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Liao J, Duan Y, Liu Y, Chen H, An Z, Chen Y, Su Z, Usman AM, Xiao G. Simvastatin alleviates glymphatic system damage via the VEGF-C/VEGFR3/PI3K-Akt pathway after experimental intracerebral hemorrhage. Brain Res Bull 2024; 216:111045. [PMID: 39097032 DOI: 10.1016/j.brainresbull.2024.111045] [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: 06/21/2024] [Revised: 07/28/2024] [Accepted: 07/31/2024] [Indexed: 08/05/2024]
Abstract
Current clinical practice primarily relies on surgical intervention to remove hematomas in patients with intracerebral hemorrhage (ICH), given the lack of effective drug therapies. Previous research indicates that simvastatin (SIM) may enhance hematoma absorption and resolution in the acute phase of ICH, though the precise mechanisms remain unclear. Recent findings have highlighted the glymphatic system (GS) as a crucial component in intracranial cerebrospinal fluid circulation, playing a significant role in hematoma clearance post-ICH. This study investigates the link between SIM efficacy in hematoma resolution and the GS. Our experimental results show that SIM alleviates GS damage in ICH-induced rats, resulting in improved outcomes such as reduced brain edema, neuronal apoptosis, and degeneration. Further analysis reveals that SIM's effects are mediated through the VEGF-C/VEGFR3/PI3K-Akt pathway. This study advances our understanding of SIM's mechanism in promoting intracranial hematoma clearance and underscores the potential of targeting the GS for ICH treatment.
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Affiliation(s)
- Junbo Liao
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China; Diagnosis and Treatment Center for Hydrocephalus, Xiangya Hospital, Central South University, Changsha, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Yingxing Duan
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Yaxue Liu
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China; Diagnosis and Treatment Center for Hydrocephalus, Xiangya Hospital, Central South University, Changsha, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Haolong Chen
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China; Diagnosis and Treatment Center for Hydrocephalus, Xiangya Hospital, Central South University, Changsha, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Zhihan An
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China; Diagnosis and Treatment Center for Hydrocephalus, Xiangya Hospital, Central South University, Changsha, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Yibing Chen
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China; Diagnosis and Treatment Center for Hydrocephalus, Xiangya Hospital, Central South University, Changsha, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Zhangjie Su
- Department of Neurosurgery, Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, Hills Road, Cambridge CB2 0QQ, UK
| | - Ahsan Muhammad Usman
- Department of Neurosurgery, Allied Hospital Faisalabad, Sargodha Road, Faisalabad 38000, Pakistan
| | - Gelei Xiao
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China; Diagnosis and Treatment Center for Hydrocephalus, Xiangya Hospital, Central South University, Changsha, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China.
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Shahid S, Wali A, Iftikhar S, Shaukat S, Zikria S, Rasheed J, Asuroglu T. Computational imaging for rapid detection of grade-I cerebral small vessel disease (cSVD). Heliyon 2024; 10:e37743. [PMID: 39309774 PMCID: PMC11416517 DOI: 10.1016/j.heliyon.2024.e37743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 09/06/2024] [Accepted: 09/09/2024] [Indexed: 09/25/2024] Open
Abstract
An early identification and subsequent management of cerebral small vessel disease (cSVD) grade 1 can delay progression into grades II and III. Machine learning algorithms have shown considerable promise in medical image interpretation automation. An experimental cross-sectional study aimed to develop an automated computer-aided diagnostic system based on AI (artificial intelligence) tools to detect grade 1-cSVD with improved accuracy. Patients with Fazekas grade 1 cSVD on Non-Contrast Magnetic Resonance Imaging (MRI) Brain of age >40 years of both genders were included. The dataset was pre-processed to be fed into a 3D convolutional neural network (CNN) model. A 3D stack with the shape (120, 128, 128, 1) containing axial slices from the brain magnetic resonance image was created. The model was created from scratch and contained four convolutional and three fully connected (FC) layers. The dataset was preprocessed by making a 3D stack, and normalizing, resizing, and completing the stack was performed. A 3D-CNN model architecture was designed to train and test preprocessed images. We achieved an accuracy of 93.12 % when 2D axial slices were used. When the 2D slices of a patient were stacked to form a 3D image, an accuracy of 85.71 % was achieved on the test set. Overall, the 3D-CNN model performed very well on the test set. The earliest and the most accurate diagnosis from computational imaging methods can help reduce the huge burden of cSVD and its associated morbidity in the form of vascular dementia.
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Affiliation(s)
- Saman Shahid
- Department of Sciences & Humanities, National University of Computer & Emerging Sciences (NUCES)-FAST Lahore Campus, Punjab, Pakistan
| | - Aamir Wali
- Department of Data Sciences, National University of Computer & Emerging Sciences (NUCES)-FAST Lahore Campus, Punjab, Pakistan
| | - Sadaf Iftikhar
- Department of Neurology, King Edward Medical University/Mayo Hospital, Lahore, Punjab, Pakistan
| | - Suneela Shaukat
- Department of Radiology, King Edward Medical University/Mayo Hospital, Lahore, Punjab, Pakistan
| | - Shahid Zikria
- Department of Sciences & Humanities, National University of Computer & Emerging Sciences (NUCES)-FAST Lahore Campus, Punjab, Pakistan
- Department of Computer Science, Information Technology University (ITU), Lahore, Punjab, Pakistan
| | - Jawad Rasheed
- Department of Computer Engineering, Istanbul Sabahattin Zaim University, Istanbul, 34303, Turkey
- Department of Software Engineering, Istanbul Nisantasi University, Istanbul, Turkey
- Deep Learning and Medical Image Analysis Laboratory, Bogazici University, Istanbul, Turkey
| | - Tunc Asuroglu
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
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Huang P, Liu L, Zhang Y, Zhong S, Liu P, Hong H, Wang S, Xie L, Lin M, Jiaerken Y, Luo X, Li K, Zeng Q, Cui L, Li J, Chen Y, Zhang R. Development and validation of a perivascular space segmentation method in multi-center datasets. Neuroimage 2024; 298:120803. [PMID: 39181194 DOI: 10.1016/j.neuroimage.2024.120803] [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: 05/22/2024] [Revised: 08/16/2024] [Accepted: 08/20/2024] [Indexed: 08/27/2024] Open
Abstract
BACKGROUND Perivascular spaces (PVS) visible on magnetic resonance imaging (MRI) are significant markers associated with various neurological diseases. Although quantitative analysis of PVS may enhance sensitivity and improve consistency across studies, the field lacks a universally validated method for analyzing images from multi-center studies. METHODS We annotated PVS on multi-center 3D T1-weighted (T1w) images acquired using scanners from three major vendors (Siemens, General Electric, and Philips). A neural network, mcPVS-Net (multi-center PVS segmentation network), was trained using data from 40 subjects and then tested in a separate cohort of 15 subjects. We assessed segmentation accuracy against ground truth masks tailored for each scanner vendor. Additionally, we evaluated the agreement between segmented PVS volumes and visual scores for each scanner. We also explored correlations between PVS volumes and various clinical factors such as age, hypertension, and white matter hyperintensities (WMH) in a larger sample of 1020 subjects. Furthermore, mcPVS-Net was applied to a new dataset comprising both T1w and T2-weighted (T2w) images from a United Imaging scanner to investigate if PVS volumes could discriminate between subjects with differing visual scores. We also compared the mcPVS-Net with a previously published method that segments PVS from T1 images. RESULTS In the test dataset, mcPVS-Net achieved a mean DICE coefficient of 0.80, with an average Precision of 0.81 and Recall of 0.79, indicating good specificity and sensitivity. The segmented PVS volumes were significantly associated with visual scores in both the basal ganglia (r = 0.541, p < 0.001) and white matter regions (r = 0.706, p < 0.001), and PVS volumes were significantly different among subjects with varying visual scores. Segmentation performance was consistent across different scanner vendors. PVS volumes exhibited significant associations with age, hypertension, and WMH. In the United Imaging scanner dataset, PVS volumes showed good associations with PVS visual scores evaluated on either T1w or T2w images. Compared to a previously published method, mcPVS-Net showed a higher accuracy and improved PVS segmentation in the basal ganglia region. CONCLUSION The mcPVS-Net demonstrated good accuracy for segmenting PVS from 3D T1w images. It may serve as a useful tool for future PVS research.
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Affiliation(s)
- Peiyu Huang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China; Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Lingyun Liu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China; Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yao Zhang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Siyan Zhong
- Department of Neurology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Peng Liu
- Department of Radiology, Linyi Traditional Chinese Medicine Hospital, Linyi, China
| | - Hui Hong
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Shuyue Wang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Linyun Xie
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Miao Lin
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yeerfan Jiaerken
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiao Luo
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Kaicheng Li
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Qingze Zeng
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Lei Cui
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jixuan Li
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yanxing Chen
- Department of Neurology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Ruiting Zhang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
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Waymont JMJ, Valdés Hernández MDC, Bernal J, Duarte Coello R, Brown R, Chappell FM, Ballerini L, Wardlaw JM. Systematic review and meta-analysis of automated methods for quantifying enlarged perivascular spaces in the brain. Neuroimage 2024; 297:120685. [PMID: 38914212 DOI: 10.1016/j.neuroimage.2024.120685] [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: 03/18/2024] [Revised: 05/20/2024] [Accepted: 06/10/2024] [Indexed: 06/26/2024] Open
Abstract
Research into magnetic resonance imaging (MRI)-visible perivascular spaces (PVS) has recently increased, as results from studies in different diseases and populations are cementing their association with sleep, disease phenotypes, and overall health indicators. With the establishment of worldwide consortia and the availability of large databases, computational methods that allow to automatically process all this wealth of information are becoming increasingly relevant. Several computational approaches have been proposed to assess PVS from MRI, and efforts have been made to summarise and appraise the most widely applied ones. We systematically reviewed and meta-analysed all publications available up to September 2023 describing the development, improvement, or application of computational PVS quantification methods from MRI. We analysed 67 approaches and 60 applications of their implementation, from 112 publications. The two most widely applied were the use of a morphological filter to enhance PVS-like structures, with Frangi being the choice preferred by most, and the use of a U-Net configuration with or without residual connections. Older adults or population studies comprising adults from 18 years old onwards were, overall, more frequent than studies using clinical samples. PVS were mainly assessed from T2-weighted MRI acquired in 1.5T and/or 3T scanners, although combinations using it with T1-weighted and FLAIR images were also abundant. Common associations researched included age, sex, hypertension, diabetes, white matter hyperintensities, sleep and cognition, with occupation-related, ethnicity, and genetic/hereditable traits being also explored. Despite promising improvements to overcome barriers such as noise and differentiation from other confounds, a need for joined efforts for a wider testing and increasing availability of the most promising methods is now paramount.
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Affiliation(s)
- Jennifer M J Waymont
- Centre for Clinical Brain Sciences, the University of Edinburgh, Chancellor's Building, 49 Little France Crescent, Edinburgh EH16 4SB, UK; UK Dementia Research Institute Centre at the University of Edinburgh, UK
| | - Maria Del C Valdés Hernández
- Centre for Clinical Brain Sciences, the University of Edinburgh, Chancellor's Building, 49 Little France Crescent, Edinburgh EH16 4SB, UK; UK Dementia Research Institute Centre at the University of Edinburgh, UK.
| | - José Bernal
- Centre for Clinical Brain Sciences, the University of Edinburgh, Chancellor's Building, 49 Little France Crescent, Edinburgh EH16 4SB, UK; UK Dementia Research Institute Centre at the University of Edinburgh, UK; German Centre for Neurodegenerative Diseases (DZNE), Germany; Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke University Magdeburg, Germany
| | - Roberto Duarte Coello
- Centre for Clinical Brain Sciences, the University of Edinburgh, Chancellor's Building, 49 Little France Crescent, Edinburgh EH16 4SB, UK; UK Dementia Research Institute Centre at the University of Edinburgh, UK
| | - Rosalind Brown
- Centre for Clinical Brain Sciences, the University of Edinburgh, Chancellor's Building, 49 Little France Crescent, Edinburgh EH16 4SB, UK; UK Dementia Research Institute Centre at the University of Edinburgh, UK
| | - Francesca M Chappell
- Centre for Clinical Brain Sciences, the University of Edinburgh, Chancellor's Building, 49 Little France Crescent, Edinburgh EH16 4SB, UK; UK Dementia Research Institute Centre at the University of Edinburgh, UK
| | | | - Joanna M Wardlaw
- Centre for Clinical Brain Sciences, the University of Edinburgh, Chancellor's Building, 49 Little France Crescent, Edinburgh EH16 4SB, UK; UK Dementia Research Institute Centre at the University of Edinburgh, UK
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Zhu F, Yao J, Feng M, Sun Z. Establishment and evaluation of a clinical prediction model for cognitive impairment in patients with cerebral small vessel disease. BMC Neurosci 2024; 25:35. [PMID: 39095700 PMCID: PMC11295716 DOI: 10.1186/s12868-024-00883-y] [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: 05/02/2024] [Accepted: 07/22/2024] [Indexed: 08/04/2024] Open
Abstract
BACKGROUND There are currently no effective prediction methods for evaluating the occurrence of cognitive impairment in patients with cerebral small vessel disease (CSVD). AIMS To investigate the risk factors for cognitive dysfunction in patients with CSVD and to construct a risk prediction model. METHODS A retrospective study was conducted on 227 patients with CSVD. All patients were assessed by brain magnetic resonance imaging (MRI), and the Montreal Cognitive Assessment (MoCA) was used to assess cognitive status. In addition, the patient's medical records were also recorded. The clinical data were divided into a normal cognitive function group and a cognitive impairment group. A MoCA score < 26 (an additional 1 point for education < 12 years) is defined as cognitive dysfunction. RESULTS A total of 227 patients (mean age 66.7 ± 6.99 years) with CSVD were included in this study, of whom 68.7% were male and 100 patients (44.1%) developed cognitive impairment. Age (OR = 1.070; 95% CI = 1.015 ~ 1.128, p < 0.05), hypertension (OR = 2.863; 95% CI = 1.438 ~ 5.699, p < 0.05), homocysteine(HCY) (OR = 1.065; 95% CI = 1.005 ~ 1.127, p < 0.05), lacunar infarct score(Lac_score) (OR = 2.732; 95% CI = 1.094 ~ 6.825, P < 0.05), and CSVD total burden (CSVD_score) (OR = 3.823; 95% CI = 1.496 ~ 9.768, P < 0.05) were found to be independent risk factors for cognitive decline in the present study. The above 5 variables were used to construct a nomogram, and the model was internally validated by using bootstrapping with a C-index of 0.839. The external model validation C-index was 0.867. CONCLUSIONS The nomogram model based on brain MR images and clinical data helps in individualizing the probability of cognitive impairment progression in patients with CSVD.
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Affiliation(s)
- Fangfang Zhu
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230000, China
- Department of Neurology, The Second Affiliated Hospital of Bengbu Medical University, Bengbu, 233000, China
| | - Jie Yao
- Department of Neurology, The Second Affiliated Hospital of Bengbu Medical University, Bengbu, 233000, China
| | - Min Feng
- Department of Neurology, The Second Affiliated Hospital of Bengbu Medical University, Bengbu, 233000, China
| | - Zhongwu Sun
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230000, China.
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Zhang Z, Ding Z, Chen F, Hua R, Wu J, Shen Z, Shi F, Xu X. Quantitative Analysis of Multimodal MRI Markers and Clinical Risk Factors for Cerebral Small Vessel Disease Based on Deep Learning. Int J Gen Med 2024; 17:739-750. [PMID: 38463439 PMCID: PMC10923240 DOI: 10.2147/ijgm.s446531] [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: 11/03/2023] [Accepted: 02/12/2024] [Indexed: 03/12/2024] Open
Abstract
Background Cerebral small vessel disease lacks specific clinical manifestations, and extraction of valuable features from multimodal images is expected to improve its diagnostic accuracy. In this study, we used deep learning techniques to segment cerebral small vessel disease imaging markers in multimodal magnetic resonance images and analyze them with clinical risk factors. Methods and results We recruited 211 lacunar stroke patients and 83 control patients. The patients' cerebral small vessel disease markers were automatically segmented using a V-shaped bottleneck network, and the number and volume were calculated after manual correction. The segmentation results of the V-shaped bottleneck network for white matter hyperintensity and recent small subcortical infarction were in high agreement with the ground truth (DSC>0.90). In small lesion segmentation, cerebral microbleed (average recall=0.778; average precision=0.758) and perivascular spaces (average recall=0.953; average precision=0.923) were superior to lacunar infarct (average recall=0.339; average precision=0.432) in recall and precision. Binary logistic regression analysis showed that age, systolic blood pressure, and total cerebral small vessel disease load score were independent risk factors for lacunar stroke (P<0.05). Ordered logistic regression analysis showed age was positively correlated with cerebral small vessel disease load score and total cholesterol was negatively correlated with cerebral small vessel disease score (P<0.05). Conclusion Lacunar stroke patients exhibited higher cerebral small vessel disease imaging markers, and age, systolic blood pressure, and total cerebral small vessel disease score were independent risk factors for lacunar stroke patients. V-shaped bottleneck network segmentation network based on multimodal deep learning can segment and quantify various cerebral small vessel disease lesions to some extent.
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Affiliation(s)
- Zhiliang Zhang
- School of Medical Imaging, Hangzhou Medical College, Hangzhou, People’s Republic of China
| | - Zhongxiang Ding
- Department of Radiology, Affiliated Hangzhou First People’s Hospital, Westlake University School of Medicine, Hangzhou, People’s Republic of China
| | - Fenyang Chen
- Department of Radiology, Affiliated Hangzhou First People’s Hospital, Westlake University School of Medicine, Hangzhou, People’s Republic of China
| | - Rui Hua
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd, Shanghai, People’s Republic of China
| | - Jiaojiao Wu
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd, Shanghai, People’s Republic of China
| | - Zhefan Shen
- Department of Radiology, Affiliated Hangzhou First People’s Hospital, Westlake University School of Medicine, Hangzhou, People’s Republic of China
| | - Feng Shi
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd, Shanghai, People’s Republic of China
| | - Xiufang Xu
- School of Medical Imaging, Hangzhou Medical College, Hangzhou, People’s Republic of China
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Parillo M, Vaccarino F, Di Gennaro G, Kumar S, Van Goethem J, Beomonte Zobel B, Quattrocchi CC, Parizel PM, Mallio CA. Overview of the Current Knowledge and Conventional MRI Characteristics of Peri- and Para-Vascular Spaces. Brain Sci 2024; 14:138. [PMID: 38391713 PMCID: PMC10886993 DOI: 10.3390/brainsci14020138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2023] [Revised: 01/10/2024] [Accepted: 01/26/2024] [Indexed: 02/24/2024] Open
Abstract
Brain spaces around (perivascular spaces) and alongside (paravascular or Virchow-Robin spaces) vessels have gained significant attention in recent years due to the advancements of in vivo imaging tools and to their crucial role in maintaining brain health, contributing to the anatomic foundation of the glymphatic system. In fact, it is widely accepted that peri- and para-vascular spaces function as waste clearance pathways for the brain for materials such as ß-amyloid by allowing exchange between cerebrospinal fluid and interstitial fluid. Visible brain spaces on magnetic resonance imaging are often a normal finding, but they have also been associated with a wide range of neurological and systemic conditions, suggesting their potential as early indicators of intracranial pressure and neurofluid imbalance. Nonetheless, several aspects of these spaces are still controversial. This article offers an overview of the current knowledge and magnetic resonance imaging characteristics of peri- and para-vascular spaces, which can help in daily clinical practice image description and interpretation. This paper is organized into different sections, including the microscopic anatomy of peri- and para-vascular spaces, their associations with pathological and physiological events, and their differential diagnosis.
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Affiliation(s)
- Marco Parillo
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200, 00128 Roma, Italy
- Research Unit of Diagnostic Imaging and Interventional Radiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21, 00128 Roma, Italy
| | - Federica Vaccarino
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200, 00128 Roma, Italy
- Research Unit of Diagnostic Imaging and Interventional Radiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21, 00128 Roma, Italy
| | - Gianfranco Di Gennaro
- Department of Health Sciences, Chair of Medical Statistics, University of Catanzaro "Magna Græcia", 88100 Catanzaro, Italy
| | - Sumeet Kumar
- Department of Neuroradiology, National Neuroscience Institute, Singapore 308433, Singapore
- Duke-National University of Singapore Medical School, Singapore 169857, Singapore
| | - Johan Van Goethem
- Department of Radiology, Antwerp University Hospital, 2650 Edegem, Belgium
| | - Bruno Beomonte Zobel
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200, 00128 Roma, Italy
- Research Unit of Diagnostic Imaging and Interventional Radiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21, 00128 Roma, Italy
| | - Carlo Cosimo Quattrocchi
- Centre for Medical Sciences-CISMed, University of Trento, Via S. Maria Maddalena 1, 38122 Trento, Italy
| | - Paul M Parizel
- Royal Perth Hospital & University of Western Australia, Perth, WA 6000, Australia
- Medical School, University of Western Australia, Perth, WA 6009, Australia
| | - Carlo Augusto Mallio
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200, 00128 Roma, Italy
- Research Unit of Diagnostic Imaging and Interventional Radiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21, 00128 Roma, Italy
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Zhu HH, Li SS, Wang YC, Song B, Gao Y, Xu YM, Li YS. Clearance dysfunction of trans-barrier transport and lymphatic drainage in cerebral small vessel disease: Review and prospect. Neurobiol Dis 2023; 189:106347. [PMID: 37951367 DOI: 10.1016/j.nbd.2023.106347] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2023] [Revised: 11/08/2023] [Accepted: 11/08/2023] [Indexed: 11/14/2023] Open
Abstract
Cerebral small vessel disease (CSVD) causes 20%-25% of stroke and contributes to 45% of dementia cases worldwide. However, since its early symptoms are inconclusive in addition to the complexity of the pathological basis, there is a rather limited effective therapies and interventions. Recently, accumulating evidence suggested that various brain-waste-clearance dysfunctions are closely related to the pathogenesis and prognosis of CSVD, and after a comprehensive and systematic review we classified them into two broad categories: trans-barrier transport and lymphatic drainage. The former includes blood brain barrier and blood-cerebrospinal fluid barrier, and the latter, glymphatic-meningeal lymphatic system and intramural periarterial drainage pathway. We summarized the concepts and potential mechanisms of these clearance systems, proposing a relatively complete framework for elucidating their interactions with CSVD. In addition, we also discussed recent advances in therapeutic strategies targeting clearance dysfunction, which may be an important area for future CSVD research.
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Affiliation(s)
- Hang-Hang Zhu
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China; NHC Key Laboratory of Prevention and Treatment of Cerebrovascular Diseases, China.
| | - Shan-Shan Li
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
| | - Yun-Chao Wang
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China; NHC Key Laboratory of Prevention and Treatment of Cerebrovascular Diseases, China.
| | - Bo Song
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China; NHC Key Laboratory of Prevention and Treatment of Cerebrovascular Diseases, China.
| | - Yuan Gao
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China; NHC Key Laboratory of Prevention and Treatment of Cerebrovascular Diseases, China.
| | - Yu-Ming Xu
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China; NHC Key Laboratory of Prevention and Treatment of Cerebrovascular Diseases, China.
| | - Yu-Sheng Li
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China; NHC Key Laboratory of Prevention and Treatment of Cerebrovascular Diseases, China.
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de Vries BM, Zwezerijnen GJC, Burchell GL, van Velden FHP, Menke-van der Houven van Oordt CW, Boellaard R. Explainable artificial intelligence (XAI) in radiology and nuclear medicine: a literature review. Front Med (Lausanne) 2023; 10:1180773. [PMID: 37250654 PMCID: PMC10213317 DOI: 10.3389/fmed.2023.1180773] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 04/17/2023] [Indexed: 05/31/2023] Open
Abstract
Rational Deep learning (DL) has demonstrated a remarkable performance in diagnostic imaging for various diseases and modalities and therefore has a high potential to be used as a clinical tool. However, current practice shows low deployment of these algorithms in clinical practice, because DL algorithms lack transparency and trust due to their underlying black-box mechanism. For successful employment, explainable artificial intelligence (XAI) could be introduced to close the gap between the medical professionals and the DL algorithms. In this literature review, XAI methods available for magnetic resonance (MR), computed tomography (CT), and positron emission tomography (PET) imaging are discussed and future suggestions are made. Methods PubMed, Embase.com and Clarivate Analytics/Web of Science Core Collection were screened. Articles were considered eligible for inclusion if XAI was used (and well described) to describe the behavior of a DL model used in MR, CT and PET imaging. Results A total of 75 articles were included of which 54 and 17 articles described post and ad hoc XAI methods, respectively, and 4 articles described both XAI methods. Major variations in performance is seen between the methods. Overall, post hoc XAI lacks the ability to provide class-discriminative and target-specific explanation. Ad hoc XAI seems to tackle this because of its intrinsic ability to explain. However, quality control of the XAI methods is rarely applied and therefore systematic comparison between the methods is difficult. Conclusion There is currently no clear consensus on how XAI should be deployed in order to close the gap between medical professionals and DL algorithms for clinical implementation. We advocate for systematic technical and clinical quality assessment of XAI methods. Also, to ensure end-to-end unbiased and safe integration of XAI in clinical workflow, (anatomical) data minimization and quality control methods should be included.
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Affiliation(s)
- Bart M. de Vries
- Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Gerben J. C. Zwezerijnen
- Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | | | | | | | - Ronald Boellaard
- Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
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10
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Okar SV, Hu F, Shinohara RT, Beck ES, Reich DS, Ineichen BV. The etiology and evolution of magnetic resonance imaging-visible perivascular spaces: Systematic review and meta-analysis. Front Neurosci 2023; 17:1038011. [PMID: 37065926 PMCID: PMC10098201 DOI: 10.3389/fnins.2023.1038011] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 03/15/2023] [Indexed: 04/03/2023] Open
Abstract
ObjectivesPerivascular spaces have been involved in neuroinflammatory and neurodegenerative diseases. Upon a certain size, these spaces can become visible on magnetic resonance imaging (MRI), referred to as enlarged perivascular spaces (EPVS) or MRI-visible perivascular spaces (MVPVS). However, the lack of systematic evidence on etiology and temporal dynamics of MVPVS hampers their diagnostic utility as MRI biomarker. Thus, the goal of this systematic review was to summarize potential etiologies and evolution of MVPVS.MethodsIn a comprehensive literature search, out of 1,488 unique publications, 140 records assessing etiopathogenesis and dynamics of MVPVS were eligible for a qualitative summary. 6 records were included in a meta-analysis to assess the association between MVPVS and brain atrophy.ResultsFour overarching and partly overlapping etiologies of MVPVS have been proposed: (1) Impairment of interstitial fluid circulation, (2) Spiral elongation of arteries, (3) Brain atrophy and/or perivascular myelin loss, and (4) Immune cell accumulation in the perivascular space. The meta-analysis in patients with neuroinflammatory diseases did not support an association between MVPVS and brain volume measures [R: −0.15 (95%-CI −0.40–0.11)]. Based on few and mostly small studies in tumefactive MVPVS and in vascular and neuroinflammatory diseases, temporal evolution of MVPVS is slow.ConclusionCollectively, this study provides high-grade evidence for MVPVS etiopathogenesis and temporal dynamics. Although several potential etiologies for MVPVS emergence have been proposed, they are only partially supported by data. Advanced MRI methods should be employed to further dissect etiopathogenesis and evolution of MVPVS. This can benefit their implementation as an imaging biomarker.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?RecordID=346564, identifier CRD42022346564.
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Affiliation(s)
- Serhat V. Okar
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States
| | - Fengling Hu
- Department of Biostatistics, Epidemiology, and Informatics, Penn Statistics in Imaging and Visualization Center, University of Pennsylvania, Philadelphia, PA, United States
| | - Russell T. Shinohara
- Department of Biostatistics, Epidemiology, and Informatics, Penn Statistics in Imaging and Visualization Center, University of Pennsylvania, Philadelphia, PA, United States
| | - Erin S. Beck
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Daniel S. Reich
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States
| | - Benjamin V. Ineichen
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States
- Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
- Center for Reproducible Science, University of Zurich, Zurich, Switzerland
- *Correspondence: Benjamin V. Ineichen, , ; orcid.org/0000-0003-1362-4819
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11
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White Matter Hyperintensities in Young Patients from a Neurological Outpatient Clinic: Prevalence, Risk Factors, and Correlation with Enlarged Perivascular Spaces. J Pers Med 2023; 13:jpm13030525. [PMID: 36983707 PMCID: PMC10054337 DOI: 10.3390/jpm13030525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 03/07/2023] [Accepted: 03/12/2023] [Indexed: 03/17/2023] Open
Abstract
(1) Background: to investigate the prevalence of white matter hyperintensities (WMH), risk factors, and correlation with enlarged perivascular spaces (ePVS) among young patients (age, 16–45 years) in a neurological outpatient clinic. (2) Methods: a total of 887 young patients who underwent a head magnetic resonance imaging (MRI)examination between 1 June 2021, and 30 November 2021, were included in this study. Paraventricular WMH (PWMH), deep WMH (DWMH), ePVS in the centrum semiovale (CSO-ePVS), and basal ganglia (BG-ePVS) were rated. Logistic regression analysis was used to identify the best predictors for the presence of WMH and, for the association of the severity of ePVS with the presence of WMH. Goodman–Kruskal gamma test was used to assess the correlation between the severity of ePVS and WMH. (3) Results: the prevalence of WMH was 37.0%, with low severity. Age, hypertension (p < 0.001), headache (p = 0.031), syncope (p = 0.012), and sleep disturbance (p = 0.003) were associated with the presence of DWMH. Age, sex (p = 0.032), hypertension (p = 0.004) and sleep disturbance (p < 0.001) were associated with the presence of PWMH. The severity of CSO-ePVS was associated with the presence and the severities of DWMH. The severity of BG-ePVS was associated with the presence and severities of DWMH and PWMH. (4) Conclusions: the prevalence of WMH was 37% and mild in young patients without specific causes. Older age, female, hypertension, headache, syncope, and sleep disturbance were associated with WMH. The severity of ePVS had an impact on the presence and severity of WMH in the corresponding brain regions.
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12
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Chen X, Luo Y, Zhang S, Yang X, Dong Z, Wang Y, Wu D. Deep medullary veins: a promising neuroimaging marker for mild cognitive impairment in outpatients. BMC Neurol 2023; 23:3. [PMID: 36604624 PMCID: PMC9814341 DOI: 10.1186/s12883-022-03037-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 12/19/2022] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND AND PURPOSE Mild cognitive impairment is an age-dependent pre-dementia state caused by varied reasons. Early detection of MCI helps handle dementia. Vascular factors are vital for the occurrence of MCI. This study investigates the correlation between deep medullary veins and multi-dimensional cognitive outcomes. MATERIALS AND METHODS A total of 73 participants with MCI and 32 controls were enrolled. Minimum Mental State Examination and Montreal Cognitive Assessment were used to examine the global cognitive function, and different cognitive domains were measured by specific neuropsychological tests. MRI was used to assess the visibility of the DMV and other neuroimage markers. RESULTS DMV score was statistically significantly higher in the MCI group compared with the control group (P = 0.009) and independently related to MCI (P = 0.007). Linear regression analysis verified that DMV score was linearly related to global cognition, memory, attention, and executive function after adjusting for cerebrovascular risk factors. CONCLUSION DMV score was independently related to the onset of MCI, and correlates with overall cognition, memory, attention, and executive function in outpatients.
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Affiliation(s)
- Xiuqi Chen
- grid.8547.e0000 0001 0125 2443Department of Neurology, Shanghai Fifth People’s Hospital, Fudan University, No.801, He Qing Road, Minhang District, Shanghai, China
| | - Yufan Luo
- grid.8547.e0000 0001 0125 2443Department of Neurology, Shanghai Fifth People’s Hospital, Fudan University, No.801, He Qing Road, Minhang District, Shanghai, China
| | - Shufan Zhang
- grid.8547.e0000 0001 0125 2443Department of Neurology, Shanghai Fifth People’s Hospital, Fudan University, No.801, He Qing Road, Minhang District, Shanghai, China ,grid.8547.e0000 0001 0125 2443Department of Neurology, Shanghai Jing’an District Central Hospital, Fudan University, Shanghai, China
| | - Xiaoli Yang
- grid.8547.e0000 0001 0125 2443Department of Neurology, Shanghai Fifth People’s Hospital, Fudan University, No.801, He Qing Road, Minhang District, Shanghai, China
| | - Zhiyuan Dong
- grid.8547.e0000 0001 0125 2443Department of Neurology, Shanghai Fifth People’s Hospital, Fudan University, No.801, He Qing Road, Minhang District, Shanghai, China
| | - Yilin Wang
- Georgetown Preparatory School, North Bethesda, MD Washington, USA
| | - Danhong Wu
- grid.8547.e0000 0001 0125 2443Department of Neurology, Shanghai Fifth People’s Hospital, Fudan University, No.801, He Qing Road, Minhang District, Shanghai, China
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13
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Pham W, Lynch M, Spitz G, O’Brien T, Vivash L, Sinclair B, Law M. A critical guide to the automated quantification of perivascular spaces in magnetic resonance imaging. Front Neurosci 2022; 16:1021311. [PMID: 36590285 PMCID: PMC9795229 DOI: 10.3389/fnins.2022.1021311] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Accepted: 11/16/2022] [Indexed: 12/15/2022] Open
Abstract
The glymphatic system is responsible for waste clearance in the brain. It is comprised of perivascular spaces (PVS) that surround penetrating blood vessels. These spaces are filled with cerebrospinal fluid and interstitial fluid, and can be seen with magnetic resonance imaging. Various algorithms have been developed to automatically label these spaces in MRI. This has enabled volumetric and morphological analyses of PVS in healthy and disease cohorts. However, there remain inconsistencies between PVS measures reported by different methods of automated segmentation. The present review emphasizes that importance of voxel-wise evaluation of model performance, mainly with the Sørensen Dice similarity coefficient. Conventional count correlations for model validation are inadequate if the goal is to assess volumetric or morphological measures of PVS. The downside of voxel-wise evaluation is that it requires manual segmentations that require large amounts of time to produce. One possible solution is to derive these semi-automatically. Additionally, recommendations are made to facilitate rigorous development and validation of automated PVS segmentation models. In the application of automated PVS segmentation tools, publication of image quality metrics, such as the contrast-to-noise ratio, alongside descriptive statistics of PVS volumes and counts will facilitate comparability between studies. Lastly, a head-to-head comparison between two algorithms, applied to two cohorts of astronauts reveals how results can differ substantially between techniques.
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Affiliation(s)
- William Pham
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC, Australia
| | - Miranda Lynch
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC, Australia
| | - Gershon Spitz
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC, Australia
- Monash-Epworth Rehabilitation Research Centre, Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, VIC, Australia
| | - Terence O’Brien
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC, Australia
- Department of Neurology, Alfred Hospital, Melbourne, VIC, Australia
- Department of Medicine, The Royal Melbourne Hospital, University of Melbourne, Melbourne, VIC, Australia
- Department of Neurology, The Royal Melbourne Hospital, University of Melbourne, Melbourne, VIC, Australia
| | - Lucy Vivash
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC, Australia
- Department of Neurology, Alfred Hospital, Melbourne, VIC, Australia
- Department of Medicine, The Royal Melbourne Hospital, University of Melbourne, Melbourne, VIC, Australia
- Department of Neurology, The Royal Melbourne Hospital, University of Melbourne, Melbourne, VIC, Australia
| | - Benjamin Sinclair
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC, Australia
- Department of Neurology, Alfred Hospital, Melbourne, VIC, Australia
- Department of Medicine, The Royal Melbourne Hospital, University of Melbourne, Melbourne, VIC, Australia
| | - Meng Law
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC, Australia
- Department of Radiology, Alfred Health Hospital, Melbourne, VIC, Australia
- Department of Electrical and Computer Systems Engineering, Monash University, Melbourne, VIC, Australia
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14
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Loh HW, Ooi CP, Seoni S, Barua PD, Molinari F, Acharya UR. Application of explainable artificial intelligence for healthcare: A systematic review of the last decade (2011-2022). COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 226:107161. [PMID: 36228495 DOI: 10.1016/j.cmpb.2022.107161] [Citation(s) in RCA: 113] [Impact Index Per Article: 56.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 09/16/2022] [Accepted: 09/25/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND AND OBJECTIVES Artificial intelligence (AI) has branched out to various applications in healthcare, such as health services management, predictive medicine, clinical decision-making, and patient data and diagnostics. Although AI models have achieved human-like performance, their use is still limited because they are seen as a black box. This lack of trust remains the main reason for their low use in practice, especially in healthcare. Hence, explainable artificial intelligence (XAI) has been introduced as a technique that can provide confidence in the model's prediction by explaining how the prediction is derived, thereby encouraging the use of AI systems in healthcare. The primary goal of this review is to provide areas of healthcare that require more attention from the XAI research community. METHODS Multiple journal databases were thoroughly searched using PRISMA guidelines 2020. Studies that do not appear in Q1 journals, which are highly credible, were excluded. RESULTS In this review, we surveyed 99 Q1 articles covering the following XAI techniques: SHAP, LIME, GradCAM, LRP, Fuzzy classifier, EBM, CBR, rule-based systems, and others. CONCLUSION We discovered that detecting abnormalities in 1D biosignals and identifying key text in clinical notes are areas that require more attention from the XAI research community. We hope this is review will encourage the development of a holistic cloud system for a smart city.
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Affiliation(s)
- Hui Wen Loh
- School of Science and Technology, Singapore University of Social Sciences, Singapore
| | - Chui Ping Ooi
- School of Science and Technology, Singapore University of Social Sciences, Singapore
| | - Silvia Seoni
- Department of Electronics and Telecommunications, Biolab, Politecnico di Torino, Torino 10129, Italy
| | - Prabal Datta Barua
- Faculty of Engineering and Information Technology, University of Technology Sydney, Australia; School of Business (Information Systems), Faculty of Business, Education, Law & Arts, University of Southern Queensland, Australia
| | - Filippo Molinari
- Department of Electronics and Telecommunications, Biolab, Politecnico di Torino, Torino 10129, Italy
| | - U Rajendra Acharya
- School of Science and Technology, Singapore University of Social Sciences, Singapore; School of Business (Information Systems), Faculty of Business, Education, Law & Arts, University of Southern Queensland, Australia; School of Engineering, Ngee Ann Polytechnic, Singapore; Department of Bioinformatics and Medical Engineering, Asia University, Taiwan; Research Organization for Advanced Science and Technology (IROAST), Kumamoto University, Kumamoto, Japan.
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