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Li H, Wang J, Li Z, Cecil KM, Altaye M, Dillman JR, Parikh NA, He L. Supervised contrastive learning enhances graph convolutional networks for predicting neurodevelopmental deficits in very preterm infants using brain structural connectome. Neuroimage 2024; 291:120579. [PMID: 38537766 PMCID: PMC11059107 DOI: 10.1016/j.neuroimage.2024.120579] [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: 11/28/2023] [Revised: 02/15/2024] [Accepted: 03/15/2024] [Indexed: 04/13/2024] Open
Abstract
Very preterm (VPT) infants (born at less than 32 weeks gestational age) are at high risk for various adverse neurodevelopmental deficits. Unfortunately, most of these deficits cannot be accurately diagnosed until the age of 2-5 years old. Given the benefits of early interventions, accurate diagnosis and prediction soon after birth are urgently needed for VPT infants. Previous studies have applied deep learning models to learn the brain structural connectome (SC) to predict neurodevelopmental deficits in the preterm population. However, none of these models are specifically designed for graph-structured data, and thus may potentially miss certain topological information conveyed in the brain SC. In this study, we aim to develop deep learning models to learn the SC acquired at term-equivalent age for early prediction of neurodevelopmental deficits at 2 years corrected age in VPT infants. We directly treated the brain SC as a graph, and applied graph convolutional network (GCN) models to capture complex topological information of the SC. In addition, we applied the supervised contrastive learning (SCL) technique to mitigate the effects of the data scarcity problem, and enable robust training of GCN models. We hypothesize that SCL will enhance GCN models for early prediction of neurodevelopmental deficits in VPT infants using the SC. We used a regional prospective cohort of ∼280 VPT infants who underwent MRI examinations at term-equivalent age from the Cincinnati Infant Neurodevelopment Early Prediction Study (CINEPS). These VPT infants completed neurodevelopmental assessment at 2 years corrected age to evaluate cognition, language, and motor skills. Using the SCL technique, the GCN model achieved mean areas under the receiver operating characteristic curve (AUCs) in the range of 0.72∼0.75 for predicting three neurodevelopmental deficits, outperforming several competing models. Our results support our hypothesis that the SCL technique is able to enhance the GCN model in our prediction tasks.
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Affiliation(s)
- Hailong Li
- Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Artificial Intelligence Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Junqi Wang
- Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Zhiyuan Li
- Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Computer Science, University of Cincinnati, Cincinnati, OH, USA
| | - Kim M Cecil
- Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA; Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Mekibib Altaye
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA; Division of Biostatistics and Epidemiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Jonathan R Dillman
- Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Artificial Intelligence Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Nehal A Parikh
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Lili He
- Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Artificial Intelligence Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Computer Science, University of Cincinnati, Cincinnati, OH, USA; Department of Biomedical Engineering, University of Cincinnati, Cincinnati, OH, USA; Department of Biomedical Informatics, University of Cincinnati College of Medicine, Cincinnati, OH, USA; Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA.
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Jang YH, Ham J, Kasani PH, Kim H, Lee JY, Lee GY, Han TH, Kim BN, Lee HJ. Predicting 2-year neurodevelopmental outcomes in preterm infants using multimodal structural brain magnetic resonance imaging with local connectivity. Sci Rep 2024; 14:9331. [PMID: 38653988 DOI: 10.1038/s41598-024-58682-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Accepted: 04/02/2024] [Indexed: 04/25/2024] Open
Abstract
The neurodevelopmental outcomes of preterm infants can be stratified based on the level of prematurity. We explored brain structural networks in extremely preterm (EP; < 28 weeks of gestation) and very-to-late (V-LP; ≥ 28 and < 37 weeks of gestation) preterm infants at term-equivalent age to predict 2-year neurodevelopmental outcomes. Using MRI and diffusion MRI on 62 EP and 131 V-LP infants, we built a multimodal feature set for volumetric and structural network analysis. We employed linear and nonlinear machine learning models to predict the Bayley Scales of Infant and Toddler Development, Third Edition (BSID-III) scores, assessing predictive accuracy and feature importance. Our findings revealed that models incorporating local connectivity features demonstrated high predictive performance for BSID-III subsets in preterm infants. Specifically, for cognitive scores in preterm (variance explained, 17%) and V-LP infants (variance explained, 17%), and for motor scores in EP infants (variance explained, 15%), models with local connectivity features outperformed others. Additionally, a model using only local connectivity features effectively predicted language scores in preterm infants (variance explained, 15%). This study underscores the value of multimodal feature sets, particularly local connectivity, in predicting neurodevelopmental outcomes, highlighting the utility of machine learning in understanding microstructural changes and their implications for early intervention.
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Affiliation(s)
- Yong Hun Jang
- Department of Translational Medicine, Hanyang University Graduate School of Biomedical Science and Engineering, Seoul, Republic of Korea
| | - Jusung Ham
- Department of Communication Sciences and Disorders, University of Iowa, Iowa City, IA, 52242, USA
| | - Payam Hosseinzadeh Kasani
- Department of Pediatrics, Hanyang University Hospital, Hanyang University College of Medicine, 222-1, Wangsimni-ro, Seongdong-gu, Seoul, 04763, Republic of Korea
| | - Hyuna Kim
- Department of Translational Medicine, Hanyang University Graduate School of Biomedical Science and Engineering, Seoul, Republic of Korea
| | - Joo Young Lee
- Department of Translational Medicine, Hanyang University Graduate School of Biomedical Science and Engineering, Seoul, Republic of Korea
| | - Gang Yi Lee
- Department of Translational Medicine, Hanyang University Graduate School of Biomedical Science and Engineering, Seoul, Republic of Korea
| | - Tae Hwan Han
- Division of Neurology, Department of Pediatrics, Hanyang University Hospital, Hanyang University College of Medicine, Seoul, Republic of Korea
| | - Bung-Nyun Kim
- Division of Children and Adolescent Psychiatry, Department of Psychiatry, Seoul National University Hospital, Seoul, Republic of Korea
| | - Hyun Ju Lee
- Department of Pediatrics, Hanyang University Hospital, Hanyang University College of Medicine, 222-1, Wangsimni-ro, Seongdong-gu, Seoul, 04763, Republic of Korea.
- Hanyang Institute of Bioscience and Biotechnology, Hanyang University, Seoul, Republic of Korea.
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Cusworth S, Gkoutos GV, Acharjee A. A novel generative adversarial networks modelling for the class imbalance problem in high dimensional omics data. BMC Med Inform Decis Mak 2024; 24:90. [PMID: 38549123 PMCID: PMC10979623 DOI: 10.1186/s12911-024-02487-2] [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: 09/02/2023] [Accepted: 03/22/2024] [Indexed: 04/01/2024] Open
Abstract
Class imbalance remains a large problem in high-throughput omics analyses, causing bias towards the over-represented class when training machine learning-based classifiers. Oversampling is a common method used to balance classes, allowing for better generalization of the training data. More naive approaches can introduce other biases into the data, being especially sensitive to inaccuracies in the training data, a problem considering the characteristically noisy data obtained in healthcare. This is especially a problem with high-dimensional data. A generative adversarial network-based method is proposed for creating synthetic samples from small, high-dimensional data, to improve upon other more naive generative approaches. The method was compared with 'synthetic minority over-sampling technique' (SMOTE) and 'random oversampling' (RO). Generative methods were validated by training classifiers on the balanced data.
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Affiliation(s)
- Samuel Cusworth
- Institute of Applied Health Research, University of Birmingham, Birmingham, UK
- NIHR Blood and Transplant Research Unit (BTRU) in Precision Transplant and Cellular Therapeutics, University of Birmingham, Birmingham, UK
| | - Georgios V Gkoutos
- College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, University of Birmingham, B15 2TT, Birmingham, UK
- Institute of Translational Medicine, University Hospitals Birmingham NHS Foundation Trust, B15 2TT, Birmingham, UK
- MRC Health Data Research UK (HDR), Midlands Site, UK
- Centre for Health Data Research, University of Birmingham, B15 2TT, Birmingham, UK
- NIHR Experimental Cancer Medicine Centre, B15 2TT, Birmingham, UK
| | - Animesh Acharjee
- College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, University of Birmingham, B15 2TT, Birmingham, UK.
- Institute of Translational Medicine, University Hospitals Birmingham NHS Foundation Trust, B15 2TT, Birmingham, UK.
- MRC Health Data Research UK (HDR), Midlands Site, UK.
- Centre for Health Data Research, University of Birmingham, B15 2TT, Birmingham, UK.
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Kojima K, Kline JE, Altaye M, Kline-Fath BM, Parikh NA. Corpus Callosum Abnormalities at Term-Equivalent Age Are Associated with Language Development at 2 Years' Corrected Age in Infants Born Very Preterm. JOURNAL OF PEDIATRICS. CLINICAL PRACTICE 2024; 11:200101. [PMID: 38827483 PMCID: PMC11138257 DOI: 10.1016/j.jpedcp.2024.200101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/10/2023] [Revised: 12/30/2023] [Accepted: 01/08/2024] [Indexed: 06/04/2024]
Abstract
We studied the effect of microstructural abnormalities in the corpus callosum on language development in 348 infants born very prematurely. We discovered that the fractional anisotropy of the corpus callosum anterior midbody was a significant predictor of standardized language scores at 2 years, independent of clinical and social risk factors.
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Affiliation(s)
- Katsuaki Kojima
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH
| | - Julia E. Kline
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH
| | - Mekibib Altaye
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH
- Division of Biostatistics and Epidemiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH
| | - Beth M. Kline-Fath
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH
- Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH
| | - Nehal A. Parikh
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH
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Xu S, Zhang J, Yue S, Qian J, Zhu D, Dong Y, Liu G, Zhang J. Global trends in neonatal MRI brain neuroimaging research over the last decade: a bibliometric analysis. Quant Imaging Med Surg 2024; 14:1526-1540. [PMID: 38415119 PMCID: PMC10895092 DOI: 10.21037/qims-23-880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 11/20/2023] [Indexed: 02/29/2024]
Abstract
Background Neuroimaging plays a central role in the evaluation, treatment, and prognosis of neonates. In recent years, the exploration of the developing brain has been a major focus of research for researchers and clinicians. In this study, we conducted bibliometric and visualization analyses of the related studies in the field of neonatal magnetic resonance imaging (MRI) brain neuroimaging from the past 10 years, and summarized its research status, hotspots, and frontier development trends. Methods The Web of Science core collection database was used as the literature source from which to retrieve the relevant papers and reviews in the field of neonatal MRI brain neuroimaging published in the Science Citation Index-Expanded from 2013 to 2022. VOSviewer and CiteSpace were used to conduct bibliometric and visualization analyses of the annual publication volume, countries, institutions, journals, authors, co-cited literature, and the overall distribution of keywords. Results We retrieved 3,568 papers and reviews published from 2013 to 2022. The number of publications increased during this period. The United States (US) and the United Kingdom were the largest contributors, with the US receiving the highest H-index and number of citations. The institutions that published the most were the University of London and Harvard University. The research mainly focused on cerebral cortex, brain tissue, brain structure network, artificial intelligence algorithm, automatic image segmentation, and premature infants. Conclusions This study reveals the research status and hotspots of magnetic resonance imaging in the field of neonatal brain neuroimaging in the past decade, which helps researchers to better understand the research status, hotspots, and frontier development trends.
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Affiliation(s)
- Shengfang Xu
- Second Clinical School, Lanzhou University, Lanzhou, China
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, China
- Medical Imaging Center, Gansu Provincial Maternity and Child-Care Hospital, Lanzhou, China
| | - Jinlong Zhang
- Pulmonary and Critical Care Medicine, The 940th Hospital of the Joint Logistic Support Force of the People’s Liberation Army, Lanzhou, China
| | - Songhong Yue
- Second Clinical School, Lanzhou University, Lanzhou, China
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, China
| | - Jifang Qian
- Medical Imaging Center, Gansu Provincial Maternity and Child-Care Hospital, Lanzhou, China
| | - Dalin Zhu
- Medical Imaging Center, Gansu Provincial Maternity and Child-Care Hospital, Lanzhou, China
| | - Yankai Dong
- Department of Cardiovascular Surgery, Guangdong Provincial Hospital of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Guangyao Liu
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, China
| | - Jing Zhang
- Second Clinical School, Lanzhou University, Lanzhou, China
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, China
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Marzi C, Giannelli M, Barucci A, Tessa C, Mascalchi M, Diciotti S. Efficacy of MRI data harmonization in the age of machine learning: a multicenter study across 36 datasets. Sci Data 2024; 11:115. [PMID: 38263181 PMCID: PMC10805868 DOI: 10.1038/s41597-023-02421-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Accepted: 07/27/2023] [Indexed: 01/25/2024] Open
Abstract
Pooling publicly-available MRI data from multiple sites allows to assemble extensive groups of subjects, increase statistical power, and promote data reuse with machine learning techniques. The harmonization of multicenter data is necessary to reduce the confounding effect associated with non-biological sources of variability in the data. However, when applied to the entire dataset before machine learning, the harmonization leads to data leakage, because information outside the training set may affect model building, and potentially falsely overestimate performance. We propose a 1) measurement of the efficacy of data harmonization; 2) harmonizer transformer, i.e., an implementation of the ComBat harmonization allowing its encapsulation among the preprocessing steps of a machine learning pipeline, avoiding data leakage by design. We tested these tools using brain T1-weighted MRI data from 1740 healthy subjects acquired at 36 sites. After harmonization, the site effect was removed or reduced, and we showed the data leakage effect in predicting individual age from MRI data, highlighting that introducing the harmonizer transformer into a machine learning pipeline allows for avoiding data leakage by design.
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Affiliation(s)
- Chiara Marzi
- Department of Statistics, Computer Science and Applications "Giuseppe Parenti", University of Florence, 50134, Florence, Italy
- "Nello Carrara" Institute of Applied Physics (IFAC), National Research Council (CNR), 50019, Sesto Fiorentino, Florence, Italy
| | - Marco Giannelli
- Unit of Medical Physics, Pisa University Hospital "Azienda Ospedaliero-Universitaria Pisana", 56126, Pisa, Italy
| | - Andrea Barucci
- "Nello Carrara" Institute of Applied Physics (IFAC), National Research Council (CNR), 50019, Sesto Fiorentino, Florence, Italy
| | - Carlo Tessa
- Radiology Unit Apuane e Lunigiana, Azienda USL Toscana Nord Ovest, 54100, Massa, Italy
| | - Mario Mascalchi
- Department of Experimental and Clinical Biomedical Sciences "Mario Serio", University of Florence, 50139, Florence, Italy
- Division of Epidemiology and Clinical Governance, Institute for Study, Prevention and netwoRk in Oncology (ISPRO), 50139, Florence, Italy
| | - Stefano Diciotti
- Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi" - DEI, University of Bologna, 47522, Cesena, Italy.
- Alma Mater Research Institute for Human-Centered Artificial Intelligence, University of Bologna, 40121, Bologna, Italy.
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Chen LW, Chu CH, Lin YC, Huang CC. The Quartile Levels of Thyroid-Stimulating Hormone by Newborn Screening Stratified Risks of Neurodevelopmental Impairment in Extremely Preterm Infants - A Population Cohort Study. J Epidemiol 2024:JE20230253. [PMID: 38191177 DOI: 10.2188/jea.je20230253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2024] Open
Abstract
BACKGROUND To evaluate whether thyroid-stimulating hormone (TSH) by newborn screening (NBS) at birth and at discharge can be surrogate markers for neurodevelopmental impairment (NDI) in extremely preterm infants. METHODS The population cohort enrolled infants born <29 weeks' gestation in 2008 - 2020 in southern Taiwan. Infants with a maternal history of thyroid disorders and infants who required thyroxine supplementation during hospitalization were excluded. TSH levels by NBS at birth and at term-equivalent age (TEA)/discharge were respectively categorized into the lowest quartile, the interquartile range, and the highest quartile, which were correlated to NDI outcomes. RESULTS Among 392 patients with paired TSH data, 358 (91%) were prospectively followed until corrected age 24 months. At birth, infants with lowest-quartile TSH had higher NDI risks (OR 2.3, 95% CI 1.3 - 4.1, P = 0.004) compared to infants with interquartile-range TSH. Conversely, by TEA/discharge, infants with highest-quartile TSH had increased NDI (OR 1.9, 1.0 - 3.4, P = 0.03). By paired TSH categories, infants persistently in the lowest TSH quartile (48%, aOR 4.4, 1.4 - 14.5, P = 0.01) and those with a shift from interquartile range to the highest quartile (32%, aOR 2.7, 1.0 - 7.4, P = 0.046) had increased NDI risks compared with the reference with consistent interquartile-range TSH. CONCLUSIONS Extremely preterm infants persistently in the lowest-quartile TSH level at birth and at discharge had the highest NDI risk. TSH quartile levels by NBS may serve as a population surrogate biomarker for assessing NDI risks in infants born extremely preterm.
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Affiliation(s)
- Li-Wen Chen
- Department of Pediatrics, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University
| | | | - Yung-Chieh Lin
- Department of Pediatrics, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University
| | - Chao-Ching Huang
- Department of Pediatrics, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University
- Department of Pediatrics, College of Medicine, Taipei Medical University
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Kojima K, Kline JE, Altaye M, Kline-Fath BM, Parikh NA. Corpus callosum abnormalities at term-equivalent age are associated with language development at two years corrected age in infants born very preterm. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.09.20.23295848. [PMID: 37790343 PMCID: PMC10543245 DOI: 10.1101/2023.09.20.23295848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
We studied the impact of microstructural abnormalities in the corpus callosum on language development in 348 infants born very prematurely. We discovered that the fractional anisotropy of the corpus callosum anterior midbody was a significant predictor of standardized language scores at two years, independent of clinical and social risk factors.
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Affiliation(s)
- Katsuaki Kojima
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH 45229-3039
- Department of Pediatrics, University of Cincinnati College of Medicine, 3230 Eden Avenue, Cincinnati, OH 45267
| | - Julia E Kline
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH 45229-3039
| | - Mekibib Altaye
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH 45229-3039
- Department of Pediatrics, University of Cincinnati College of Medicine, 3230 Eden Avenue, Cincinnati, OH 45267
- Division of Biostatistics and Epidemiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH 45229-3039
| | - Beth M Kline-Fath
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH 45229-3039
- Department of Radiology, University of Cincinnati College of Medicine, 3230 Eden Avenue, Cincinnati, OH 45267
| | - Nehal A Parikh
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH 45229-3039
- Department of Pediatrics, University of Cincinnati College of Medicine, 3230 Eden Avenue, Cincinnati, OH 45267
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Li H, Li Z, Du K, Zhu Y, Parikh NA, He L. A Semi-Supervised Graph Convolutional Network for Early Prediction of Motor Abnormalities in Very Preterm Infants. Diagnostics (Basel) 2023; 13:1508. [PMID: 37189608 PMCID: PMC10137879 DOI: 10.3390/diagnostics13081508] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 04/05/2023] [Accepted: 04/19/2023] [Indexed: 05/17/2023] Open
Abstract
Approximately 32-42% of very preterm infants develop minor motor abnormalities. Earlier diagnosis soon after birth is urgently needed because the first two years of life represent a critical window of opportunity for early neuroplasticity in infants. In this study, we developed a semi-supervised graph convolutional network (GCN) model that is able to simultaneously learn the neuroimaging features of subjects and consider the pairwise similarity between them. The semi-supervised GCN model also allows us to combine labeled data with additional unlabeled data to facilitate model training. We conducted our experiments on a multisite regional cohort of 224 preterm infants (119 labeled subjects and 105 unlabeled subjects) who were born at 32 weeks or earlier from the Cincinnati Infant Neurodevelopment Early Prediction Study. A weighted loss function was applied to mitigate the impact of an imbalanced positive:negative (~1:2) subject ratio in our cohort. With only labeled data, our GCN model achieved an accuracy of 66.4% and an AUC of 0.67 in the early prediction of motor abnormalities, outperforming prior supervised learning models. By taking advantage of additional unlabeled data, the GCN model had significantly better accuracy (68.0%, p = 0.016) and a higher AUC (0.69, p = 0.029). This pilot work suggests that the semi-supervised GCN model can be utilized to aid early prediction of neurodevelopmental deficits in preterm infants.
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Affiliation(s)
- Hailong Li
- Imaging Research Center, Department of Radiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, USA
- Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH 45229, USA
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, USA
- Artificial Intelligence Imaging Research Center, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, USA
| | - Zhiyuan Li
- Imaging Research Center, Department of Radiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, USA
- Department of Computer Science, University of Cincinnati, Cincinnati, OH 45221, USA
| | - Kevin Du
- Imaging Research Center, Department of Radiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, USA
| | - Yu Zhu
- Imaging Research Center, Department of Radiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, USA
| | - Nehal A. Parikh
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH 45229, USA
| | - Lili He
- Imaging Research Center, Department of Radiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, USA
- Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH 45229, USA
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, USA
- Artificial Intelligence Imaging Research Center, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, USA
- Department of Computer Science, University of Cincinnati, Cincinnati, OH 45221, USA
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Ali R, Hussain J, Lee SW. Multilayer perceptron-based self-care early prediction of children with disabilities. Digit Health 2023; 9:20552076231184054. [PMID: 37426585 PMCID: PMC10328031 DOI: 10.1177/20552076231184054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 06/07/2023] [Indexed: 07/11/2023] Open
Abstract
Early identification of children with self-care impairments is one of the key challenges professional therapists face due to the complex and time-consuming detection process using relevant self-care activities. Due to the complex nature of the problem, machine-learning methods have been widely applied in this area. In this study, a feed-forward artificial neural network (ANN)-based self-care prediction methodology, called multilayer perceptron (MLP)-progressive, has been proposed. The proposed methodology integrates unsupervised instance-based resampling and randomizing preprocessing techniques to MLP for improved early detection of self-care disabilities in children. Preprocessing of the dataset affects the MLP performance; hence, randomization and resampling of the dataset improves the performance of the MLP model. To confirm the usefulness of MLP-progressive, three experiments were conducted, including validating MLP-progressive methodology over multi-class and binary-class datasets, impact analysis of the proposed preprocessing filters on the model performance, and comparing the MLP-progressive results with state-of-the-art studies. The evaluation metrics accuracy, precision, recall, F-measure, TP rate, FP rate, and ROC were used to measure performance of the proposed disability detection model. The proposed MLP-progressive model outperforms existing methods and attains a classification accuracy of 97.14% and 98.57% on multi-class and binary-class datasets, respectively. Additionally, when evaluated on the multi-class dataset, significant improvements in accuracies ranging from 90.00% to 97.14% were observed when compared to state-of-the-art methods.
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Affiliation(s)
- Rahman Ali
- Quaid-e-Azam College of Commerce, University of Peshawar, Khyber Pakhtunkhwa, Pakistan
| | - Jamil Hussain
- Department of Data Science, Sejong University, Seoul, Korea
| | - Seung Won Lee
- Sungkyunkwan University School of Medicine, Suwon, Korea
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Baker S, Kandasamy Y. Machine learning for understanding and predicting neurodevelopmental outcomes in premature infants: a systematic review. Pediatr Res 2023; 93:293-299. [PMID: 35641551 PMCID: PMC9153218 DOI: 10.1038/s41390-022-02120-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Revised: 02/25/2022] [Accepted: 05/08/2022] [Indexed: 12/24/2022]
Abstract
BACKGROUND Machine learning has been attracting increasing attention for use in healthcare applications, including neonatal medicine. One application for this tool is in understanding and predicting neurodevelopmental outcomes in preterm infants. In this study, we have carried out a systematic review to identify findings and challenges to date. METHODS This systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. Four databases were searched in February 2022, with articles then screened in a non-blinded manner by two authors. RESULTS The literature search returned 278 studies, with 11 meeting the eligibility criteria for inclusion. Convolutional neural networks were the most common machine learning approach, with most studies seeking to predict neurodevelopmental outcomes from images and connectomes describing brain structure and function. Studies to date also sought to identify features predictive of outcomes; however, results varied greatly. CONCLUSIONS Initial studies in this field have achieved promising results; however, many machine learning techniques remain to be explored, and the consensus is yet to be reached on which clinical and brain features are most predictive of neurodevelopmental outcomes. IMPACT This systematic review looks at the question of whether machine learning can be used to predict and understand neurodevelopmental outcomes in preterm infants. Our review finds that promising initial works have been conducted in this field, but many challenges and opportunities remain. Quality assessment of relevant articles is conducted using the Newcastle-Ottawa Scale. This work identifies challenges that remain and suggests several key directions for future research. To the best of the authors' knowledge, this is the first systematic review to explore this topic.
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Affiliation(s)
- Stephanie Baker
- College of Science and Engineering, James Cook University, Cairns, QLD, 4878, Australia.
| | - Yogavijayan Kandasamy
- grid.417216.70000 0000 9237 0383Department of Neonatology, Townsville Hospital and Health Service, Townsville, QLD 4810 Australia ,grid.1011.10000 0004 0474 1797College of Medicine and Dentistry, James Cook University, Townsville, QLD 4810 Australia
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Kline JE, Dudley J, Illapani VSP, Li H, Kline-Fath B, Tkach J, He L, Yuan W, Parikh NA. Diffuse excessive high signal intensity in the preterm brain on advanced MRI represents widespread neuropathology. Neuroimage 2022; 264:119727. [PMID: 36332850 PMCID: PMC9908008 DOI: 10.1016/j.neuroimage.2022.119727] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 10/26/2022] [Accepted: 10/31/2022] [Indexed: 11/06/2022] Open
Abstract
Preterm brains commonly exhibit elevated signal intensity in the white matter on T2-weighted MRI at term-equivalent age. This signal, known as diffuse excessive high signal intensity (DEHSI) or diffuse white matter abnormality (DWMA) when quantitatively assessed, is associated with abnormal microstructure on diffusion tensor imaging. However, postmortem data are largely lacking and difficult to obtain, and the pathological significance of DEHSI remains in question. In a cohort of 202 infants born preterm at ≤32 weeks gestational age, we leveraged two newer diffusion MRI models - Constrained Spherical Deconvolution (CSD) and neurite orientation dispersion and density index (NODDI) - to better characterize the macro and microstructural properties of DWMA and inform the ongoing debate around the clinical significance of DWMA. With increasing DWMA volume, fiber density broadly decreased throughout the white matter and fiber cross-section decreased in the major sensorimotor tracts. Neurite orientation dispersion decreased in the centrum semiovale, corona radiata, and temporal lobe. These findings provide insight into DWMA's biological underpinnings and demonstrate that it is a serious pathology.
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Affiliation(s)
- Julia E Kline
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Jon Dudley
- Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States; Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Venkata Sita Priyanka Illapani
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Hailong Li
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States; Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States; Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Beth Kline-Fath
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States; Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States; Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Jean Tkach
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States; Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States; Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Lili He
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States; Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States; Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Weihong Yuan
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States; Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States; Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Nehal A Parikh
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States; Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States.
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Artificial intelligence-based methods for fusion of electronic health records and imaging data. Sci Rep 2022; 12:17981. [PMID: 36289266 PMCID: PMC9605975 DOI: 10.1038/s41598-022-22514-4] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 10/17/2022] [Indexed: 01/24/2023] Open
Abstract
Healthcare data are inherently multimodal, including electronic health records (EHR), medical images, and multi-omics data. Combining these multimodal data sources contributes to a better understanding of human health and provides optimal personalized healthcare. The most important question when using multimodal data is how to fuse them-a field of growing interest among researchers. Advances in artificial intelligence (AI) technologies, particularly machine learning (ML), enable the fusion of these different data modalities to provide multimodal insights. To this end, in this scoping review, we focus on synthesizing and analyzing the literature that uses AI techniques to fuse multimodal medical data for different clinical applications. More specifically, we focus on studies that only fused EHR with medical imaging data to develop various AI methods for clinical applications. We present a comprehensive analysis of the various fusion strategies, the diseases and clinical outcomes for which multimodal fusion was used, the ML algorithms used to perform multimodal fusion for each clinical application, and the available multimodal medical datasets. We followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. We searched Embase, PubMed, Scopus, and Google Scholar to retrieve relevant studies. After pre-processing and screening, we extracted data from 34 studies that fulfilled the inclusion criteria. We found that studies fusing imaging data with EHR are increasing and doubling from 2020 to 2021. In our analysis, a typical workflow was observed: feeding raw data, fusing different data modalities by applying conventional machine learning (ML) or deep learning (DL) algorithms, and finally, evaluating the multimodal fusion through clinical outcome predictions. Specifically, early fusion was the most used technique in most applications for multimodal learning (22 out of 34 studies). We found that multimodality fusion models outperformed traditional single-modality models for the same task. Disease diagnosis and prediction were the most common clinical outcomes (reported in 20 and 10 studies, respectively) from a clinical outcome perspective. Neurological disorders were the dominant category (16 studies). From an AI perspective, conventional ML models were the most used (19 studies), followed by DL models (16 studies). Multimodal data used in the included studies were mostly from private repositories (21 studies). Through this scoping review, we offer new insights for researchers interested in knowing the current state of knowledge within this research field.
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Ali R, Li H, Dillman JR, Altaye M, Wang H, Parikh NA, He L. A self-training deep neural network for early prediction of cognitive deficits in very preterm infants using brain functional connectome data. Pediatr Radiol 2022; 52:2227-2240. [PMID: 36131030 PMCID: PMC9574648 DOI: 10.1007/s00247-022-05510-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 08/09/2022] [Accepted: 09/01/2022] [Indexed: 11/30/2022]
Abstract
BACKGROUND Deep learning has been employed using brain functional connectome data for evaluating the risk of cognitive deficits in very preterm infants. Although promising, training these deep learning models typically requires a large amount of labeled data, and labeled medical data are often very difficult and expensive to obtain. OBJECTIVE This study aimed to develop a self-training deep neural network (DNN) model for early prediction of cognitive deficits at 2 years of corrected age in very preterm infants (gestational age ≤32 weeks) using both labeled and unlabeled brain functional connectome data. MATERIALS AND METHODS We collected brain functional connectome data from 343 very preterm infants at a mean (standard deviation) postmenstrual age of 42.7 (2.5) weeks, among whom 103 children had a cognitive assessment at 2 years (i.e. labeled data), and the remaining 240 children had not received 2-year assessments at the time this study was conducted (i.e. unlabeled data). To develop a self-training DNN model, we built an initial student model using labeled brain functional connectome data. Then, we applied the trained model as a teacher model to generate pseudo-labels for unlabeled brain functional connectome data. Next, we combined labeled and pseudo-labeled data to train a new student model. We iterated this procedure to obtain the best student model for the early prediction task in very preterm infants. RESULTS In our cross-validation experiments, the proposed self-training DNN model achieved an accuracy of 71.0%, a specificity of 71.5%, a sensitivity of 70.4% and an area under the curve of 0.75, significantly outperforming transfer learning models through pre-training approaches. CONCLUSION We report the first self-training prognostic study in very preterm infants, efficiently utilizing a small amount of labeled data with a larger share of unlabeled data to aid the model training. The proposed technique is expected to facilitate deep learning with insufficient training data.
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Affiliation(s)
- Redha Ali
- Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
- Department of Radiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave., MLC 5033, Cincinnati, OH, 45229, USA
| | - Hailong Li
- Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
- Department of Radiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave., MLC 5033, Cincinnati, OH, 45229, USA
- Center for Artificial Intelligence in Imaging Research, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
- Center for Prevention of Neurodevelopmental Disorders, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Jonathan R Dillman
- Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
- Department of Radiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave., MLC 5033, Cincinnati, OH, 45229, USA
- Center for Artificial Intelligence in Imaging Research, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
- Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Mekibib Altaye
- Center for Prevention of Neurodevelopmental Disorders, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
- Biostatistics & Epidemiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Hui Wang
- Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
- Department of Radiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave., MLC 5033, Cincinnati, OH, 45229, USA
- MR Clinical Science, Philips, Cincinnati, OH, USA
| | - Nehal A Parikh
- Center for Prevention of Neurodevelopmental Disorders, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
- The Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Lili He
- Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
- Department of Radiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave., MLC 5033, Cincinnati, OH, 45229, USA.
- Center for Artificial Intelligence in Imaging Research, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
- Center for Prevention of Neurodevelopmental Disorders, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
- Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA.
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