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Craine A, Scott A, Desai D, Kligerman S, Adler E, Kim NH, Alshawabkeh L, Contijoch F. 3D regional evaluation of right ventricular myocardial work from cineCT. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.07.30.24311094. [PMID: 39132470 PMCID: PMC11312672 DOI: 10.1101/2024.07.30.24311094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/13/2024]
Abstract
Background Regional myocardial work (MW) is not measured in the right ventricle (RV) due to a lack of high spatial resolution regional strain (RS) estimates throughout the ventricle. We present a cineCT-based approach to evaluate regional RV performance and demonstrate its ability to phenotype three complex populations: end-stage LV failure (HF), chronic thromboembolic pulmonary hypertension (CTEPH), and repaired tetralogy of Fallot (rTOF). Methods 49 patients (19 HF, 11 CTEPH, 19 rTOF) underwent cineCT and right heart catheterization (RHC). RS was estimated from full-cycle ECG-gated cineCT and combined with RHC pressure waveforms to create regional pressure-strain loops; endocardial MW was measured as the loop area. Detailed, 3D mapping of RS and MW enabled spatial visualization of strain and work strength, and phenotyping of patients. Results HF patients demonstrated more overall impaired strain and work compared to the CTEPH and rTOF cohorts. For example, the HF patients had more akinetic areas (median: 9%) than CTEPH (median: <1%, p=0.02) and rTOF (median: 1%, p<0.01) and performed more low work (median: 69%) than the rTOF cohort (median: 38%, p<0.01). The CTEPH cohort had more impairment in the septal wall; <1% of the free wall and 16% of the septal wall performed negative work. The rTOF cohort demonstrated a wide distribution of strain and work, ranging from hypokinetic to hyperkinetic strain and low to medium-high work. Impaired strain (-0.15≤RS) and negative work were strongly-to-very strongly correlated with RVEF (R=-0.89, p<0.01; R=-0.70, p<0.01 respectively), while impaired work (MW≤5 mmHg) was moderately correlated with RVEF (R=-0.53, p<0.01). Conclusions Regional RV MW maps can be derived from clinical CT and RHC studies and can provide patient-specific phenotyping of RV function in complex heart disease patients. Clinical Perspective Evaluating regional variations in right ventricular (RV) performance can be challenging, particularly in patients with significant impairments due to the need for 3D spatial coverage with high spatial resolution. ECG-gated cineCT can fully visualize the RV and be used to quantify regional strain with high spatial resolution. However, strain is influenced by loading conditions. Myocardial work (MW) - measured clinically derived as the ventricular pressure-strain loop area - is considered a more comprehensive metric due to its independence of preload and afterload. In this study, we sought to develop regional RV myocardial work (MW) assessments in 3D with high spatial resolution by combining cineCT-derived regional strain with RV pressure waveforms from right heart catheterization (RHC). We developed our method using data from three clinical cohorts who routinely undergo cineCT and RHC: patients in heart failure, patients with chronic thromboembolic pulmonary hypertension, and adults with repaired tetralogy of Fallot.We demonstrate that regional strain and work provide different perspectives on RV performance. While strain can be used to evaluate apparent function, similar profiles of RV strain can lead to different MW estimates. Specifically, MW integrates apparent strain with measures of afterload, and timing information helps to account for dyssynchrony. As a result, CT-based assessment of RV MW appears to be a useful new metric for the care of patients with dysfunction.
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Affiliation(s)
- Amanda Craine
- Department of Bioengineering, University of California San Diego, 9500 Gilman Drive, La Jolla, CA USA
| | - Anderson Scott
- Department of Bioengineering, University of California San Diego, 9500 Gilman Drive, La Jolla, CA USA
| | - Dhruvi Desai
- Department of Bioengineering, University of California San Diego, 9500 Gilman Drive, La Jolla, CA USA
| | - Seth Kligerman
- Department of Radiology, National Jewish Health, 1400 Jackson Street, Denver, CO USA
| | - Eric Adler
- Division of Cardiovascular Medicine, University of California San Diego, 9500 Gilman Drive, La Jolla, CA USA
| | - Nick H Kim
- Division of Pulmonary, Critical Care, Sleep Medicine & Physiology, Department of Medicine, University of California San Diego, 9500 Gilman Drive, La Jolla, CA USA
| | - Laith Alshawabkeh
- Division of Cardiovascular Medicine, University of California San Diego, 9500 Gilman Drive, La Jolla, CA USA
| | - Francisco Contijoch
- Department of Bioengineering, University of California San Diego, 9500 Gilman Drive, La Jolla, CA USA
- Department of Radiology, University of California San Diego, 9500 Gilman Drive, La Jolla, CA USA
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Zhao Z, Zhang Y, Su J, Yang L, Pang L, Gao Y, Wang H. A comprehensive review for artificial intelligence on neuroimaging in rehabilitation of ischemic stroke. Front Neurol 2024; 15:1367854. [PMID: 38606275 PMCID: PMC11007047 DOI: 10.3389/fneur.2024.1367854] [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: 01/09/2024] [Accepted: 03/08/2024] [Indexed: 04/13/2024] Open
Abstract
Stroke is the second leading cause of death worldwide, with ischemic stroke accounting for a significant proportion of morbidity and mortality among stroke patients. Ischemic stroke often causes disability and cognitive impairment in patients, which seriously affects the quality of life of patients. Therefore, how to predict the recovery of patients can provide support for clinical intervention in advance and improve the enthusiasm of patients for rehabilitation treatment. With the popularization of imaging technology, the diagnosis and treatment of ischemic stroke patients are often accompanied by a large number of imaging data. Through machine learning and Deep Learning, information from imaging data can be used more effectively. In this review, we discuss recent advances in neuroimaging, machine learning, and Deep Learning in the rehabilitation of ischemic stroke.
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Affiliation(s)
- Zijian Zhao
- Rehabilitation Center, ShengJing Hospital of China Medical University, Shenyang, Liaoning Province, China
| | - Yuanyuan Zhang
- Rehabilitation Center, ShengJing Hospital of China Medical University, Shenyang, Liaoning Province, China
| | - Jiuhui Su
- Department of Orthopedics, Haicheng Bonesetting Hospital, Haicheng, Liaoning Province, China
| | - Lianbo Yang
- Department of Reparative and Reconstructive Surgery, The Second Hospital of Dalian Medical University, Dalian Liaoning Province, China
| | - Luhang Pang
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning Province, China
| | - Yingshan Gao
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning Province, China
| | - Hongbo Wang
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning Province, China
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Li M, Guo X, Verma A, Rudkouskaya A, McKenna AM, Intes X, Wang G, Barroso M. Contrast-enhanced photon-counting micro-CT of tumor xenograft models. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.03.574097. [PMID: 38260707 PMCID: PMC10802390 DOI: 10.1101/2024.01.03.574097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Photon-counting micro computed tomography (micro-CT) offers new potential in preclinical imaging, particularly in distinguishing materials. It becomes especially helpful when combined with contrast agents, enabling the differentiation of tumors from surrounding tissues. There are mainly two types of contrast agents in the market for micro-CT: small molecule-based and nanoparticle-based. However, despite their widespread use in liver tumor studies, there is a notable gap in research on the application of these commercially available agents for photon-counting micro-CT in breast and ovarian tumors. Herein, we explored the effectiveness of these agents in differentiating tumor xenografts from various origins (AU565, MDA-MB-231, and SKOV-3) in nude mice, using photon-counting micro-CT. Specifically, ISOVUE-370 (a small molecule-based agent) and Exitrone Nano 12000 (a nanoparticle-based agent) were investigated in this context. To improve tumor visualization, we proposed a novel color visualization method for photon-counting micro-CT, which changes color tones to highlight contrast media distribution, offering a robust alternative to traditional material decomposition methods with less computational demand. Our in vivo experiments confirm its effectiveness, showing distinct enhancement characteristics for each contrast agent. Qualitative and quantitative analyses suggested that Exitrone Nano 12000 provides superior vasculature enhancement and better quantitative consistency across scans, while ISOVUE-370 gives more comprehensive tumor enhancement but with a significant variance between scans due to its short blood half-time. This variability leads to high sensitivity to timing and individual differences among mice. Further, a paired t-test on mean and standard deviation values within tumor volumes showed significant differences between the AU565 and SKOV-3 tumor models with the nanoparticle-based (p-values < 0.02), attributable to their distinct vascularity, as confirmed by immunohistochemistry. These findings underscore the utility of photon-counting micro-CT in non-invasively assessing the morphology and anatomy of different tumor xenografts, which is crucial for tumor characterization and longitudinal monitoring of tumor development and response to treatments.
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Affiliation(s)
- Mengzhou Li
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
| | - Xiaodong Guo
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
| | - Amit Verma
- Department of Molecular and Cellular Physiology, Albany Medical College, Albany, NY 12208, USA
| | - Alena Rudkouskaya
- Department of Molecular and Cellular Physiology, Albany Medical College, Albany, NY 12208, USA
| | - Antigone M. McKenna
- Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
| | - Xavier Intes
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
| | - Ge Wang
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
| | - Margarida Barroso
- Department of Molecular and Cellular Physiology, Albany Medical College, Albany, NY 12208, USA
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Chen M, Qian D, Wang Y, An J, Meng K, Xu S, Liu S, Sun M, Li M, Pang C. Systematic Review of Machine Learning Applied to the Secondary Prevention of Ischemic Stroke. J Med Syst 2024; 48:8. [PMID: 38165495 DOI: 10.1007/s10916-023-02020-4] [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: 05/26/2023] [Accepted: 11/13/2023] [Indexed: 01/03/2024]
Abstract
Ischemic stroke is a serious disease posing significant threats to human health and life, with the highest absolute and relative risks of a poor prognosis following the first occurrence, and more than 90% of strokes are attributable to modifiable risk factors. Currently, machine learning (ML) is widely used for the prediction of ischemic stroke outcomes. By identifying risk factors, predicting the risk of poor prognosis and thus developing personalized treatment plans, it effectively reduces the probability of poor prognosis, leading to more effective secondary prevention. This review includes 41 studies since 2018 that used ML algorithms to build prognostic prediction models for ischemic stroke, transient ischemic attack (TIA), and acute ischemic stroke (AIS). We analyzed in detail the risk factors used in these studies, the sources and processing methods of the required data, the model building and validation, and their application in different prediction time windows. The results indicate that among the included studies, the top five risk factors in terms of frequency were cardiovascular diseases, age, sex, national institutes of health stroke scale (NIHSS) score, and diabetes. Furthermore, 64% of the studies used single-center data, 65% of studies using imbalanced data did not perform data balancing, 88% of the studies did not utilize external validation datasets for model validation, and 72% of the studies did not provide explanations for their models. Addressing these issues is crucial for enhancing the credibility and effectiveness of the research, consequently improving the development and implementation of secondary prevention measures.
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Affiliation(s)
- Meng Chen
- School of Life Science and Technology, Changchun University of Science and Technology, Jilin Province, Changchun, 130022, People's Republic of China
| | - Dongbao Qian
- School of Life Science and Technology, Changchun University of Science and Technology, Jilin Province, Changchun, 130022, People's Republic of China
| | - Yixuan Wang
- Union Hospital of Jilin University, Jilin Province, Neurosurgery, Changchun, 130033, People's Republic of China
| | - Junyan An
- Union Hospital of Jilin University, Jilin Province, Neurosurgery, Changchun, 130033, People's Republic of China
| | - Ke Meng
- Union Hospital of Jilin University, Jilin Province, Neurosurgery, Changchun, 130033, People's Republic of China
| | - Shuai Xu
- School of Life Science and Technology, Changchun University of Science and Technology, Jilin Province, Changchun, 130022, People's Republic of China
| | - Sheng Liu
- School of Life Science and Technology, Changchun University of Science and Technology, Jilin Province, Changchun, 130022, People's Republic of China
| | - Meiyan Sun
- Union Hospital of Jilin University, Jilin Province, Neurosurgery, Changchun, 130033, People's Republic of China
| | - Miao Li
- School of Life Science and Technology, Changchun University of Science and Technology, Jilin Province, Changchun, 130022, People's Republic of China.
- Union Hospital of Jilin University, Jilin Province, Neurosurgery, Changchun, 130033, People's Republic of China.
| | - Chunying Pang
- School of Life Science and Technology, Changchun University of Science and Technology, Jilin Province, Changchun, 130022, People's Republic of China.
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Akay EMZ, Hilbert A, Carlisle BG, Madai VI, Mutke MA, Frey D. Artificial Intelligence for Clinical Decision Support in Acute Ischemic Stroke: A Systematic Review. Stroke 2023; 54:1505-1516. [PMID: 37216446 DOI: 10.1161/strokeaha.122.041442] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 02/21/2023] [Indexed: 05/24/2023]
Abstract
BACKGROUND Established randomized trial-based parameters for acute ischemic stroke group patients into generic treatment groups, leading to attempts using various artificial intelligence (AI) methods to directly correlate patient characteristics to outcomes and thereby provide decision support to stroke clinicians. We review AI-based clinical decision support systems in the development stage, specifically regarding methodological robustness and constraints for clinical implementation. METHODS Our systematic review included full-text English language publications proposing a clinical decision support system using AI techniques for direct decision support in acute ischemic stroke cases in adult patients. We (1) describe data and outcomes used in those systems, (2) estimate the systems' benefits compared with traditional stroke diagnosis and treatment, and (3) reported concordance with reporting standards for AI in healthcare. RESULTS One hundred twenty-one studies met our inclusion criteria. Sixty-five were included for full extraction. In our sample, utilized data sources, methods, and reporting practices were highly heterogeneous. CONCLUSIONS Our results suggest significant validity threats, dissonance in reporting practices, and challenges to clinical translation. We outline practical recommendations for the successful implementation of AI research in acute ischemic stroke treatment and diagnosis.
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Affiliation(s)
- Ela Marie Z Akay
- Charité Lab for Artificial Intelligence in Medicine (CLAIM) (E.M.Z.A., A.H., D.F.), Charité Universitätsmedizin Berlin, Germany
| | - Adam Hilbert
- Charité Lab for Artificial Intelligence in Medicine (CLAIM) (E.M.Z.A., A.H., D.F.), Charité Universitätsmedizin Berlin, Germany
| | - Benjamin G Carlisle
- QUEST Center for Responsible Research, Berlin Institute of Health (BIH) (B.G.C., V.I.M.), Charité Universitätsmedizin Berlin, Germany
| | - Vince I Madai
- QUEST Center for Responsible Research, Berlin Institute of Health (BIH) (B.G.C., V.I.M.), Charité Universitätsmedizin Berlin, Germany
- Faculty of Computing, Engineering and the Built Environment, School of Computing and Digital Technology, Birmingham City University, United Kingdom (V.I.M.)
| | - Matthias A Mutke
- Department of Neuroradiology, Heidelberg University Hospital, Germany (M.A.M.)
| | - Dietmar Frey
- Charité Lab for Artificial Intelligence in Medicine (CLAIM) (E.M.Z.A., A.H., D.F.), Charité Universitätsmedizin Berlin, Germany
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Winder AJ, Wilms M, Amador K, Flottmann F, Fiehler J, Forkert ND. Predicting the tissue outcome of acute ischemic stroke from acute 4D computed tomography perfusion imaging using temporal features and deep learning. Front Neurosci 2022; 16:1009654. [PMID: 36408399 PMCID: PMC9672821 DOI: 10.3389/fnins.2022.1009654] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 10/12/2022] [Indexed: 12/27/2023] Open
Abstract
Predicting follow-up lesions from baseline CT perfusion (CTP) datasets in acute ischemic stroke patients is important for clinical decision making. Deep convolutional networks (DCNs) are assumed to be the current state-of-the-art for this task. However, many DCN classifiers have not been validated against the methods currently used in research (random decision forests, RDF) and clinical routine (Tmax thresholding). Specialized DCNs have even been designed to extract complex temporal features directly from spatiotemporal CTP data instead of using standard perfusion parameter maps. However, the benefits of applying deep learning to source or deconvolved CTP data compared to perfusion parameter maps have not been formally investigated so far. In this work, a modular UNet-based DCN is proposed that separates temporal feature extraction from tissue outcome prediction, allowing for both model validation using perfusion parameter maps as well as end-to-end learning from spatiotemporal CTP data. 145 retrospective datasets comprising baseline CTP imaging, perfusion parameter maps, and follow-up non-contrast CT with manual lesion segmentations were assembled from acute ischemic stroke patients treated with intravenous thrombolysis alone (IV; n = 43) or intra-arterial mechanical thrombectomy (IA; n = 102) with or without combined IV. Using the perfusion parameter maps as input, the proposed DCN (mean Dice: 0.287) outperformed the RDF (0.262) and simple Tmax-thresholding (0.249). The performance of the proposed DCN was approximately equal using features optimized from the deconvolved residual curves (0.286) compared to perfusion parameter maps (0.287), while using features optimized from the source concentration-time curves (0.296) provided the best tissue outcome predictions.
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Affiliation(s)
- Anthony J. Winder
- Department of Radiology, University of Calgary, Calgary, AB, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Matthias Wilms
- Department of Paediatrics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, AB, Canada
| | - Kimberly Amador
- Department of Radiology, University of Calgary, Calgary, AB, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Fabian Flottmann
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Jens Fiehler
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Nils D. Forkert
- Department of Radiology, University of Calgary, Calgary, AB, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, AB, Canada
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada
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Sui H, Wu J, Zhou Q, Liu L, Lv Z, Zhang X, Yang H, Shen Y, Liao S, Shi F, Mo Z. Nomograms predict prognosis and hospitalization time using non-contrast CT and CT perfusion in patients with ischemic stroke. Front Neurosci 2022; 16:912287. [PMID: 35937898 PMCID: PMC9355636 DOI: 10.3389/fnins.2022.912287] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Accepted: 06/24/2022] [Indexed: 12/12/2022] Open
Abstract
Background Stroke is a major disease with high morbidity and mortality worldwide. Currently, there is no quantitative method to evaluate the short-term prognosis and length of hospitalization of patients. Purpose We aimed to develop nomograms as prognosis predictors based on imaging characteristics from non-contrast computed tomography (NCCT) and CT perfusion (CTP) and clinical characteristics for predicting activity of daily living (ADL) and hospitalization time of patients with ischemic stroke. Materials and methods A total of 476 patients were enrolled in the study and divided into the training set (n = 381) and testing set (n = 95). Each of them owned NCCT and CTP images. We propose to extract imaging features representing as the Alberta stroke program early CT score (ASPECTS) values from NCCT, ischemic lesion volumes from CBF, and TMAX maps from CTP. Based on imaging features and clinical characteristics, we addressed two main issues: (1) predicting prognosis according to the Barthel index (BI)–binary logistic regression analysis was employed for feature selection, and the resulting nomogram was assessed in terms of discrimination capability, calibration, and clinical utility and (2) predicting the hospitalization time of patients–the Cox proportional hazard model was used for this purpose. After feature selection, another specific nomogram was established with calibration curves and time-dependent ROC curves for evaluation. Results In the task of predicting binary prognosis outcome, a nomogram was constructed with the area under the curve (AUC) value of 0.883 (95% CI: 0.781–0.985), the accuracy of 0.853, and F1-scores of 0.909 in the testing set. We further tried to predict discharge BI into four classes. Similar performance was achieved as an AUC of 0.890 in the testing set. In the task of predicting hospitalization time, the Cox proportional hazard model was used. The concordance index of the model was 0.700 (SE = 0.019), and AUCs for predicting discharge at a specific week were higher than 0.80, which demonstrated the superior performance of the model. Conclusion The novel non-invasive NCCT- and CTP-based nomograms could predict short-term ADL and hospitalization time of patients with ischemic stroke, thus allowing a personalized clinical outcome prediction and showing great potential in improving clinical efficiency. Summary Combining NCCT- and CTP-based nomograms could accurately predict short-term outcomes of patients with ischemic stroke, including whose discharge BI and the length of hospital stay. Key Results Using a large dataset of 1,310 patients, we show a novel nomogram with a good performance in predicting discharge BI class of patients (AUCs > 0.850). The second nomogram owns an excellent ability to predict the length of hospital stay (AUCs > 0.800).
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Affiliation(s)
- He Sui
- Department of Radiology, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Jiaojiao Wu
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Qing Zhou
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Lin Liu
- Department of Radiology, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Zhongwen Lv
- Department of Radiology, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Xintan Zhang
- Department of Radiology, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Haibo Yang
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Yi Shen
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Shu Liao
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Feng Shi
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Zhanhao Mo
- Department of Radiology, China-Japan Union Hospital of Jilin University, Changchun, China
- *Correspondence: Zhanhao Mo,
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8
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Wang X, Fan Y, Zhang N, Li J, Duan Y, Yang B. Performance of Machine Learning for Tissue Outcome Prediction in Acute Ischemic Stroke: A Systematic Review and Meta-Analysis. Front Neurol 2022; 13:910259. [PMID: 35873778 PMCID: PMC9305175 DOI: 10.3389/fneur.2022.910259] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Accepted: 06/20/2022] [Indexed: 12/03/2022] Open
Abstract
Machine learning (ML) has been proposed for lesion segmentation in acute ischemic stroke (AIS). This study aimed to provide a systematic review and meta-analysis of the overall performance of current ML algorithms for final infarct prediction from baseline imaging. We made a comprehensive literature search on eligible studies developing ML models for core infarcted tissue estimation on admission CT or MRI in AIS patients. Eleven studies meeting the inclusion criteria were included in the quantitative analysis. Study characteristics, model methodology, and predictive performance of the included studies were extracted. A meta-analysis was conducted on the dice similarity coefficient (DSC) score by using a random-effects model to assess the overall predictive performance. Study heterogeneity was assessed by Cochrane Q and Higgins I2 tests. The pooled DSC score of the included ML models was 0.50 (95% CI 0.39–0.61), with high heterogeneity observed across studies (I2 96.5%, p < 0.001). Sensitivity analyses using the one-study removed method showed the adjusted overall DSC score ranged from 0.47 to 0.52. Subgroup analyses indicated that the DL-based models outperformed the conventional ML classifiers with the best performance observed in DL algorithms combined with CT data. Despite the presence of heterogeneity, current ML-based approaches for final infarct prediction showed moderate but promising performance. Before well integrated into clinical stroke workflow, future investigations are suggested to train ML models on large-scale, multi-vendor data, validate on external cohorts and adopt formalized reporting standards for improving model accuracy and robustness.
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Affiliation(s)
- Xinrui Wang
- Department of Radiology, General Hospital of Northern Theater Command, Shenyang, China
| | - Yiming Fan
- Department of Orthopedics, Chinese PLA General Hospital, Beijing, China
| | - Nan Zhang
- Department of Radiology, General Hospital of Northern Theater Command, Shenyang, China
| | - Jing Li
- Department of Radiology, Changhai Hospital, Shanghai, China
| | - Yang Duan
- Department of Radiology, General Hospital of Northern Theater Command, Shenyang, China
| | - Benqiang Yang
- Department of Radiology, General Hospital of Northern Theater Command, Shenyang, China
- *Correspondence: Benqiang Yang
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Mäkelä T, Öman O, Hokkinen L, Wilppu U, Salli E, Savolainen S, Kangasniemi M. Automatic CT Angiography Lesion Segmentation Compared to CT Perfusion in Ischemic Stroke Detection: a Feasibility Study. J Digit Imaging 2022; 35:551-563. [PMID: 35211838 PMCID: PMC9156593 DOI: 10.1007/s10278-022-00611-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 12/30/2021] [Accepted: 01/12/2022] [Indexed: 12/15/2022] Open
Abstract
In stroke imaging, CT angiography (CTA) is used for detecting arterial occlusions. These images could also provide information on the extent of ischemia. The study aim was to develop and evaluate a convolutional neural network (CNN)-based algorithm for detecting and segmenting acute ischemic lesions from CTA images of patients with suspected middle cerebral artery stroke. These results were compared to volumes reported by widely used CT perfusion-based RAPID software (IschemaView). A 42-layer-deep CNN was trained on 50 CTA volumes with manually delineated targets. The lower bound for predicted lesion size to reliably discern stroke from false positives was estimated. The severity of false positives and false negatives was reviewed visually to assess the clinical applicability and to further guide the method development. The CNN model corresponded to the manual segmentations with voxel-wise sensitivity 0.54 (95% confidence interval: 0.44-0.63), precision 0.69 (0.60-0.76), and Sørensen-Dice coefficient 0.61 (0.52-0.67). Stroke/nonstroke differentiation accuracy 0.88 (0.81-0.94) was achieved when only considering the predicted lesion size (i.e., regardless of location). By visual estimation, 46% of cases showed some false findings, such as CNN highlighting chronic periventricular white matter changes or beam hardening artifacts, but only in 9% the errors were severe, translating to 0.91 accuracy. The CNN model had a moderately strong correlation to RAPID-reported Tmax > 10 s volumes (Pearson's r = 0.76 (0.58-0.86)). The results suggest that detecting anterior circulation ischemic strokes from CTA using a CNN-based algorithm can be feasible when accompanied with physiological knowledge to rule out false positives.
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Affiliation(s)
- Teemu Mäkelä
- grid.7737.40000 0004 0410 2071HUS Medical Imaging Center, Radiology, University of Helsinki and Helsinki University Hospital, Haartmaninkatu 4, P.O. Box 340, 00290 Helsinki, Finland ,grid.7737.40000 0004 0410 2071Department of Physics, University of Helsinki, P.O. Box 64, 00014 Helsinki, Finland
| | - Olli Öman
- grid.7737.40000 0004 0410 2071HUS Medical Imaging Center, Radiology, University of Helsinki and Helsinki University Hospital, Haartmaninkatu 4, P.O. Box 340, 00290 Helsinki, Finland
| | - Lasse Hokkinen
- grid.7737.40000 0004 0410 2071HUS Medical Imaging Center, Radiology, University of Helsinki and Helsinki University Hospital, Haartmaninkatu 4, P.O. Box 340, 00290 Helsinki, Finland
| | - Ulla Wilppu
- grid.7737.40000 0004 0410 2071HUS Medical Imaging Center, Radiology, University of Helsinki and Helsinki University Hospital, Haartmaninkatu 4, P.O. Box 340, 00290 Helsinki, Finland
| | - Eero Salli
- grid.7737.40000 0004 0410 2071HUS Medical Imaging Center, Radiology, University of Helsinki and Helsinki University Hospital, Haartmaninkatu 4, P.O. Box 340, 00290 Helsinki, Finland
| | - Sauli Savolainen
- grid.7737.40000 0004 0410 2071HUS Medical Imaging Center, Radiology, University of Helsinki and Helsinki University Hospital, Haartmaninkatu 4, P.O. Box 340, 00290 Helsinki, Finland ,grid.7737.40000 0004 0410 2071Department of Physics, University of Helsinki, P.O. Box 64, 00014 Helsinki, Finland
| | - Marko Kangasniemi
- grid.7737.40000 0004 0410 2071HUS Medical Imaging Center, Radiology, University of Helsinki and Helsinki University Hospital, Haartmaninkatu 4, P.O. Box 340, 00290 Helsinki, Finland
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10
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Zhu G, Chen H, Jiang B, Chen F, Xie Y, Wintermark M. Application of Deep Learning to Ischemic and Hemorrhagic Stroke Computed Tomography and Magnetic Resonance Imaging. Semin Ultrasound CT MR 2022; 43:147-152. [PMID: 35339255 DOI: 10.1053/j.sult.2022.02.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Deep Learning (DL) algorithm holds great potential in the field of stroke imaging. It has been applied not only to the "downstream" side such as lesion detection, treatment decision making, and outcome prediction, but also to the "upstream" side for generation and enhancement of stroke imaging. This paper aims to comprehensively overview the common applications of DL to stroke imaging. In the future, more standardized imaging datasets and more extensive studies are needed to establish and validate the role of DL in stroke imaging.
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Affiliation(s)
- Guangming Zhu
- Department of Radiology, Neuroradiology Section, Stanford University School of Medicine, Stanford, CA
| | - Hui Chen
- Department of Radiology, Neuroradiology Section, Stanford University School of Medicine, Stanford, CA
| | - Bin Jiang
- Department of Radiology, Neuroradiology Section, Stanford University School of Medicine, Stanford, CA
| | - Fei Chen
- Department of Neurology, Xuan Wu hospital, Capital Meidcal University, Beijing, China
| | - Yuan Xie
- Subtle Medical Inc, Menlo Park, CA
| | - Max Wintermark
- Department of Radiology, Neuroradiology Section, Stanford University School of Medicine, Stanford, CA.
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11
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Benzakoun J, Charron S, Turc G, Hassen WB, Legrand L, Boulouis G, Naggara O, Baron JC, Thirion B, Oppenheim C. Tissue outcome prediction in hyperacute ischemic stroke: Comparison of machine learning models. J Cereb Blood Flow Metab 2021; 41:3085-3096. [PMID: 34159824 PMCID: PMC8756479 DOI: 10.1177/0271678x211024371] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Machine Learning (ML) has been proposed for tissue fate prediction after acute ischemic stroke (AIS), with the aim to help treatment decision and patient management. We compared three different ML models to the clinical method based on diffusion-perfusion thresholding for the voxel-based prediction of final infarct, using a large MRI dataset obtained in a cohort of AIS patients prior to recanalization treatment. Baseline MRI (MRI0), including diffusion-weighted sequence (DWI) and Tmax maps from perfusion-weighted sequence, and 24-hr follow-up MRI (MRI24h) were retrospectively collected in consecutive 394 patients AIS patients (median age = 70 years; final infarct volume = 28mL). Manually segmented DWI24h lesion was considered the final infarct. Gradient Boosting, Random Forests and U-Net were trained using DWI, apparent diffusion coefficient (ADC) and Tmax maps on MRI0 as inputs to predict final infarct. Tissue outcome predictions were compared to final infarct using Dice score. Gradient Boosting had significantly better predictive performance (median [IQR] Dice Score as for median age, maybe you can replace the comma with an equal sign for consistency 0.53 [0.29-0.68]) than U-Net (0.48 [0.18-0.68]), Random Forests (0.51 [0.27-0.66]), and clinical thresholding method (0.45 [0.25-0.62]) (P < 0.001). In this benchmark of ML models for tissue outcome prediction in AIS, Gradient Boosting outperformed other ML models and clinical thresholding method and is thus promising for future decision-making.
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Affiliation(s)
- Joseph Benzakoun
- Institut de Psychiatrie et Neurosciences de Paris (IPNP), INSERM U1266, Paris, France.,Department of Neuroradiology, GHU Paris Psychiatrie et Neurosciences, FHU Neurovasc, Paris, France.,Faculté de médecine, Université de Paris, Paris, France
| | - Sylvain Charron
- Institut de Psychiatrie et Neurosciences de Paris (IPNP), INSERM U1266, Paris, France.,Faculté de médecine, Université de Paris, Paris, France
| | - Guillaume Turc
- Institut de Psychiatrie et Neurosciences de Paris (IPNP), INSERM U1266, Paris, France.,Faculté de médecine, Université de Paris, Paris, France.,Department of Neurology, GHU Paris Psychiatrie et Neurosciences, FHU Neurovasc, Paris, France
| | - Wagih Ben Hassen
- Institut de Psychiatrie et Neurosciences de Paris (IPNP), INSERM U1266, Paris, France.,Department of Neuroradiology, GHU Paris Psychiatrie et Neurosciences, FHU Neurovasc, Paris, France
| | - Laurence Legrand
- Institut de Psychiatrie et Neurosciences de Paris (IPNP), INSERM U1266, Paris, France.,Department of Neuroradiology, GHU Paris Psychiatrie et Neurosciences, FHU Neurovasc, Paris, France
| | - Grégoire Boulouis
- Institut de Psychiatrie et Neurosciences de Paris (IPNP), INSERM U1266, Paris, France.,Department of Neuroradiology, GHU Paris Psychiatrie et Neurosciences, FHU Neurovasc, Paris, France.,Faculté de médecine, Université de Paris, Paris, France
| | - Olivier Naggara
- Institut de Psychiatrie et Neurosciences de Paris (IPNP), INSERM U1266, Paris, France.,Department of Neuroradiology, GHU Paris Psychiatrie et Neurosciences, FHU Neurovasc, Paris, France.,Faculté de médecine, Université de Paris, Paris, France
| | - Jean-Claude Baron
- Institut de Psychiatrie et Neurosciences de Paris (IPNP), INSERM U1266, Paris, France.,Faculté de médecine, Université de Paris, Paris, France.,Department of Neurology, GHU Paris Psychiatrie et Neurosciences, FHU Neurovasc, Paris, France
| | | | - Catherine Oppenheim
- Institut de Psychiatrie et Neurosciences de Paris (IPNP), INSERM U1266, Paris, France.,Department of Neuroradiology, GHU Paris Psychiatrie et Neurosciences, FHU Neurovasc, Paris, France.,Faculté de médecine, Université de Paris, Paris, France
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12
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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”.
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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
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13
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Velagapudi L, Mouchtouris N, Baldassari MP, Nauheim D, Khanna O, Saiegh FA, Herial N, Gooch MR, Tjoumakaris S, Rosenwasser RH, Jabbour P. Discrepancies in Stroke Distribution and Dataset Origin in Machine Learning for Stroke. J Stroke Cerebrovasc Dis 2021; 30:105832. [PMID: 33940363 DOI: 10.1016/j.jstrokecerebrovasdis.2021.105832] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Revised: 04/11/2021] [Accepted: 04/11/2021] [Indexed: 01/02/2023] Open
Abstract
BACKGROUND Machine learning algorithms depend on accurate and representative datasets for training in order to become valuable clinical tools that are widely generalizable to a varied population. We aim to conduct a review of machine learning uses in stroke literature to assess the geographic distribution of datasets and patient cohorts used to train these models and compare them to stroke distribution to evaluate for disparities. AIMS 582 studies were identified on initial searching of the PubMed database. Of these studies, 106 full texts were assessed after title and abstract screening which resulted in 489 papers excluded. Of these 106 studies, 79 were excluded due to using cohorts from outside the United States or being review articles or editorials. 27 studies were thus included in this analysis. SUMMARY OF REVIEW Of the 27 studies included, 7 (25.9%) used patient data from California, 6 (22.2%) were multicenter, 3 (11.1%) were in Massachusetts, 2 (7.4%) each in Illinois, Missouri, and New York, and 1 (3.7%) each from South Carolina, Washington, West Virginia, and Wisconsin. 1 (3.7%) study used data from Utah and Texas. These were qualitatively compared to a CDC study showing the highest distribution of stroke in Mississippi (4.3%) followed by Oklahoma (3.4%), Washington D.C. (3.4%), Louisiana (3.3%), and Alabama (3.2%) while the prevalence in California was 2.6%. CONCLUSIONS It is clear that a strong disconnect exists between the datasets and patient cohorts used in training machine learning algorithms in clinical research and the stroke distribution in which clinical tools using these algorithms will be implemented. In order to ensure a lack of bias and increase generalizability and accuracy in future machine learning studies, datasets using a varied patient population that reflects the unequal distribution of stroke risk factors would greatly benefit the usability of these tools and ensure accuracy on a nationwide scale.
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Affiliation(s)
- Lohit Velagapudi
- Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA
| | | | | | - David Nauheim
- Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA
| | - Omaditya Khanna
- Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA
| | - Fadi Al Saiegh
- Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA
| | - Nabeel Herial
- Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA
| | - M Reid Gooch
- Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA
| | | | | | - Pascal Jabbour
- Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA.
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14
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Soun JE, Chow DS, Nagamine M, Takhtawala RS, Filippi CG, Yu W, Chang PD. Artificial Intelligence and Acute Stroke Imaging. AJNR Am J Neuroradiol 2021; 42:2-11. [PMID: 33243898 PMCID: PMC7814792 DOI: 10.3174/ajnr.a6883] [Citation(s) in RCA: 97] [Impact Index Per Article: 32.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Accepted: 07/22/2020] [Indexed: 12/12/2022]
Abstract
Artificial intelligence technology is a rapidly expanding field with many applications in acute stroke imaging, including ischemic and hemorrhage subtypes. Early identification of acute stroke is critical for initiating prompt intervention to reduce morbidity and mortality. Artificial intelligence can help with various aspects of the stroke treatment paradigm, including infarct or hemorrhage detection, segmentation, classification, large vessel occlusion detection, Alberta Stroke Program Early CT Score grading, and prognostication. In particular, emerging artificial intelligence techniques such as convolutional neural networks show promise in performing these imaging-based tasks efficiently and accurately. The purpose of this review is twofold: first, to describe AI methods and available public and commercial platforms in stroke imaging, and second, to summarize the literature of current artificial intelligence-driven applications for acute stroke triage, surveillance, and prediction.
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Affiliation(s)
- J E Soun
- From the Departments of Radiological Sciences (J.E.S., D.S.C., P.D.C.)
| | - D S Chow
- From the Departments of Radiological Sciences (J.E.S., D.S.C., P.D.C.)
- Center for Artificial Intelligence in Diagnostic Medicine (D.S.C., R.S.T., P.D.C.), University of California, Irvine, Orange, California
| | | | - R S Takhtawala
- Center for Artificial Intelligence in Diagnostic Medicine (D.S.C., R.S.T., P.D.C.), University of California, Irvine, Orange, California
| | - C G Filippi
- Department of Radiology (C.G.F.), Northwell Health, Lenox Hill Hospital, New York, New York
| | | | - P D Chang
- From the Departments of Radiological Sciences (J.E.S., D.S.C., P.D.C.)
- Center for Artificial Intelligence in Diagnostic Medicine (D.S.C., R.S.T., P.D.C.), University of California, Irvine, Orange, California
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15
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Karthik R, Menaka R, Johnson A, Anand S. Neuroimaging and deep learning for brain stroke detection - A review of recent advancements and future prospects. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 197:105728. [PMID: 32882591 DOI: 10.1016/j.cmpb.2020.105728] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2020] [Accepted: 08/23/2020] [Indexed: 05/24/2023]
Abstract
BACKGROUND AND OBJECTIVE In recent years, deep learning algorithms have created a massive impact on addressing research challenges in different domains. The medical field also greatly benefits from the use of improving deep learning models which save time and produce accurate results. This research aims to emphasize the impact of deep learning models in brain stroke detection and lesion segmentation. This is achieved by discussing the state of the art approaches proposed by the recent works in this field. METHODS In this study, the advancements in stroke lesion detection and segmentation were focused. The survey analyses 113 research papers published in different academic research databases. The research articles have been filtered out based on specific criteria to obtain the most prominent insights related to stroke lesion detection and segmentation. RESULTS The features of the stroke lesion vary based on the type of imaging modality. To develop an effective method for stroke lesion detection, the features need to be carefully extracted from the input images. This review takes an attempt to categorize and discuss the different deep architectures employed for stroke lesion detection and segmentation, based on the underlying imaging modality. This further assists in understanding the relevance of the two-deep neural network components in medical image analysis namely Convolutional Neural Network (CNN) and Fully Convolutional Network (FCN). It hints at other possible deep architectures that can be proposed for better results towards stroke lesion detection. Also, the emerging trends and breakthroughs in stroke detection have been detailed in this evaluation. CONCLUSION This work concludes by examining the technical and non-technical challenges faced by researchers and indicate the future implications in stroke detection. It could support the bio-medical researchers to propose better solutions for stroke lesion detection.
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Affiliation(s)
- R Karthik
- Center for Cyber Physical Systems, Vellore Institute of Technology, Chennai, India.
| | - R Menaka
- Center for Cyber Physical Systems, Vellore Institute of Technology, Chennai, India.
| | - Annie Johnson
- School of Electronics Engineering, Vellore Institute of Technology, Chennai, India
| | - Sundar Anand
- School of Electronics Engineering, Vellore Institute of Technology, Chennai, India
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16
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Lui YW, Chang PD, Zaharchuk G, Barboriak DP, Flanders AE, Wintermark M, Hess CP, Filippi CG. Artificial Intelligence in Neuroradiology: Current Status and Future Directions. AJNR Am J Neuroradiol 2020; 41:E52-E59. [PMID: 32732276 PMCID: PMC7658873 DOI: 10.3174/ajnr.a6681] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Fueled by new techniques, computational tools, and broader availability of imaging data, artificial intelligence has the potential to transform the practice of neuroradiology. The recent exponential increase in publications related to artificial intelligence and the central focus on artificial intelligence at recent professional and scientific radiology meetings underscores the importance. There is growing momentum behind leveraging artificial intelligence techniques to improve workflow and diagnosis and treatment and to enhance the value of quantitative imaging techniques. This article explores the reasons why neuroradiologists should care about the investments in new artificial intelligence applications, highlights current activities and the roles neuroradiologists are playing, and renders a few predictions regarding the near future of artificial intelligence in neuroradiology.
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Affiliation(s)
- Y W Lui
- From the Department of Radiology (Y.W.L.), New York University Langone Medical Center, New York, New York
| | - P D Chang
- Department of Radiology (P.D.C.), University of California Irvine Health Medical Center, Orange, California
| | - G Zaharchuk
- Department of Neuroradiology (G.Z., M.W.), Stanford University, Stanford, California
| | - D P Barboriak
- Department of Radiology (D.P.B.), Duke University Medical Center, Durham, North Carolina
| | - A E Flanders
- Department of Radiology (A.E.F.), Thomas Jefferson University Hospital, Philadelphia, Pennsylvania
| | - M Wintermark
- Department of Neuroradiology (G.Z., M.W.), Stanford University, Stanford, California
| | - C P Hess
- Department of Radiology and Biomedical Imaging (C.P.H.), University of California, San Francisco, San Francisco, California
| | - C G Filippi
- Department of Radiology (C.G.F.), Northwell Health, New York, New York.
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