committee, ESMINT, 8008 Zurich, Switzerland; (E.A.I.R.A.h.C.); (D.B.); (D.B.); (J.C.); (A.A.C.); (V.D.R.); (J.-C.G.); (J.H.); (O.K.-H.); (Ö.K.); (J.L.); (E.P.); (I.V.)
- Department of Neuroradiology, Toronto Western Hospital, Toronto, ON M5T 2S8, Canada
| | - Ernesto Pearson
- ESMINT Artificial Intelligence and Robotics Ad hoc Committee, ESMINT, 8008 Zurich, Switzerland; (E.A.I.R.A.h.C.); (D.B.); (D.B.); (J.C.); (A.A.C.); (V.D.R.); (J.-C.G.); (J.H.); (O.K.-H.); (Ö.K.); (J.L.); (E.P.); (I.V.)
- CH Bergerac-Centre Hospitalier, Samuel Pozzi 9 Boulevard du Professeur Albert Calmette, 24100 Bergerac, France
| | - Ivan Vukasinovic
- ESMINT Artificial Intelligence and Robotics Ad hoc Committee, ESMINT, 8008 Zurich, Switzerland; (E.A.I.R.A.h.C.); (D.B.); (D.B.); (J.C.); (A.A.C.); (V.D.R.); (J.-C.G.); (J.H.); (O.K.-H.); (Ö.K.); (J.L.); (E.P.); (I.V.)
- Department of Neuroradiology, University Clinical Center of Serbia, 11000 Belgrade, Serbia
| |
Collapse
|