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Torres-Simon L, Del Cerro-León A, Yus M, Bruña R, Gil-Martinez L, Marcos Dolado A, Maestú F, Arrazola-Garcia J, Cuesta P. Decoding the Best Automated Segmentation Tools for Vascular White Matter Hyperintensities in the Aging Brain: A Clinician's Guide to Precision and Purpose. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.03.30.23287946. [PMID: 38798616 PMCID: PMC11118558 DOI: 10.1101/2023.03.30.23287946] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Cerebrovascular damage from small vessel disease (SVD) occurs in healthy and pathological aging. SVD markers, such as white matter hyperintensities (WMH), are commonly found in individuals over 60 and increase in prevalence with age. WMHs are detectable on standard MRI by adhering to the STRIVE criteria. Currently, visual assessment scales are used in clinical and research scenarios but is time-consuming and has rater variability, limiting its practicality. Addressing this issue, our study aimed to determine the most precise WMH segmentation software, offering insights into methodology and usability to balance clinical precision with practical application. This study employed a dataset comprising T1, FLAIR, and DWI images from 300 cognitively healthy older adults. WMHs in this cohort were evaluated using four automated neuroimaging tools: Lesion Prediction Algorithm (LPA) and Lesion Growth Algorithm (LGA) from Lesion Segmentation Tool (LST), Sequence Adaptive Multimodal Segmentation (SAMSEG), and Brain Intensity Abnormalities Classification Algorithm (BIANCA). Additionally, clinicians manually segmented WMHs in a subsample of 45 participants to establish a gold standard. The study assessed correlations with the Fazekas scale, algorithm performance, and the influence of WMH volume on reliability. Results indicated that supervised algorithms were superior, particularly in detecting small WMHs, and can improve their consistency when used in parallel with unsupervised tools. The research also proposed a biomarker for moderate vascular damage, derived from the top 95th percentile of WMH volume in healthy individuals aged 50 to 60. This biomarker effectively differentiated subgroups within the cohort, correlating with variations in brain structure and behavior.
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Rachmadi MF, Byra M, Skibbe H. A new family of instance-level loss functions for improving instance-level segmentation and detection of white matter hyperintensities in routine clinical brain MRI. Comput Biol Med 2024; 174:108414. [PMID: 38599072 DOI: 10.1016/j.compbiomed.2024.108414] [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: 10/17/2023] [Revised: 03/16/2024] [Accepted: 04/03/2024] [Indexed: 04/12/2024]
Abstract
In this study, we introduce "instance loss functions", a new family of loss functions designed to enhance the training of neural networks in the instance-level segmentation and detection of objects in biomedical image data, particularly those of varied numbers and sizes. Intended to be utilized conjointly with traditional loss functions, these proposed functions, prioritize object instances over pixel-by-pixel comparisons. The specific functions, the instance segmentation loss (Linstance), the instance center loss (Lcenter), the false instance rate loss (Lfalse), and the instance proximity loss (Lproximity), serve distinct purposes. Specifically, Linstance improves instance-wise segmentation quality, Lcenter enhances segmentation quality of small instances, Lfalse minimizes the rate of false and missed detections across varied instance sizes, and Lproximity improves detection quality by pulling predicted instances towards the ground truth instances. Through the task of segmenting white matter hyperintensities (WMH) in brain MRI, we benchmarked our proposed instance loss functions, both individually and in combination via an ensemble inference models approach, against traditional pixel-level loss functions. Data were sourced from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the WMH Segmentation Challenge datasets, which exhibit significant variation in WMH instance sizes. Empirical evaluations demonstrate that combining two instance-level loss functions through ensemble inference models outperforms models using other loss function on both the ADNI and WMH Segmentation Challenge datasets for the segmentation and detection of WMH instances. Further, applying these functions to the segmentation of nuclei in histopathology images demonstrated their effectiveness and generalizability beyond WMH, improving performance even in contexts with less severe instance imbalance.
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Affiliation(s)
- Muhammad Febrian Rachmadi
- Brain Image Analysis Unit, RIKEN Center for Brain Science, Wako-shi, Japan; Faculty of Computer Science, Universitas Indonesia, Depok, Indonesia.
| | - Michal Byra
- Brain Image Analysis Unit, RIKEN Center for Brain Science, Wako-shi, Japan; Institute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw, Poland
| | - Henrik Skibbe
- Brain Image Analysis Unit, RIKEN Center for Brain Science, Wako-shi, Japan
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Dong C, Hayashi S. Deep learning applications in vascular dementia using neuroimaging. Curr Opin Psychiatry 2024; 37:101-106. [PMID: 38226547 DOI: 10.1097/yco.0000000000000920] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/17/2024]
Abstract
PURPOSE OF REVIEW Vascular dementia (VaD) is the second common cause of dementia after Alzheimer's disease, and deep learning has emerged as a critical tool in dementia research. The aim of this article is to highlight the current deep learning applications in VaD-related imaging biomarkers and diagnosis. RECENT FINDINGS The main deep learning technology applied in VaD using neuroimaging data is convolutional neural networks (CNN). CNN models have been widely used for lesion detection and segmentation, such as white matter hyperintensities (WMH), cerebral microbleeds (CMBs), perivascular spaces (PVS), lacunes, cortical superficial siderosis, and brain atrophy. Applications in VaD subtypes classification also showed excellent results. CNN-based deep learning models have potential for further diagnosis and prognosis of VaD. SUMMARY Deep learning neural networks with neuroimaging data in VaD research represent significant promise for advancing early diagnosis and treatment strategies. Ongoing research and collaboration between clinicians, data scientists, and neuroimaging experts are essential to address challenges and unlock the full potential of deep learning in VaD diagnosis and management.
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Affiliation(s)
- Chao Dong
- Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry & Mental Health, School of Clinical Medicine, UNSW Sydney, NSW, Australia
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Tsuchida A, Boutinaud P, Verrecchia V, Tzourio C, Debette S, Joliot M. Early detection of white matter hyperintensities using SHIVA-WMH detector. Hum Brain Mapp 2024; 45:e26548. [PMID: 38050769 PMCID: PMC10789222 DOI: 10.1002/hbm.26548] [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: 05/03/2023] [Revised: 10/06/2023] [Accepted: 11/16/2023] [Indexed: 12/06/2023] Open
Abstract
White matter hyperintensities (WMHs) are well-established markers of cerebral small vessel disease, and are associated with an increased risk of stroke, dementia, and mortality. Although their prevalence increases with age, small and punctate WMHs have been reported with surprisingly high frequency even in young, neurologically asymptomatic adults. However, most automated methods to segment WMH published to date are not optimized for detecting small and sparse WMH. Here we present the SHIVA-WMH tool, a deep-learning (DL)-based automatic WMH segmentation tool that has been trained with manual segmentations of WMH in a wide range of WMH severity. We show that it is able to detect WMH with high efficiency in subjects with only small punctate WMH as well as in subjects with large WMHs (i.e., with confluency) in evaluation datasets from three distinct databases: magnetic resonance imaging-Share consisting of young university students, MICCAI 2017 WMH challenge dataset consisting of older patients from memory clinics, and UK Biobank with community-dwelling middle-aged and older adults. Across these three cohorts with a wide-ranging WMH load, our tool achieved voxel-level and individual lesion cluster-level Dice scores of 0.66 and 0.71, respectively, which were higher than for three reference tools tested: the lesion prediction algorithm implemented in the lesion segmentation toolbox (LPA: Schmidt), PGS tool, a DL-based algorithm and the current winner of the MICCAI 2017 WMH challenge (Park et al.), and HyperMapper tool (Mojiri Forooshani et al.), another DL-based method with high reported performance in subjects with mild WMH burden. Our tool is publicly and openly available to the research community to facilitate investigations of WMH across a wide range of severity in other cohorts, and to contribute to our understanding of the emergence and progression of WMH.
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Affiliation(s)
- Ami Tsuchida
- GIN, IMN‐UMR5293Université de Bordeaux, CEA, CNRSBordeauxFrance
- BPH‐U1219, INSERMUniversité de BordeauxBordeauxFrance
| | | | - Violaine Verrecchia
- GIN, IMN‐UMR5293Université de Bordeaux, CEA, CNRSBordeauxFrance
- BPH‐U1219, INSERMUniversité de BordeauxBordeauxFrance
| | | | | | - Marc Joliot
- GIN, IMN‐UMR5293Université de Bordeaux, CEA, CNRSBordeauxFrance
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Strain JF, Rahmani M, Dierker D, Owen C, Jafri H, Vlassenko AG, Womack K, Fripp J, Tosun D, Benzinger TLS, Weiner M, Masters C, Lee JM, Morris JC, Goyal MS. Accuracy of TrUE-Net in comparison to established white matter hyperintensity segmentation methods: An independent validation study. Neuroimage 2024; 285:120494. [PMID: 38086495 DOI: 10.1016/j.neuroimage.2023.120494] [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: 06/28/2023] [Revised: 10/23/2023] [Accepted: 12/09/2023] [Indexed: 12/17/2023] Open
Abstract
White matter hyperintensities (WMH) are nearly ubiquitous in the aging brain, and their topography and overall burden are associated with cognitive decline. Given their numerosity, accurate methods to automatically segment WMH are needed. Recent developments, including the availability of challenge data sets and improved deep learning algorithms, have led to a new promising deep-learning based automated segmentation model called TrUE-Net, which has yet to undergo rigorous independent validation. Here, we compare TrUE-Net to six established automated WMH segmentation tools, including a semi-manual method. We evaluated the techniques at both global and regional level to compare their ability to detect the established relationship between WMH burden and age. We found that TrUE-Net was highly reliable at identifying WMH regions with low false positive rates, when compared to semi-manual segmentation as the reference standard. TrUE-Net performed similarly or favorably when compared to the other automated techniques. Moreover, TrUE-Net was able to detect relationships between WMH and age to a similar degree as the reference standard semi-manual segmentation at both the global and regional level. These results support the use of TrUE-Net for identifying WMH at the global or regional level, including in large, combined datasets.
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Affiliation(s)
- Jeremy F Strain
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA; Neuroimaging Labs Research Center, Washington University School of Medicine, St. Louis MO, USA.
| | - Maryam Rahmani
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA; Neuroimaging Labs Research Center, Washington University School of Medicine, St. Louis MO, USA
| | - Donna Dierker
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA; Neuroimaging Labs Research Center, Washington University School of Medicine, St. Louis MO, USA
| | - Christopher Owen
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Hussain Jafri
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Andrei G Vlassenko
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA; Neuroimaging Labs Research Center, Washington University School of Medicine, St. Louis MO, USA
| | - Kyle Womack
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Jurgen Fripp
- The Australian e-Health Research Centre, CSIRO Health and Biosecurity, Brisbane, QLD, Australia
| | - Duygu Tosun
- Division of Radiology and Biomedical Imaging, University of California - San Francisco, San Francisco, CA, USA
| | - Tammie L S Benzinger
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA; Knight Alzheimer Disease Research Center, St. Louis, MO, USA
| | - Michael Weiner
- Division of Radiology and Biomedical Imaging, University of California - San Francisco, San Francisco, CA, USA
| | - Colin Masters
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Jin-Moo Lee
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA; Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA; Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, USA
| | - John C Morris
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA; Knight Alzheimer Disease Research Center, St. Louis, MO, USA
| | - Manu S Goyal
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA; Neuroimaging Labs Research Center, Washington University School of Medicine, St. Louis MO, USA
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Steiner L, Muri R, Wijesinghe D, Jann K, Maissen-Abgottspon S, Radojewski P, Pospieszny K, Kreis R, Kiefer C, Hochuli M, Trepp R, Everts R. Cerebral blood flow and white matter alterations in adults with phenylketonuria. Neuroimage Clin 2023; 41:103550. [PMID: 38091797 PMCID: PMC10716784 DOI: 10.1016/j.nicl.2023.103550] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 11/15/2023] [Accepted: 12/08/2023] [Indexed: 03/16/2024]
Abstract
BACKGROUND Phenylketonuria (PKU) represents a congenital metabolic defect that disrupts the process of converting phenylalanine (Phe) into tyrosine. Earlier investigations have revealed diminished cognitive performance and changes in brain structure and function (including the presence of white matter lesions) among individuals affected by PKU. However, there exists limited understanding regarding cerebral blood flow (CBF) and its potential associations with cognition, white matter lesions, and metabolic parameters in patients with PKU, which we therefore aimed to investigate in this study. METHOD Arterial spin labeling perfusion MRI was performed to measure CBF in 30 adults with early-treated classical PKU (median age 35.5 years) and 59 healthy controls (median age 30.0 years). For all participants, brain Phe levels were measured with 1H spectroscopy, and white matter lesions were rated by two neuroradiologists on T2 weighted images. White matter integrity was examined with diffusion tensor imaging (DTI). For patients only, concurrent plasma Phe levels were assessed after an overnight fasting period. Furthermore, past Phe levels were collected to estimate historical metabolic control. On the day of the MRI, each participant underwent a cognitive assessment measuring IQ and performance in executive functions, attention, and processing speed. RESULTS No significant group difference was observed in global CBF between patients and controls (F (1, 87) = 3.81, p = 0.054). Investigating CBF on the level of cerebral arterial territories, reduced CBF was observed in the left middle and posterior cerebral artery (MCA and PCA), with the most prominent reduction of CBF in the anterior subdivision of the MCA (F (1, 87) = 6.15, p = 0.015, surviving FDR correction). White matter lesions in patients were associated with cerebral blood flow reduction in the affected structure. Particularly, patients with lesions in the occipital lobe showed significant CBF reductions in the left PCA (U = 352, p = 0.013, surviving FDR correction). Additionally, axial diffusivity measured with DTI was positively associated with CBF in the ACA and PCA (surviving FDR correction). Cerebral blood flow did not correlate with cognitive performance or metabolic parameters. CONCLUSION The relationship between cerebral blood flow and white matter indicates a complex interplay between vascular health and white matter alterations in patients with PKU. It highlights the importance of considering a multifactorial model when investigating the impact of PKU on the brain.
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Affiliation(s)
- Leonie Steiner
- Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Inselspital, Bern University Hospital and University of Bern, Switzerland; Division of Neuropaediatrics, Development and Rehabilitation, Department of Paediatrics, Inselspital, Bern University Hospital and University of Bern, Switzerland
| | - Raphaela Muri
- Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Inselspital, Bern University Hospital and University of Bern, Switzerland; Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital and University of Bern, Switzerland; Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland
| | - Dilmini Wijesinghe
- Laboratory of Functional MRI Technology (LOFT), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, USA
| | - Kay Jann
- Laboratory of Functional MRI Technology (LOFT), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, USA
| | - Stephanie Maissen-Abgottspon
- Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Inselspital, Bern University Hospital and University of Bern, Switzerland; Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland
| | - Piotr Radojewski
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital and University of Bern, Switzerland
| | - Katarzyna Pospieszny
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital and University of Bern, Switzerland
| | - Roland Kreis
- Magnetic Resonance Methodology, Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital and University of Bern, Switzerland
| | - Claus Kiefer
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital and University of Bern, Switzerland; Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland
| | - Michel Hochuli
- Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Inselspital, Bern University Hospital and University of Bern, Switzerland
| | - Roman Trepp
- Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Inselspital, Bern University Hospital and University of Bern, Switzerland
| | - Regula Everts
- Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Inselspital, Bern University Hospital and University of Bern, Switzerland; Division of Neuropaediatrics, Development and Rehabilitation, Department of Paediatrics, Inselspital, Bern University Hospital and University of Bern, Switzerland; Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland.
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Lee S, Rieu Z, Kim RE, Lee M, Yen K, Yong J, Kim D. Automatic segmentation of white matter hyperintensities in T2-FLAIR with AQUA: A comparative validation study against conventional methods. Brain Res Bull 2023; 205:110825. [PMID: 38000477 DOI: 10.1016/j.brainresbull.2023.110825] [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: 06/22/2023] [Revised: 11/05/2023] [Accepted: 11/21/2023] [Indexed: 11/26/2023]
Abstract
White matter hyperintensities (WMHs) are lesions in the white matter of the brain that are associated with cognitive decline and an increased risk of dementia. The manual segmentation of WMHs is highly time-consuming and prone to intra- and inter-variability. Therefore, automatic segmentation approaches are gaining attention as a more efficient and objective means to detect and monitor WMHs. In this study, we propose AQUA, a deep learning model designed for fully automatic segmentation of WMHs from T2-FLAIR scans, which improves upon our previous study for small lesion detection and incorporating a multicenter approach. AQUA implements a two-dimensional U-Net architecture and uses patch-based training. Additionally, the network was modified to include Bottleneck Attention Module on each convolutional block of both the encoder and decoder to enhance performance for small-sized WMH. We evaluated the performance and robustness of AQUA by comparing it with five well-known supervised and unsupervised methods for automatic segmentation of WMHs (LGA, LPA, SLS, UBO, and BIANCA). To accomplish this, we tested these six methods on the MICCAI 2017 WMH Segmentation Challenge dataset, which contains MRI images from 170 elderly participants with WMHs of presumed vascular origin, and assessed their robustness across multiple sites and scanner types. The results showed that AQUA achieved superior performance in terms of spatial (Dice = 0.72) and volumetric (logAVD = 0.10) agreement with the manual segmentation compared to the other methods. While the recall and F1-score were moderate at 0.49 and 0.59, respectively, they improved to 0.75 and 0.82 when excluding small lesions (≤ 6 voxels). Remarkably, despite being trained on a different dataset with different ethnic backgrounds, lesion loads, and scanners, AQUA's results were comparable to the top 10 ranked methods of the MICCAI challenge. The findings suggest that AQUA is effective and practical for automatic segmentation of WMHs from T2-FLAIR scans, which could help identify individuals at risk of cognitive decline and dementia and allow for early intervention and management.
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Affiliation(s)
- Soojin Lee
- Research Institute, NEUROPHET Inc., Seoul, South Korea; Pacific Parkinson's Research Centre, The University of British Columbia, Vancouver, Canada.
| | - ZunHyan Rieu
- Research Institute, NEUROPHET Inc., Seoul, South Korea
| | - Regina Ey Kim
- Research Institute, NEUROPHET Inc., Seoul, South Korea
| | - Minho Lee
- Research Institute, NEUROPHET Inc., Seoul, South Korea
| | - Kevin Yen
- Division of Neurology, Department of Medicine, University of Alberta, Edmonton, Canada
| | - Junghyun Yong
- Research Institute, NEUROPHET Inc., Seoul, South Korea
| | - Donghyeon Kim
- Research Institute, NEUROPHET Inc., Seoul, South Korea
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Malani SN, Acharya S, Shukla S. Current and Future Developments in Imaging and Treatment of White Matter Disease: A Systematic Review. Cureus 2023; 15:e51030. [PMID: 38264375 PMCID: PMC10804206 DOI: 10.7759/cureus.51030] [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: 07/03/2023] [Accepted: 12/24/2023] [Indexed: 01/25/2024] Open
Abstract
The elderly often suffer from "mild" dementia due to white matter disease, which is another name for repeated brain infarctions. The degeneration of white matter, which links various parts of the brain to the spinal cord, is the root cause of this disorder, which develops with age. Dementia, imbalance, and movement problems are symptoms of this degenerative disease that worsen with age. This research's goal is to study current therapy options and identify methods for early diagnosis of white matter illness. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement for meta-analyses and systematic reviews served as the basis for our literature review. Results from the search in ScienceDirect and Medline/Pubmed led to the finalization of 33 studies. The complex relationship between white matter hyperintensities (WMHs) and neurological disorders is the subject of this comprehensive review, which sheds light on the varied terrain of WMH studies by highlighting their consequences and developing evaluation techniques.
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Affiliation(s)
- Sagar N Malani
- Department of Medicine, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research (Deemed to Be University), Wardha, IND
| | - Sourya Acharya
- Department of Medicine, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research (Deemed to Be University), Wardha, IND
| | - Samarth Shukla
- Department of Medicine, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research (Deemed to Be University), Wardha, IND
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Gentile G, Jenkinson M, Griffanti L, Luchetti L, Leoncini M, Inderyas M, Mortilla M, Cortese R, De Stefano N, Battaglini M. BIANCA-MS: An optimized tool for automated multiple sclerosis lesion segmentation. Hum Brain Mapp 2023; 44:4893-4913. [PMID: 37530598 PMCID: PMC10472913 DOI: 10.1002/hbm.26424] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 05/20/2023] [Accepted: 07/06/2023] [Indexed: 08/03/2023] Open
Abstract
In this work we present BIANCA-MS, a novel tool for brain white matter lesion segmentation in multiple sclerosis (MS), able to generalize across both the wide spectrum of MRI acquisition protocols and the heterogeneity of manually labeled data. BIANCA-MS is based on the original version of BIANCA and implements two innovative elements: a harmonized setting, tested under different MRI protocols, which avoids the need to further tune algorithm parameters to each dataset; and a cleaning step developed to improve consistency in automated and manual segmentations, thus reducing unwanted variability in output segmentations and validation data. BIANCA-MS was tested on three datasets, acquired with different MRI protocols. First, we compared BIANCA-MS to other widely used tools. Second, we tested how BIANCA-MS performs in separate datasets. Finally, we evaluated BIANCA-MS performance on a pooled dataset where all MRI data were merged. We calculated the overlap using the DICE spatial similarity index (SI) as well as the number of false positive/negative clusters (nFPC/nFNC) in comparison to the manual masks processed with the cleaning step. BIANCA-MS clearly outperformed other available tools in both high- and low-resolution images and provided comparable performance across different scanning protocols, sets of modalities and image resolutions. BIANCA-MS performance on the pooled dataset (SI: 0.72 ± 0.25, nFPC: 13 ± 11, nFNC: 4 ± 8) were comparable to those achieved on each individual dataset (median across datasets SI: 0.72 ± 0.28, nFPC: 14 ± 11, nFNC: 4 ± 8). Our findings suggest that BIANCA-MS is a robust and accurate approach for automated MS lesion segmentation.
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Affiliation(s)
- Giordano Gentile
- Department of Medicine, Surgery and NeuroscienceUniversity of SienaSienaItaly
- SIENA Imaging SRLSienaItaly
| | - Mark Jenkinson
- Welcome Centre for Integrative Neuroimaging (WIN), FMRIB, Nuffield Department of Clinical NeurosciencesUniversity of Oxford, John Radcliffe HospitalOxfordUK
- Australian Institute of Machine Learning (AIML), School of Computer and Mathematical SciencesUniversity of AdelaideAdelaideSouth AustraliaAustralia
- South Australian Health and Medical Research Institute (SAHMRI)AdelaideSouth AustraliaAustralia
| | - Ludovica Griffanti
- Welcome Centre for Integrative Neuroimaging (WIN), FMRIB, Nuffield Department of Clinical NeurosciencesUniversity of Oxford, John Radcliffe HospitalOxfordUK
- Welcome Centre for Integrative Neuroimaging (WIN), OHBA, Department of PsychiatryUniversity of Oxford, Warneford HospitalOxfordUK
| | - Ludovico Luchetti
- Department of Medicine, Surgery and NeuroscienceUniversity of SienaSienaItaly
| | - Matteo Leoncini
- Department of Medicine, Surgery and NeuroscienceUniversity of SienaSienaItaly
- SIENA Imaging SRLSienaItaly
| | - Maira Inderyas
- Department of Medicine, Surgery and NeuroscienceUniversity of SienaSienaItaly
- SIENA Imaging SRLSienaItaly
| | | | - Rosa Cortese
- Department of Medicine, Surgery and NeuroscienceUniversity of SienaSienaItaly
| | - Nicola De Stefano
- Department of Medicine, Surgery and NeuroscienceUniversity of SienaSienaItaly
| | - Marco Battaglini
- Department of Medicine, Surgery and NeuroscienceUniversity of SienaSienaItaly
- SIENA Imaging SRLSienaItaly
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Zhen T, Fang J, Hu D, Ruan M, Wang L, Fan S, Shen Q. Risk stratification by nomogram of deep learning radiomics based on multiparametric magnetic resonance imaging in knee meniscus injury. INTERNATIONAL ORTHOPAEDICS 2023; 47:2497-2505. [PMID: 37386277 DOI: 10.1007/s00264-023-05875-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Accepted: 06/19/2023] [Indexed: 07/01/2023]
Abstract
PURPOSE To construct and validate a nomogram model that integrated deep learning radiomic features based on multiparametric MRI and clinical features for risk stratification of meniscus injury. METHODS A total of 167 knee MR images were collected from two institutions. All patients were classified into two groups based on the MR diagnostic criteria proposed by Stoller et al. The automatic meniscus segmentation model was constructed through V-net. LASSO regression was performed to extract the optimal features correlated to risk stratification. A nomogram model was constructed by combining the Radscore and clinical features. The performance of the models was evaluated by ROC analysis and calibration curve. Subsequently, the model was simulated by junior doctors in order to test its practical application effect. RESULTS The Dice similarity coefficients of automatic meniscus segmentation models were all over 0.8. Eight optimal features, identified by LASSO regression, were employed to calculate the Radscore. The combined model showed a better performance in both the training cohort (AUC = 0.90, 95%CI: 0.84-0.95) and the validation cohort (AUC = 0.84, 95%CI: 0.72-0.93). The calibration curve indicated a better accuracy of the combined model than either the Radscore or clinical model alone. The simulation results showed that the diagnostic accuracy of junior doctors increased from 74.9 to 86.2% after using the model. CONCLUSION Deep learning V-net demonstrated great performance in automatic meniscus segmentation of the knee joint. It was reliable for stratifying the risk of meniscus injury of the knee by nomogram which integrated the Radscores and clinical features.
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Affiliation(s)
- Tao Zhen
- Department of Radiology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, No. 261, Huansha Road, Zhejiang, 310006, Hangzhou, China
| | - Jing Fang
- Zhejiang Provincial Hospital of Chinese Medicine, Hangzhou, 310006, China
| | - Dacheng Hu
- Department of Radiology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, No. 261, Huansha Road, Zhejiang, 310006, Hangzhou, China
| | - Mei Ruan
- Department of Radiology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, No. 261, Huansha Road, Zhejiang, 310006, Hangzhou, China
| | - Luoyu Wang
- Department of Radiology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, No. 261, Huansha Road, Zhejiang, 310006, Hangzhou, China
| | - Sandra Fan
- Department of Radiology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, No. 261, Huansha Road, Zhejiang, 310006, Hangzhou, China
| | - Qijun Shen
- Department of Radiology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, No. 261, Huansha Road, Zhejiang, 310006, Hangzhou, China.
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11
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Duering M, Biessels GJ, Brodtmann A, Chen C, Cordonnier C, de Leeuw FE, Debette S, Frayne R, Jouvent E, Rost NS, Ter Telgte A, Al-Shahi Salman R, Backes WH, Bae HJ, Brown R, Chabriat H, De Luca A, deCarli C, Dewenter A, Doubal FN, Ewers M, Field TS, Ganesh A, Greenberg S, Helmer KG, Hilal S, Jochems ACC, Jokinen H, Kuijf H, Lam BYK, Lebenberg J, MacIntosh BJ, Maillard P, Mok VCT, Pantoni L, Rudilosso S, Satizabal CL, Schirmer MD, Schmidt R, Smith C, Staals J, Thrippleton MJ, van Veluw SJ, Vemuri P, Wang Y, Werring D, Zedde M, Akinyemi RO, Del Brutto OH, Markus HS, Zhu YC, Smith EE, Dichgans M, Wardlaw JM. Neuroimaging standards for research into small vessel disease-advances since 2013. Lancet Neurol 2023; 22:602-618. [PMID: 37236211 DOI: 10.1016/s1474-4422(23)00131-x] [Citation(s) in RCA: 80] [Impact Index Per Article: 80.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 03/03/2023] [Accepted: 03/28/2023] [Indexed: 05/28/2023]
Abstract
Cerebral small vessel disease (SVD) is common during ageing and can present as stroke, cognitive decline, neurobehavioural symptoms, or functional impairment. SVD frequently coexists with neurodegenerative disease, and can exacerbate cognitive and other symptoms and affect activities of daily living. Standards for Reporting Vascular Changes on Neuroimaging 1 (STRIVE-1) categorised and standardised the diverse features of SVD that are visible on structural MRI. Since then, new information on these established SVD markers and novel MRI sequences and imaging features have emerged. As the effect of combined SVD imaging features becomes clearer, a key role for quantitative imaging biomarkers to determine sub-visible tissue damage, subtle abnormalities visible at high-field strength MRI, and lesion-symptom patterns, is also apparent. Together with rapidly emerging machine learning methods, these metrics can more comprehensively capture the effect of SVD on the brain than the structural MRI features alone and serve as intermediary outcomes in clinical trials and future routine practice. Using a similar approach to that adopted in STRIVE-1, we updated the guidance on neuroimaging of vascular changes in studies of ageing and neurodegeneration to create STRIVE-2.
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Affiliation(s)
- Marco Duering
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany; Medical Image Analysis Center, University of Basel, Basel, Switzerland; Department of Biomedical Engineering, University of Basel, Basel, Switzerland.
| | - Geert Jan Biessels
- Department of Neurology, University Medical Center Utrecht, Utrecht, Netherlands
| | - Amy Brodtmann
- Cognitive Health Initiative, Central Clinical School, Monash University, Melbourne, VIC, Australia; Florey Institute of Neuroscience and Mental Health, University of Melbourne, Melbourne, VIC, Australia
| | - Christopher Chen
- Department of Pharmacology, Memory Aging and Cognition Centre, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Psychological Medicine, Memory Aging and Cognition Centre, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Charlotte Cordonnier
- Université de Lille, INSERM, CHU Lille, U1172-Lille Neuroscience and Cognition (LilNCog), Lille, France
| | - Frank-Erik de Leeuw
- Department of Neurology, Donders Center for Medical Neuroscience, Radboudumc, Nijmegen, Netherlands
| | - Stéphanie Debette
- Bordeaux Population Health Research Center, University of Bordeaux, INSERM, UMR 1219, Bordeaux, France; Department of Neurology, Institute for Neurodegenerative Diseases, CHU de Bordeaux, Bordeaux, France
| | - Richard Frayne
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada; Department of Radiology, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada; Seaman Family MR Research Centre, Foothills Medical Centre, University of Calgary, Calgary, AB, Canada
| | - Eric Jouvent
- AP-HP, Lariboisière Hospital, Translational Neurovascular Centre, FHU NeuroVasc, Université Paris Cité, Paris, France; Université Paris Cité, INSERM UMR 1141, NeuroDiderot, Paris, France
| | - Natalia S Rost
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | | | | | - Walter H Backes
- School for Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, Netherlands; School for Cardiovascular Diseases, Maastricht University Medical Center, Maastricht, Netherlands; Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, Netherlands
| | - Hee-Joon Bae
- Department of Neurology, Seoul National University College of Medicine, Seoul, South Korea; Cerebrovascular Disease Center, Seoul National University Bundang Hospital, Seongn-si, South Korea
| | - Rosalind Brown
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK; UK Dementia Research Institute, University of Edinburgh, Edinburgh, UK
| | - Hugues Chabriat
- Centre Neurovasculaire Translationnel, CERVCO, INSERM U1141, FHU NeuroVasc, Université Paris Cité, Paris, France
| | - Alberto De Luca
- Image Sciences Institute, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, Netherlands
| | - Charles deCarli
- Department of Neurology and Center for Neuroscience, University of California, Davis, CA, USA
| | - Anna Dewenter
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Fergus N Doubal
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Michael Ewers
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Thalia S Field
- Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada; Vancouver Stroke Program, Division of Neurology, University of British Columbia, Vancouver, BC, Canada
| | - Aravind Ganesh
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada; Department of Community Health Sciences, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada; Mathison Centre for Mental Health Research and Education, University of Calgary, Calgary, AB, Canada
| | - Steven Greenberg
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Karl G Helmer
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA; Athinoula A Martinos Center for Biomedical Imaging, Boston, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Saima Hilal
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore
| | - Angela C C Jochems
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK; UK Dementia Research Institute, University of Edinburgh, Edinburgh, UK
| | - Hanna Jokinen
- Division of Neuropsychology, HUS Neurocenter, Helsinki University Hospital, University of Helsinki, Helsinki, Finland; Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Hugo Kuijf
- Image Sciences Institute, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, Netherlands
| | - Bonnie Y K Lam
- Division of Neurology, Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Gerald Choa Neuroscience Institute, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Margaret KL Cheung Research Centre for Management of Parkinsonism, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Therese Pei Fong Chow Research Centre for Prevention of Dementia, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Lui Che Woo Institute of Innovative Medicine, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Li Ka Shing Institute of Health Science, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Lau Tat-chuen Research Centre of Brain Degenerative Diseases in Chinese, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Jessica Lebenberg
- AP-HP, Lariboisière Hospital, Translational Neurovascular Centre, FHU NeuroVasc, Université Paris Cité, Paris, France; Université Paris Cité, INSERM UMR 1141, NeuroDiderot, Paris, France
| | - Bradley J MacIntosh
- Sandra E Black Centre for Brain Resilience and Repair, Hurvitz Brain Sciences, Physical Sciences Platform, Sunnybrook Research Institute, Toronto, ON, Canada; Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada; Computational Radiology and Artificial Intelligence Unit, Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
| | - Pauline Maillard
- Department of Neurology and Center for Neuroscience, University of California, Davis, CA, USA
| | - Vincent C T Mok
- Division of Neurology, Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Gerald Choa Neuroscience Institute, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Margaret KL Cheung Research Centre for Management of Parkinsonism, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Therese Pei Fong Chow Research Centre for Prevention of Dementia, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Lui Che Woo Institute of Innovative Medicine, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Li Ka Shing Institute of Health Science, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Lau Tat-chuen Research Centre of Brain Degenerative Diseases in Chinese, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Leonardo Pantoni
- Department of Biomedical and Clinical Science, University of Milan, Milan, Italy
| | - Salvatore Rudilosso
- Comprehensive Stroke Center, Department of Neuroscience, Hospital Clinic and August Pi i Sunyer Biomedical Research Institute (IDIBAPS), Barcelona, Spain
| | - Claudia L Satizabal
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA; Department of Population Health Sciences, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA; Department of Neurology, Boston University Medical Center, Boston, MA, USA; Framingham Heart Study, Framingham, MA, USA
| | - Markus D Schirmer
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | | | - Colin Smith
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Julie Staals
- School for Cardiovascular Diseases, Maastricht University Medical Center, Maastricht, Netherlands; Department of Neurology, Maastricht University Medical Center, Maastricht, Netherlands
| | - Michael J Thrippleton
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK; Edinburgh Imaging and Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | | | | | - Yilong Wang
- Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - David Werring
- Stroke Research Centre, UCL Queen Square Institute of Neurology, London, UK
| | - Marialuisa Zedde
- Neurology Unit, Stroke Unit, Department of Neuromotor Physiology and Rehabilitation, Azienda Unità Sanitaria-IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Rufus O Akinyemi
- Neuroscience and Ageing Research Unit, Institute for Advanced Medical Research and Training, College of Medicine, University of Ibadan, Ibadan, Nigeria
| | - Oscar H Del Brutto
- School of Medicine and Research Center, Universidad de Especialidades Espiritu Santo, Ecuador
| | - Hugh S Markus
- Stroke Research Group, Department of Clinical Neuroscience, University of Cambridge, Cambridge, UK
| | - Yi-Cheng Zhu
- Department of Neurology, Peking Union Medical College Hospital, Beijing, China
| | - Eric E Smith
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada; Department of Community Health Sciences, University of Calgary, Calgary, AB, Canada; Department of Radiology, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Martin Dichgans
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany; Munich Cluster for Systems Neurology (SyNergy), Munich, Germany; German Center for Neurodegenerative Diseases (DZNE), Munich, Germany; German Centre for Cardiovascular Research (DZHK), Munich, Germany
| | - Joanna M Wardlaw
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK; UK Dementia Research Institute, University of Edinburgh, Edinburgh, UK.
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12
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Ferris JK, Lo BP, Khlif MS, Brodtmann A, Boyd LA, Liew SL. Optimizing automated white matter hyperintensity segmentation in individuals with stroke. FRONTIERS IN NEUROIMAGING 2023; 2:1099301. [PMID: 37554631 PMCID: PMC10406248 DOI: 10.3389/fnimg.2023.1099301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 02/15/2023] [Indexed: 08/10/2023]
Abstract
White matter hyperintensities (WMHs) are a risk factor for stroke. Consequently, many individuals who suffer a stroke have comorbid WMHs. The impact of WMHs on stroke recovery is an active area of research. Automated WMH segmentation methods are often employed as they require minimal user input and reduce risk of rater bias; however, these automated methods have not been specifically validated for use in individuals with stroke. Here, we present methodological validation of automated WMH segmentation methods in individuals with stroke. We first optimized parameters for FSL's publicly available WMH segmentation software BIANCA in two independent (multi-site) datasets. Our optimized BIANCA protocol achieved good performance within each independent dataset, when the BIANCA model was trained and tested in the same dataset or trained on mixed-sample data. BIANCA segmentation failed when generalizing a trained model to a new testing dataset. We therefore contrasted BIANCA's performance with SAMSEG, an unsupervised WMH segmentation tool available through FreeSurfer. SAMSEG does not require prior WMH masks for model training and was more robust to handling multi-site data. However, SAMSEG performance was slightly lower than BIANCA when data from a single site were tested. This manuscript will serve as a guide for the development and utilization of WMH analysis pipelines for individuals with stroke.
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Affiliation(s)
- Jennifer K. Ferris
- Graduate Program in Rehabilitation Sciences, University of British Columbia, Vancouver, BC, Canada
- Gerontology Research Centre, Simon Fraser University, Vancouver, BC, Canada
| | - Bethany P. Lo
- Chan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA, United States
| | - Mohamed Salah Khlif
- Cognitive Health Initiative, Central Clinical School, Monash University, Melbourne, VIC, Australia
| | - Amy Brodtmann
- Cognitive Health Initiative, Central Clinical School, Monash University, Melbourne, VIC, Australia
- Department of Medicine, Royal Melbourne Hospital, Melbourne, VIC, Australia
| | - Lara A. Boyd
- Graduate Program in Rehabilitation Sciences, University of British Columbia, Vancouver, BC, Canada
- Department of Physical Therapy, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Sook-Lei Liew
- Chan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA, United States
- Department of Neurology, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
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13
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Genetic and Environmental Effects on the Development of White Matter Hyperintensities in a Middle Age Twin Population. Medicina (B Aires) 2022; 58:medicina58101425. [PMID: 36295585 PMCID: PMC9612298 DOI: 10.3390/medicina58101425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 09/27/2022] [Accepted: 10/05/2022] [Indexed: 11/17/2022] Open
Abstract
Introduction: White matter hyperintensities (WMH) indicate white matter brain lesions in magnetic resonance imaging (MRI), which can be used as a marker for brain aging and cerebrovascular and neurodegenerative disorders. Twin studies revealed substantial but not uniform WMH heritability in elderly twins. The objective of our study was to investigate the genetic and environmental components of WMH, as well as their importance in a healthy twin population, utilizing 3T MRI scanners in a middle-aged twin population. Methods: Brain MRI was performed on 120 healthy adult twins from the Hungarian Twin Registry on a 3T scanner (86 monozygotic, MZ and 34 dizygotic, DZ twins; median age 50 ± 26.5 years, 72.5% female and 27.5% male). The count of WMH on FLAIR images was calculated using an automated volumetry pipeline (volBrain) and human processing. The age- and sex-adjusted MZ and DZ intra-pair correlations were determined and the total variance was decomposed into genetic, shared and unique environmental components using structural equation modeling. Results: Age and sex-adjusted MZ intrapair correlations were higher than DZ correlations, indicating moderate genetic influence in each lesion (rMZ = 0.466, rDZ = −0.025 for total count; rMZ = 0.482, rDZ = 0.093 for deep white matter count; rMZ = 0.739, rDZ = 0.39 for infratentorial count; rMZ = 0.573, rDZ = 0.372 for cerebellar count and rMZ = 0.473, rDZ = 0.19 for periventricular count), indicating a moderate heritability (A = 40.3%, A = 45%, A = 72.7% and A = 55.5%and 47.2%, respectively). The rest of the variance was influenced by unique environmental effects (E between 27.3% and 59.7%, respectively). Conclusions: The number of WMH lesions is moderately influenced by genetic effects, particularly in the infratentorial region in middle-aged twins. These results suggest that the distribution of WMH in various brain regions is heterogeneous.
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Joo L, Shim WH, Suh CH, Lim SJ, Heo H, Kim WS, Hong E, Lee D, Sung J, Lim JS, Lee JH, Kim SJ. Diagnostic performance of deep learning-based automatic white matter hyperintensity segmentation for classification of the Fazekas scale and differentiation of subcortical vascular dementia. PLoS One 2022; 17:e0274562. [PMID: 36107961 PMCID: PMC9477348 DOI: 10.1371/journal.pone.0274562] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 08/31/2022] [Indexed: 11/26/2022] Open
Abstract
Purpose To validate the diagnostic performance of commercially available, deep learning-based automatic white matter hyperintensity (WMH) segmentation algorithm for classifying the grades of the Fazekas scale and differentiating subcortical vascular dementia. Methods This retrospective, observational, single-institution study investigated the diagnostic performance of a deep learning-based automatic WMH volume segmentation to classify the grades of the Fazekas scale and differentiate subcortical vascular dementia. The VUNO Med-DeepBrain was used for the WMH segmentation system. The system for segmentation of WMH was designed with convolutional neural networks, in which the input image was comprised of a pre-processed axial FLAIR image, and the output was a segmented WMH mask and its volume. Patients presented with memory complaint between March 2017 and June 2018 were included and were split into training (March 2017–March 2018, n = 596) and internal validation test set (April 2018–June 2018, n = 204). Results Optimal cut-off values to categorize WMH volume as normal vs. mild/moderate/severe, normal/mild vs. moderate/severe, and normal/mild/moderate vs. severe were 3.4 mL, 9.6 mL, and 17.1 mL, respectively, and the AUC were 0.921, 0.956 and 0.960, respectively. When differentiating normal/mild vs. moderate/severe using WMH volume in the test set, sensitivity, specificity, and accuracy were 96.4%, 89.9%, and 91.7%, respectively. For distinguishing subcortical vascular dementia from others using WMH volume, sensitivity, specificity, and accuracy were 83.3%, 84.3%, and 84.3%, respectively. Conclusion Deep learning-based automatic WMH segmentation may be an accurate and promising method for classifying the grades of the Fazekas scale and differentiating subcortical vascular dementia.
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Affiliation(s)
- Leehi Joo
- Department of Radiology, Korea University Guro Hospital, Seoul, Republic of Korea
| | - Woo Hyun Shim
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Chong Hyun Suh
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
- * E-mail:
| | - Su Jin Lim
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Hwon Heo
- Department of Convergence Medicine, Asan Medical Center, University of Ulsan College of Medicine, Ulsan, Republic of Korea
| | - Woo Seok Kim
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | | | | | | | - Jae-Sung Lim
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Jae-Hong Lee
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Sang Joon Kim
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
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15
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Martinez-Millana A, Saez-Saez A, Tornero-Costa R, Azzopardi-Muscat N, Traver V, Novillo-Ortiz D. Artificial intelligence and its impact on the domains of universal health coverage, health emergencies and health promotion: An overview of systematic reviews. Int J Med Inform 2022; 166:104855. [PMID: 35998421 PMCID: PMC9551134 DOI: 10.1016/j.ijmedinf.2022.104855] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 08/01/2022] [Accepted: 08/11/2022] [Indexed: 12/04/2022]
Abstract
An overview of systematic reviews on the application of AI including 129 studies. AI use is prominent in Universal Health Coverage, featuring image analysis in neoplasms. Half of the reviews did not evaluate validation procedures nor reporting guidelines. Risk of bias was only included un a third of the reviews. There is not sufficient evidence to transfer AI to actual healthcare delivery.
Background Artificial intelligence is fueling a new revolution in medicine and in the healthcare sector. Despite the growing evidence on the benefits of artificial intelligence there are several aspects that limit the measure of its impact in people’s health. It is necessary to assess the current status on the application of AI towards the improvement of people’s health in the domains defined by WHO’s Thirteenth General Programme of Work (GPW13) and the European Programme of Work (EPW), to inform about trends, gaps, opportunities, and challenges. Objective To perform a systematic overview of systematic reviews on the application of artificial intelligence in the people’s health domains as defined in the GPW13 and provide a comprehensive and updated map on the application specialties of artificial intelligence in terms of methodologies, algorithms, data sources, outcomes, predictors, performance, and methodological quality. Methods A systematic search in MEDLINE, EMBASE, Cochrane and IEEEXplore was conducted between January 2015 and June 2021 to collect systematic reviews using a combination of keywords related to the domains of universal health coverage, health emergencies protection, and better health and wellbeing as defined by the WHO’s PGW13 and EPW. Eligibility criteria was based on methodological quality and the inclusion of practical implementation of artificial intelligence. Records were classified and labeled using ICD-11 categories into the domains of the GPW13. Descriptors related to the area of implementation, type of modeling, data entities, outcomes and implementation on care delivery were extracted using a structured form and methodological aspects of the included reviews studies was assessed using the AMSTAR checklist. Results The search strategy resulted in the screening of 815 systematic reviews from which 203 were assessed for eligibility and 129 were included in the review. The most predominant domain for artificial intelligence applications was Universal Health Coverage (N = 98) followed by Health Emergencies (N = 16) and Better Health and Wellbeing (N = 15). Neoplasms area on Universal Health Coverage was the disease area featuring most of the applications (21.7 %, N = 28). The reviews featured analytics primarily over both public and private data sources (67.44 %, N = 87). The most used type of data was medical imaging (31.8 %, N = 41) and predictors based on regions of interest and clinical data. The most prominent subdomain of Artificial Intelligence was Machine Learning (43.4 %, N = 56), in which Support Vector Machine method was predominant (20.9 %, N = 27). Regarding the purpose, the application of Artificial Intelligence I is focused on the prediction of the diseases (36.4 %, N = 47). With respect to the validation, more than a half of the reviews (54.3 %, N = 70) did not report a validation procedure and, whenever available, the main performance indicator was the accuracy (28.7 %, N = 37). According to the methodological quality assessment, a third of the reviews (34.9 %, N = 45) implemented methods for analysis the risk of bias and the overall AMSTAR score below was 5 (4.01 ± 1.93) on all the included systematic reviews. Conclusion Artificial intelligence is being used for disease modelling, diagnose, classification and prediction in the three domains of GPW13. However, the evidence is often limited to laboratory and the level of adoption is largely unbalanced between ICD-11 categoriesand diseases. Data availability is a determinant factor on the developmental stage of artificial intelligence applications. Most of the reviewed studies show a poor methodological quality and are at high risk of bias, which limits the reproducibility of the results and the reliability of translating these applications to real clinical scenarios. The analyzed papers show results only in laboratory and testing scenarios and not in clinical trials nor case studies, limiting the supporting evidence to transfer artificial intelligence to actual care delivery.
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Affiliation(s)
- Antonio Martinez-Millana
- Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Camino de Vera S/N, Valencia 46022, Spain
| | - Aida Saez-Saez
- Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Camino de Vera S/N, Valencia 46022, Spain
| | - Roberto Tornero-Costa
- Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Camino de Vera S/N, Valencia 46022, Spain
| | - Natasha Azzopardi-Muscat
- Division of Country Health Policies and Systems, World Health Organization, Regional Office for Europe, Copenhagen, Denmark
| | - Vicente Traver
- Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Camino de Vera S/N, Valencia 46022, Spain
| | - David Novillo-Ortiz
- Division of Country Health Policies and Systems, World Health Organization, Regional Office for Europe, Copenhagen, Denmark.
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16
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Jiang J, Wang D, Song Y, Sachdev PS, Wen W. Computer-Aided Extraction of Select MRI Markers of Cerebral Small Vessel Disease: A Systematic Review. Neuroimage 2022; 261:119528. [PMID: 35914668 DOI: 10.1016/j.neuroimage.2022.119528] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 07/18/2022] [Accepted: 07/28/2022] [Indexed: 11/30/2022] Open
Abstract
Cerebral small vessel disease (CSVD) is a major vascular contributor to cognitive impairment in ageing, including dementias. Imaging remains the most promising method for in vivo studies of CSVD. To replace the subjective and laborious visual rating approaches, emerging studies have applied state-of-the-art artificial intelligence to extract imaging biomarkers of CSVD from MRI scans. We aimed to summarise published computer-aided methods for the examination of three imaging biomarkers of CSVD, namely cerebral microbleeds (CMB), dilated perivascular spaces (PVS), and lacunes of presumed vascular origin. Seventy classical image processing, classical machine learning, and deep learning studies were identified. Transfer learning and weak supervision techniques have been applied to accommodate the limitations in the training data. While good performance metrics were achieved in local datasets, there have not been generalisable pipelines validated in different research and/or clinical cohorts. Future studies could consider pooling data from multiple sources to increase data size and diversity, and evaluating performance using both image processing metrics and associations with clinical measures.
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Affiliation(s)
- Jiyang Jiang
- Centre for Healthy Brain Ageing, Discipline of Psychiatry and Mental Health, School of Clinical Medicine, Faculty of Medicine, University of New South Wales, NSW 2052, Australia.
| | - Dadong Wang
- Quantitative Imaging Research Team, Data61, CSIRO, Marsfield, NSW 2122, Australia
| | - Yang Song
- School of Computer Science and Engineering, University of New South Wales, NSW 2052, Australia
| | - Perminder S Sachdev
- Centre for Healthy Brain Ageing, Discipline of Psychiatry and Mental Health, School of Clinical Medicine, Faculty of Medicine, University of New South Wales, NSW 2052, Australia; Neuropsychiatric Institute, Prince of Wales Hospital, Randwick, NSW 2031, Australia
| | - Wei Wen
- Centre for Healthy Brain Ageing, Discipline of Psychiatry and Mental Health, School of Clinical Medicine, Faculty of Medicine, University of New South Wales, NSW 2052, Australia; Neuropsychiatric Institute, Prince of Wales Hospital, Randwick, NSW 2031, Australia
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17
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Thyreau B, Tatewaki Y, Chen L, Takano Y, Hirabayashi N, Furuta Y, Hata J, Nakaji S, Maeda T, Noguchi‐Shinohara M, Mimura M, Nakashima K, Mori T, Takebayashi M, Ninomiya T, Taki Y. Higher-resolution quantification of white matter hypointensities by large-scale transfer learning from 2D images on the JPSC-AD cohort. Hum Brain Mapp 2022; 43:3998-4012. [PMID: 35524684 PMCID: PMC9374893 DOI: 10.1002/hbm.25899] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 03/24/2022] [Accepted: 04/20/2022] [Indexed: 12/14/2022] Open
Abstract
White matter lesions (WML) commonly occur in older brains and are quantifiable on MRI, often used as a biomarker in Aging research. Although algorithms are regularly proposed that identify these lesions from T2‐fluid‐attenuated inversion recovery (FLAIR) sequences, none so far can estimate lesions directly from T1‐weighted images with acceptable accuracy. Since 3D T1 is a polyvalent and higher‐resolution sequence, it could be beneficial to obtain the distribution of WML directly from it. However a serious difficulty, both for algorithms and human, can be found in the ambiguities of brain signal intensity in T1 images. This manuscript shows that a cross‐domain ConvNet (Convolutional Neural Network) approach can help solve this problem. Still, this is non‐trivial, as it would appear to require a large and varied dataset (for robustness) labelled at the same high resolution (for spatial accuracy). Instead, our model was taught from two‐dimensional FLAIR images with a loss function designed to handle the super‐resolution need. And crucially, we leveraged a very large training set for this task, the recently assembled, multi‐sites Japan Prospective Studies Collaboration for Aging and Dementia (JPSC‐AD) cohort. We describe the two‐step procedure that we followed to handle such a large number of imperfectly labeled samples. A large‐scale accuracy evaluation conducted against FreeSurfer 7, and a further visual expert rating revealed that WML segmentation from our ConvNet was consistently better. Finally, we made a directly usable software program based on that trained ConvNet model, available at https://github.com/bthyreau/deep-T1-WMH.
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Affiliation(s)
- Benjamin Thyreau
- Smart‐Aging Research Center, Institute of Development, Aging, and CancerTohoku UniversitySendaiJapan
| | - Yasuko Tatewaki
- Department of Aging Research and Geriatric Medicine, Institute of Development, Aging, and CancerTohoku UniversitySendaiJapan
- Department of Geriatric Medicine and NeuroimagingTohoku University HospitalSendaiJapan
| | - Liying Chen
- Smart‐Aging Research Center, Institute of Development, Aging, and CancerTohoku UniversitySendaiJapan
| | - Yuji Takano
- Smart‐Aging Research Center, Institute of Development, Aging, and CancerTohoku UniversitySendaiJapan
- Department of Psychological SciencesUniversity of Human EnvironmentsMatsuyamaJapan
| | - Naoki Hirabayashi
- Department of Epidemiology and Public Health, Graduate School of Medical SciencesKyushu UniversityFukuokaJapan
| | - Yoshihiko Furuta
- Department of Epidemiology and Public Health, Graduate School of Medical SciencesKyushu UniversityFukuokaJapan
| | - Jun Hata
- Department of Epidemiology and Public Health, Graduate School of Medical SciencesKyushu UniversityFukuokaJapan
| | - Shigeyuki Nakaji
- Department of Social Medicine, Graduate School of MedicineHirosaki UniversityHirosakiJapan
| | - Tetsuya Maeda
- Division of Neurology and Gerontology, Department of Internal Medicine, School of MedicineIwate Medical UniversityIwateJapan
| | - Moeko Noguchi‐Shinohara
- Department of Neurology and Neurobiology of Aging, Kanazawa University Graduate School of Medical SciencesKanazawa UniversityKanazawaJapan
| | | | - Kenji Nakashima
- National Hospital Organization, Matsue Medical CenterShimaneJapan
| | - Takaaki Mori
- Department of Neuropsychiatry, Ehime University Graduate School of MedicineEhime UniversityEhimeJapan
| | - Minoru Takebayashi
- Faculty of Life Sciences, Department of NeuropsychiatryKumamoto UniversityKumamotoJapan
| | - Toshiharu Ninomiya
- Department of Epidemiology and Public Health, Graduate School of Medical SciencesKyushu UniversityFukuokaJapan
| | - Yasuyuki Taki
- Smart‐Aging Research Center, Institute of Development, Aging, and CancerTohoku UniversitySendaiJapan
- Department of Aging Research and Geriatric Medicine, Institute of Development, Aging, and CancerTohoku UniversitySendaiJapan
- Department of Geriatric Medicine and NeuroimagingTohoku University HospitalSendaiJapan
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18
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Zhao Y, Zhao J, Gu Y, Chen B, Guo J, Xie J, Yan Q, Ma Y, Wu Y, Zhang J, Lu Q, Liu J. Outer Retinal Layer Thickness Changes in White Matter Hyperintensity and Parkinson's Disease. Front Neurosci 2021; 15:741651. [PMID: 34594186 PMCID: PMC8477009 DOI: 10.3389/fnins.2021.741651] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Accepted: 08/18/2021] [Indexed: 12/02/2022] Open
Abstract
Purpose: To investigate the thickness changes of outer retinal layers in subjects with white matter hyperintensities (WMH) and Parkinson's Disease (PD). Methods: 56 eyes from 31 patients with WMH, 11 eyes from 6 PD patients, and 58 eyes from 32 healthy controls (HC) were enrolled in this study. A macular-centered scan was conducted on each participant using a spectral-domain optical coherence tomography (SD-OCT) device. After speckle noise reduction, a state-of-the-art deep learning method (i.e., a context encoder network) was employed to segment the outer retinal layers from OCT B-scans. Thickness quantification of the outer retinal layers was conducted on the basis of the segmentation results. Results: WMH patients had significantly thinner Henle fiber layers, outer nuclear layers (HFL+ONL) and photoreceptor outer segments (OS) than HC (p = 0.031, and p = 0.005), while PD patients showed a significant increase of mean thickness in the interdigitation zone and the retinal pigment epithelium/Bruch complex (IZ+RPE) (19.619 ± 4.626) compared to HC (17.434 ± 1.664). There were no significant differences in the thickness of the outer plexiform layer (OPL), the myoid and ellipsoid zone (MEZ), and the IZ+RPE layer between WMH and HC subjects. Similarly, there were also no obvious differences in the thickness of the OPL, HFL+ONL, MEZ and the OS layer between PD and HC subjects. Conclusion: Thickness changes in HFL+ONL, OS, and IZ+RPE layers may correlate with brain-related diseases such as WMH and PD. Further longitudinal study is needed to confirm HFL+ONL/OS/IZ+RPE layer thickness as potential biomarkers for detecting certain brain-related diseases.
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Affiliation(s)
- Yitian Zhao
- Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China.,Zhejiang International Scientific and Technological Cooperative Base of Biomedical Materials and Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China.,Zhejiang Engineering Research Center for Biomedical Materials, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
| | - Jinyu Zhao
- Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China.,School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, China
| | - Yuanyuan Gu
- Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China.,Zhejiang International Scientific and Technological Cooperative Base of Biomedical Materials and Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China.,Zhejiang Engineering Research Center for Biomedical Materials, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
| | - Bang Chen
- Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
| | - Jiaqi Guo
- Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
| | - Jianyang Xie
- Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
| | - Qifeng Yan
- Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
| | - Yuhui Ma
- Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
| | - Yufei Wu
- The Affiliated People's Hospital of Ningbo University, Ningbo, China
| | - Jiong Zhang
- Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
| | - Qinkang Lu
- The Affiliated People's Hospital of Ningbo University, Ningbo, China
| | - Jiang Liu
- Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China
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